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Zhu A, Tailor P, Verma R, Zhang I, Schott B, Ye C, Szirth B, Habiel M, Khouri AS. Implementation of deep learning artificial intelligence in vision-threatening disease screenings for an underserved community during COVID-19. J Telemed Telecare 2024; 30:1590-1597. [PMID: 36908254 PMCID: PMC10014445 DOI: 10.1177/1357633x231158832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Accepted: 02/05/2023] [Indexed: 03/14/2023]
Abstract
INTRODUCTION Age-related macular degeneration, diabetic retinopathy, and glaucoma are vision-threatening diseases that are leading causes of vision loss. Many studies have validated deep learning artificial intelligence for image-based diagnosis of vision-threatening diseases. Our study prospectively investigated deep learning artificial intelligence applications in student-run non-mydriatic screenings for an underserved, primarily Hispanic community during COVID-19. METHODS Five supervised student-run community screenings were held in West New York, New Jersey. Participants underwent non-mydriatic 45-degree retinal imaging by medical students. Images were uploaded to a cloud-based deep learning artificial intelligence for vision-threatening disease referral. An on-site tele-ophthalmology grader and remote clinical ophthalmologist graded images, with adjudication by a senior ophthalmologist to establish the gold standard diagnosis, which was used to assess the performance of deep learning artificial intelligence. RESULTS A total of 385 eyes from 195 screening participants were included (mean age 52.43 ± 14.5 years, 40.0% female). A total of 48 participants were referred for at least one vision-threatening disease. Deep learning artificial intelligence marked 150/385 (38.9%) eyes as ungradable, compared to 10/385 (2.6%) ungradable as per the human gold standard (p < 0.001). Deep learning artificial intelligence had 63.2% sensitivity, 94.5% specificity, 32.0% positive predictive value, and 98.4% negative predictive value in vision-threatening disease referrals. Deep learning artificial intelligence successfully referred all 4 eyes with multiple vision-threatening diseases. Deep learning artificial intelligence graded images (35.6 ± 13.3 s) faster than the tele-ophthalmology grader (129 ± 41.0) and clinical ophthalmologist (68 ± 21.9, p < 0.001). DISCUSSION Deep learning artificial intelligence can increase the efficiency and accessibility of vision-threatening disease screenings, particularly in underserved communities. Deep learning artificial intelligence should be adaptable to different environments. Consideration should be given to how deep learning artificial intelligence can best be utilized in a real-world application, whether in computer-aided or autonomous diagnosis.
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Affiliation(s)
- Aretha Zhu
- Institute of Ophthalmology & Visual Science, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Priya Tailor
- Institute of Ophthalmology & Visual Science, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Rashika Verma
- Institute of Ophthalmology & Visual Science, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Isis Zhang
- Institute of Ophthalmology & Visual Science, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Brian Schott
- Institute of Ophthalmology & Visual Science, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Catherine Ye
- Institute of Ophthalmology & Visual Science, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Bernard Szirth
- Institute of Ophthalmology & Visual Science, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Miriam Habiel
- Institute of Ophthalmology & Visual Science, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Albert S Khouri
- Institute of Ophthalmology & Visual Science, Rutgers New Jersey Medical School, Newark, NJ, USA
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Bush N, Khashab M, Akshintala VS. Current and Emerging Applications of Artificial Intelligence (AI) in the Management of Pancreatobiliary (PB) disorders. Curr Gastroenterol Rep 2024; 26:304-309. [PMID: 39134866 DOI: 10.1007/s11894-024-00942-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/30/2024] [Indexed: 09/11/2024]
Abstract
PURPOSE OF REVIEW: In this review, we aim to summarize the existing literature and future directions on the use of artificial intelligence (AI) for the diagnosis and treatment of PB (pancreaticobiliary) disorders. RECENT FINDINGS: AI models have been developed to aid in the diagnosis and management of PB disorders such as pancreatic adenocarcinoma (PDAC), pancreatic neuroendocrine tumors (pNETs), acute pancreatitis, chronic pancreatitis, autoimmune pancreatitis, choledocholithiasis, indeterminate biliary strictures, cholangiocarcinoma and endoscopic procedures such as ERCP, EUS, and cholangioscopy. Recent studies have integrated radiological, endoscopic and pathological data to develop models to aid in better detection and prognostication of these disorders. AI is an indispensable proponent in the future practice of medicine. It has been extensively studied and approved for use in the detection of colonic polyps. AI models based on clinical, laboratory, and radiomics have been developed to aid in the diagnosis and management of various PB disorders and its application is ever expanding. Despite promising results, these AI-based models need further external validation to be clinically applicable.
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Affiliation(s)
- Nikhil Bush
- Department of Gastroenterology and Hepatology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Mouen Khashab
- Department of Gastroenterology and Hepatology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Venkata S Akshintala
- Department of Gastroenterology and Hepatology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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3
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Yu S, Jeon BR, Liu C, Kim D, Park HI, Park HD, Shin JH, Lee JH, Choi Q, Kim S, Yun YM, Cho EJ. Laboratory Preparation for Digital Medicine in Healthcare 4.0: An Investigation Into the Awareness and Applications of Big Data and Artificial Intelligence. Ann Lab Med 2024; 44:562-571. [PMID: 38953115 PMCID: PMC11375187 DOI: 10.3343/alm.2024.0111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 04/03/2024] [Accepted: 06/21/2024] [Indexed: 07/03/2024] Open
Abstract
Background Healthcare 4.0. refers to the integration of advanced technologies, such as artificial intelligence (AI) and big data analysis, into the healthcare sector. Recognizing the impact of Healthcare 4.0 technologies in laboratory medicine (LM), we seek to assess the overall awareness and implementation of Healthcare 4.0 among members of the Korean Society for Laboratory Medicine (KSLM). Methods A web-based survey was conducted using an anonymous questionnaire. The survey comprised 36 questions covering demographic information (seven questions), big data (10 questions), and AI (19 questions). Results In total, 182 (17.9%) of 1,017 KSLM members participated in the survey. Thirty-two percent of respondents considered AI to be the most important technology in LM in the era of Healthcare 4.0, closely followed by 31% who favored big data. Approximately 80% of respondents were familiar with big data but had not conducted research using it, and 71% were willing to participate in future big data research conducted by the KSLM. Respondents viewed AI as the most valuable tool in molecular genetics within various divisions. More than half of the respondents were open to the notion of using AI as assistance rather than a complete replacement for their roles. Conclusions This survey highlighted KSLM members' awareness of the potential applications and implications of big data and AI. We emphasize the complexity of AI integration in healthcare, citing technical and ethical challenges leading to diverse opinions on its impact on employment and training. This highlights the need for a holistic approach to adopting new technologies.
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Affiliation(s)
- Shinae Yu
- Department of Laboratory Medicine, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea
| | - Byung Ryul Jeon
- Department of Laboratory Medicine & Genetics, Soonchunhyang University Bucheon Hospital, Soonchunhyang University College of Medicine, Bucheon, Korea
| | - Changseung Liu
- Departments of Laboratory Medicine, Gangneung Asan Hospital, University of Ulsan College of Medicine, Gangneung, Korea
| | - Dokyun Kim
- Department of Laboratory Medicine and Research Institute of Bacterial Resistance, Yonsei University College of Medicine, Seoul, Korea
| | - Hae-Il Park
- Department of Laboratory Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Hyung Doo Park
- Department of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Jeong Hwan Shin
- Department of Laboratory Medicine, Inje University College of Medicine, Busan, Korea
| | - Jun Hyung Lee
- Department of Laboratory Medicine, GC Labs, Yongin, Korea
| | - Qute Choi
- Department of Laboratory Medicine, Chungnam National University Sejong Hospital, Chungnam National University School of Medicine, Daejeon, Korea
| | - Sollip Kim
- Department of Laboratory Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Yeo Min Yun
- Department of Laboratory Medicine, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Korea
| | - Eun-Jung Cho
- Department of Laboratory Medicine, Hallym University Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong, Korea
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4
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Shah N, Khalid U, Kavia R, Batura D. Current advances in the use of artificial intelligence in predicting and managing urological complications. Int Urol Nephrol 2024; 56:3427-3435. [PMID: 38982018 DOI: 10.1007/s11255-024-04149-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Accepted: 07/03/2024] [Indexed: 07/11/2024]
Abstract
BACKGROUND Artificial intelligence (AI) has emerged as a promising avenue for improving patient care and surgical outcomes in urological surgery. However, the extent of AI's impact in predicting and managing complications is not fully elucidated. OBJECTIVES We review the application of AI to foresee and manage complications in urological surgery, assess its efficacy, and discuss challenges to its use. METHODS AND MATERIALS A targeted non-systematic literature search was conducted using the PubMed and Google Scholar databases to identify studies on AI in urological surgery and its complications. Evidence from the studies was synthesised. RESULTS Incorporating AI into various facets of urological surgery has shown promising advancements. From preoperative planning to intraoperative guidance, AI is revolutionising the field, demonstrating remarkable proficiency in tasks such as image analysis, decision-making support, and complication prediction. Studies show that AI programmes are highly accurate, increase surgical precision and efficiency, and reduce complications. However, implementation challenges exist in AI errors, human errors, and ethical issues. CONCLUSION AI has great potential in predicting and managing surgical complications of urological surgery. Advancements have been made, but challenges and ethical considerations must be addressed before widespread AI implementation.
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Affiliation(s)
- Nikhil Shah
- Faculty of Medicine, Medical University of Plovdiv, 4002, Plovdiv, Bulgaria
| | - Usman Khalid
- Faculty of Medicine, Medical University of Plovdiv, 4002, Plovdiv, Bulgaria
| | - Rajesh Kavia
- Department of Urology, London North West University Healthcare NHS Trust, Watford Road, Harrow, London, HA1 3UJ, UK
| | - Deepak Batura
- Department of Urology, London North West University Healthcare NHS Trust, Watford Road, Harrow, London, HA1 3UJ, UK.
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Carnino JM, Chong NYK, Bayly H, Salvati LR, Tiwana HS, Levi JR. AI-generated text in otolaryngology publications: a comparative analysis before and after the release of ChatGPT. Eur Arch Otorhinolaryngol 2024; 281:6141-6146. [PMID: 39014250 DOI: 10.1007/s00405-024-08834-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 07/08/2024] [Indexed: 07/18/2024]
Abstract
PURPOSE This study delves into the broader implications of artificial intelligence (AI) text generation technologies, including large language models (LLMs) and chatbots, on the scientific literature of otolaryngology. By observing trends in AI-generated text within published otolaryngology studies, this investigation aims to contextualize the impact of AI-driven tools that are reshaping scientific writing and communication. METHODS Text from 143 original articles published in JAMA Otolaryngology - Head and Neck Surgery was collected, representing periods before and after ChatGPT's release in November 2022. The text from each article's abstract, introduction, methods, results, and discussion were entered into ZeroGPT.com to estimate the percentage of AI-generated content. Statistical analyses, including T-Tests and Fligner-Killeen's tests, were conducted using R. RESULTS A significant increase was observed in the mean percentage of AI-generated text post-ChatGPT release, especially in the abstract (from 34.36 to 46.53%, p = 0.004), introduction (from 32.43 to 45.08%, p = 0.010), and discussion sections (from 15.73 to 25.03%, p = 0.015). Publications of authors from non-English speaking countries demonstrated a higher percentage of AI-generated text. CONCLUSION This study found that the advent of ChatGPT has significantly impacted writing practices among researchers publishing in JAMA Otolaryngology - Head and Neck Surgery, raising concerns over the accuracy of AI-created content and potential misinformation risks. This manuscript highlights the evolving dynamics between AI technologies, scientific communication, and publication integrity, emphasizing the urgent need for continued research in this dynamic field. The findings also suggest an increasing reliance on AI tools like ChatGPT, raising questions about their broader implications for scientific publishing.
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Affiliation(s)
- Jonathan M Carnino
- Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
| | - Nicholas Y K Chong
- Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Henry Bayly
- Boston University School of Public Health, Boston, MA, USA
| | | | - Hardeep S Tiwana
- Washington State University Elson S. Floyd College of Medicine, Spokane, WA, USA
| | - Jessica R Levi
- Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Otolaryngology - Head and Neck Surgery, Boston Medical Center, Boston, MA, USA
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Botha NN, Segbedzi CE, Dumahasi VK, Maneen S, Kodom RV, Tsedze IS, Akoto LA, Atsu FS, Lasim OU, Ansah EW. Artificial intelligence in healthcare: a scoping review of perceived threats to patient rights and safety. Arch Public Health 2024; 82:188. [PMID: 39444019 DOI: 10.1186/s13690-024-01414-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Accepted: 10/01/2024] [Indexed: 10/25/2024] Open
Abstract
BACKGROUND The global health system remains determined to leverage on every workable opportunity, including artificial intelligence (AI) to provide care that is consistent with patients' needs. Unfortunately, while AI models generally return high accuracy within the trials in which they are trained, their ability to predict and recommend the best course of care for prospective patients is left to chance. PURPOSE This review maps evidence between January 1, 2010 to December 31, 2023, on the perceived threats posed by the usage of AI tools in healthcare on patients' rights and safety. METHODS We deployed the guidelines of Tricco et al. to conduct a comprehensive search of current literature from Nature, PubMed, Scopus, ScienceDirect, Dimensions AI, Web of Science, Ebsco Host, ProQuest, JStore, Semantic Scholar, Taylor & Francis, Emeralds, World Health Organisation, and Google Scholar. In all, 80 peer reviewed articles qualified and were included in this study. RESULTS We report that there is a real chance of unpredictable errors, inadequate policy and regulatory regime in the use of AI technologies in healthcare. Moreover, medical paternalism, increased healthcare cost and disparities in insurance coverage, data security and privacy concerns, and bias and discriminatory services are imminent in the use of AI tools in healthcare. CONCLUSIONS Our findings have some critical implications for achieving the Sustainable Development Goals (SDGs) 3.8, 11.7, and 16. We recommend that national governments should lead in the roll-out of AI tools in their healthcare systems. Also, other key actors in the healthcare industry should contribute to developing policies on the use of AI in healthcare systems.
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Affiliation(s)
- Nkosi Nkosi Botha
- Department of Health, Physical Education and Recreation, University of Cape Coast, Cape Coast, Ghana.
- Air Force Medical Centre, Armed Forces Medical Services, Air Force Base, Takoradi, Ghana.
| | - Cynthia E Segbedzi
- Department of Health, Physical Education and Recreation, University of Cape Coast, Cape Coast, Ghana
| | - Victor K Dumahasi
- Institute of Environmental and Sanitation Studies, Environmental Science, College of Basic and Applied Sciences, University of Ghana, Legon, Ghana
| | - Samuel Maneen
- Department of Health, Physical Education and Recreation, University of Cape Coast, Cape Coast, Ghana
| | - Ruby V Kodom
- Department of Health Services Management/Distance Education, University of Ghana, Legon, Ghana
| | - Ivy S Tsedze
- Department of Adult Health, School of Nursing and Midwifery, University of Cape Coast, Cape Coast, Ghana
| | - Lucy A Akoto
- Air Force Medical Centre, Armed Forces Medical Services, Air Force Base, Takoradi, Ghana
| | | | - Obed U Lasim
- Department of Health Information Management, School of Allied Health Sciences, University of Cape Coast, Cape Coast, Ghana
| | - Edward W Ansah
- Department of Health, Physical Education and Recreation, University of Cape Coast, Cape Coast, Ghana
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7
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Duo Y, Han L, Yang Y, Wang Z, Wang L, Chen J, Xiang Z, Yoon J, Luo G, Tang BZ. Aggregation-Induced Emission Luminogen: Role in Biopsy for Precision Medicine. Chem Rev 2024; 124:11242-11347. [PMID: 39380213 PMCID: PMC11503637 DOI: 10.1021/acs.chemrev.4c00244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Revised: 09/11/2024] [Accepted: 09/17/2024] [Indexed: 10/10/2024]
Abstract
Biopsy, including tissue and liquid biopsy, offers comprehensive and real-time physiological and pathological information for disease detection, diagnosis, and monitoring. Fluorescent probes are frequently selected to obtain adequate information on pathological processes in a rapid and minimally invasive manner based on their advantages for biopsy. However, conventional fluorescent probes have been found to show aggregation-caused quenching (ACQ) properties, impeding greater progresses in this area. Since the discovery of aggregation-induced emission luminogen (AIEgen) have promoted rapid advancements in molecular bionanomaterials owing to their unique properties, including high quantum yield (QY) and signal-to-noise ratio (SNR), etc. This review seeks to present the latest advances in AIEgen-based biofluorescent probes for biopsy in real or artificial samples, and also the key properties of these AIE probes. This review is divided into: (i) tissue biopsy based on smart AIEgens, (ii) blood sample biopsy based on smart AIEgens, (iii) urine sample biopsy based on smart AIEgens, (iv) saliva sample biopsy based on smart AIEgens, (v) biopsy of other liquid samples based on smart AIEgens, and (vi) perspectives and conclusion. This review could provide additional guidance to motivate interest and bolster more innovative ideas for further exploring the applications of various smart AIEgens in precision medicine.
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Affiliation(s)
- Yanhong Duo
- Department
of Radiation Oncology, Shenzhen People’s Hospital, The Second
Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen 518020, Guangdong China
- Wyss
Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts 02138, United States
| | - Lei Han
- College of
Chemistry and Pharmaceutical Sciences, Qingdao
Agricultural University, 700 Changcheng Road, Qingdao 266109, Shandong China
| | - Yaoqiang Yang
- Department
of Radiation Oncology, Shenzhen People’s Hospital, The Second
Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen 518020, Guangdong China
| | - Zhifeng Wang
- Department
of Urology, Henan Provincial People’s Hospital, Zhengzhou University
People’s Hospital, Henan University
People’s Hospital, Zhengzhou, 450003, China
| | - Lirong Wang
- State
Key Laboratory of Luminescent Materials and Devices, South China University of Technology, Guangzhou 510640, China
| | - Jingyi Chen
- Wyss
Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts 02138, United States
| | - Zhongyuan Xiang
- Department
of Laboratory Medicine, The Second Xiangya Hospital, Central South University, Changsha 410000, Hunan, China
| | - Juyoung Yoon
- Department
of Chemistry and Nanoscience, Ewha Womans
University, 52 Ewhayeodae-gil, Seodaemun-gu, Seoul 03760, Korea
| | - Guanghong Luo
- Department
of Radiation Oncology, Shenzhen People’s Hospital, The Second
Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen 518020, Guangdong China
| | - Ben Zhong Tang
- School
of Science and Engineering, Shenzhen Institute of Aggregate Science
and Technology, The Chinese University of
Hong Kong, Shenzhen 518172, Guangdong China
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Wang K, Zou W, Lai Y, Hao C, Liu N, Ling X, Liu X, Liu T, Yang X, Zu C, Wu S. Accessibility, Cost, and Quality of an Online Regular Follow-Up Visit Service at an Internet Hospital in China: Mixed Methods Study. J Med Internet Res 2024; 26:e54902. [PMID: 39432365 DOI: 10.2196/54902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 07/10/2024] [Accepted: 09/05/2024] [Indexed: 10/22/2024] Open
Abstract
BACKGROUND Telemedicine provides remote health care services to overcome constraints of time and space in accessing medical care. Similarly, internet hospitals in China support and provide remote health care services. Due to the COVID-19 pandemic, there has been a proliferation of internet hospitals. Many new services, including online consultations and regular online follow-up visit services, can now be accessed via internet hospitals in China. However, the accessibility, cost, and quality advantages of regular online follow-up visit services remain unclear. OBJECTIVE This study aimed to evaluate the accessibility, costs, and quality of an online regular follow-up visit service provided by an internet hospital in China. By analyzing the accessibility, costs, and quality of this service from the supply and demand sides, we can summarize the practical and theoretical experiences. METHODS A mixed methods study was conducted using clinical records from 18,473 patients receiving 39,239 online regular follow-up visit services at an internet hospital in 2021, as well as interviews with 7 physicians, 2 head nurses, and 3 administrative staff members. The quantitative analysis examined patient demographics, diagnoses, prescriptions, geographic distribution, physician characteristics, accessibility (travel time and costs), and service hours. The qualitative analysis elucidated physician perspectives on ensuring the quality of online health care. RESULTS Patients were predominantly middle-aged men with chronic diseases like viral hepatitis who were located near the hospital. The vast majority were from Guangdong province where the hospital is based, especially concentrated in Guangzhou city. The online regular follow-up visit service reduced travel time by 1 hour to 9 hours and costs by ¥6 to ¥991 (US $0.86-$141.32) depending on proximity, with greater savings for patients farther from the hospital. Consultation times were roughly equivalent between online and in-person visits. Physicians provided most online services during lunch breaks (12 PM to 2 PM) or after work hours (7 PM to 11 PM), indicating increased workload. The top departments providing online regular follow-up visit services were Infectious Diseases, Rheumatology, and Dermatology. The most commonly prescribed medications aligned with the prevalent chronic diagnoses. To ensure quality, physicians conducted initial in-person consultations to fully evaluate patients before allowing online regular follow-up visits, during which they communicated with patients to assess conditions and determine if in-person care was warranted. They also periodically reminded patients to come in person for more comprehensive evaluations. However, they acknowledged online visits cannot fully replace face-to-face care. CONCLUSIONS Telemedicine services such as online regular follow-up visit services provided by internet hospitals must strictly adhere to fundamental medical principles of diagnosis, prescription, and treatment. For patients with chronic diseases, online regular follow-up visit services improve accessibility and reduce cost but cannot fully replace in-person evaluations. Physicians leverage various strategies to ensure the quality of online care.
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Affiliation(s)
- Kun Wang
- Department of Medical Statistics, School of Public Health, Sun Yat-Sen University, Guangzhou, China
| | - Wenxin Zou
- School of Government, Sun Yat-sen University, Guangzhou, China
| | - Yingsi Lai
- Department of Medical Statistics, School of Public Health, Sun Yat-Sen University, Guangzhou, China
- Sun Yat-sen Global Health Institute, Institute of State Governance, Sun Yat-sen University, Guangzhou, China
| | - Chun Hao
- Department of Medical Statistics, School of Public Health, Sun Yat-Sen University, Guangzhou, China
- Sun Yat-sen Global Health Institute, Institute of State Governance, Sun Yat-sen University, Guangzhou, China
| | - Ning Liu
- School of Management, Lanzhou University, Lanzhou, China
- China Research Center for Government Performance-Management, Lanzhou University, Lanzhou, China
| | - Xiang Ling
- Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xiaohan Liu
- Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Ting Liu
- Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xin Yang
- Department of Health Policy and Management, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Chenxi Zu
- Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Shaolong Wu
- School of Government, Sun Yat-sen University, Guangzhou, China
- Sun Yat-sen Global Health Institute, Institute of State Governance, Sun Yat-sen University, Guangzhou, China
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9
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Leinonen T, Wong D, Vasankari A, Wahab A, Nadarajah R, Kaisti M, Airola A. Empirical investigation of multi-source cross-validation in clinical ECG classification. Comput Biol Med 2024; 183:109271. [PMID: 39427424 DOI: 10.1016/j.compbiomed.2024.109271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Revised: 10/08/2024] [Accepted: 10/09/2024] [Indexed: 10/22/2024]
Abstract
Traditionally, machine learning-based clinical prediction models have been trained and evaluated on patient data from a single source, such as a hospital. Cross-validation methods can be used to estimate the accuracy of such models on new patients originating from the same source, by repeated random splitting of the data. However, such estimates tend to be highly overoptimistic when compared to accuracy obtained from deploying models to sources not represented in the dataset, such as a new hospital. The increasing availability of multi-source medical datasets provides new opportunities for obtaining more comprehensive and realistic evaluations of expected accuracy through source-level cross-validation designs. In this study, we present a systematic empirical evaluation of standard K-fold cross-validation and leave-source-out cross-validation methods in a multi-source setting. We consider the task of electrocardiogram based cardiovascular disease classification, combining and harmonizing the openly available PhysioNet/CinC Challenge 2021 and the Shandong Provincial Hospital datasets for our study. Our results show that K-fold cross-validation, both on single-source and multi-source data, systemically overestimates prediction performance when the end goal is to generalize to new sources. Leave-source-out cross-validation provides more reliable performance estimates, having close to zero bias though larger variability. The evaluation highlights the dangers of obtaining misleading cross-validation results on medical data and demonstrates how these issues can be mitigated when having access to multi-source data.
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Affiliation(s)
| | - David Wong
- Leeds Institute of Health Sciences, University of Leeds, UK
| | | | - Ali Wahab
- Institute of Cardiovascular and Metabolic Medicine, University of Leeds, UK
| | - Ramesh Nadarajah
- Institute of Cardiovascular and Metabolic Medicine, University of Leeds, UK
| | - Matti Kaisti
- Department of Computing, University of Turku, Finland
| | - Antti Airola
- Department of Computing, University of Turku, Finland.
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10
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Pupic N, Gabison S, Evans G, Fernie G, Dolatabadi E, Dutta T. Detecting Patient Position Using Bed-Reaction Forces for Pressure Injury Prevention and Management. SENSORS (BASEL, SWITZERLAND) 2024; 24:6483. [PMID: 39409523 PMCID: PMC11479332 DOI: 10.3390/s24196483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2024] [Revised: 09/21/2024] [Accepted: 10/01/2024] [Indexed: 10/20/2024]
Abstract
A key best practice to prevent and treat pressure injuries (PIs) is to ensure at-risk individuals are repositioned regularly. Our team designed a non-contact position detection system that predicts an individual's position in bed using data from load cells under the bed legs. The system was originally designed to predict the individual's position as left-side lying, right-side lying, or supine. Our previous work suggested that a higher precision for detecting position (classifying more than three positions) may be needed to determine whether key bony prominences on the pelvis at high risk of PIs have been off-loaded. The objective of this study was to determine the impact of categorizing participant position with higher precision using the system prediction F1 score. Data from 18 participants was collected from four load cells placed under the bed legs and a pelvis-mounted inertial measurement unit while the participants assumed 21 positions. The data was used to train classifiers to predict the participants' transverse pelvic angle using three different position bin sizes (45°, ~30°, and 15°). A leave-one-participant-out cross validation approach was used to evaluate classifier performance for each bin size. Results indicated that our prediction F1 score dropped as the position category precision was increased.
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Affiliation(s)
- Nikola Pupic
- KITE Research Institute, Toronto Rehabilitation Institute—University Health Network, Toronto, ON M5G 2A2, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada
| | - Sharon Gabison
- KITE Research Institute, Toronto Rehabilitation Institute—University Health Network, Toronto, ON M5G 2A2, Canada
- Department of Physical Therapy, University of Toronto, Toronto, ON M5G 1V7, Canada
- Rehabilitation Sciences Institute, University of Toronto, Toronto, ON M5G 1V7, Canada
| | - Gary Evans
- KITE Research Institute, Toronto Rehabilitation Institute—University Health Network, Toronto, ON M5G 2A2, Canada
| | - Geoff Fernie
- KITE Research Institute, Toronto Rehabilitation Institute—University Health Network, Toronto, ON M5G 2A2, Canada
| | | | - Tilak Dutta
- KITE Research Institute, Toronto Rehabilitation Institute—University Health Network, Toronto, ON M5G 2A2, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada
- Rehabilitation Sciences Institute, University of Toronto, Toronto, ON M5G 1V7, Canada
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11
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Niu Y, Li J, Xu X, Luo P, Liu P, Wang J, Mu J. Deep learning-driven ultrasound-assisted diagnosis: optimizing GallScopeNet for precise identification of biliary atresia. Front Med (Lausanne) 2024; 11:1445069. [PMID: 39440041 PMCID: PMC11493747 DOI: 10.3389/fmed.2024.1445069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Accepted: 09/12/2024] [Indexed: 10/25/2024] Open
Abstract
Background Biliary atresia (BA) is a severe congenital biliary developmental abnormality threatening neonatal health. Traditional diagnostic methods rely heavily on experienced radiologists, making the process time-consuming and prone to variability. The application of deep learning for the automated diagnosis of BA remains underexplored. Methods This study introduces GallScopeNet, a deep learning model designed to improve diagnostic efficiency and accuracy through innovative architecture and advanced feature extraction techniques. The model utilizes data from a carefully constructed dataset of gallbladder ultrasound images. A dataset comprising thousands of ultrasound images was employed, with the majority used for training and validation and a subset reserved for external testing. The model's performance was evaluated using five-fold cross-validation and external assessment, employing metrics such as accuracy and the area under the receiver operating characteristic curve (AUC), compared against clinical diagnostic standards. Results GallScopeNet demonstrated exceptional performance in distinguishing BA from non-BA cases. In the external test dataset, GallScopeNet achieved an accuracy of 81.21% and an AUC of 0.85, indicating strong diagnostic capabilities. The results highlighted the model's ability to maintain high classification performance, reducing misdiagnosis and missed diagnosis. Conclusion GallScopeNet effectively differentiates between BA and non-BA images, demonstrating significant potential and reliability for early diagnosis. The system's high efficiency and accuracy suggest it could serve as a valuable diagnostic tool in clinical settings, providing substantial technical support for improving diagnostic workflows.
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Affiliation(s)
- Yupeng Niu
- College of Information Engineering, Sichuan Agricultural University, Ya’an, China
- Artificial Intelligence Laboratory, Sichuan Agricultural University, Ya’an, China
| | - Jingze Li
- College of Information Engineering, Sichuan Agricultural University, Ya’an, China
| | - Xiyuan Xu
- College of Veterinary Medicine, Sichuan Agricultural University, Chengdu, China
| | - Pu Luo
- College of Information Engineering, Sichuan Agricultural University, Ya’an, China
- Artificial Intelligence Laboratory, Sichuan Agricultural University, Ya’an, China
| | - Pingchuan Liu
- People’s Hospital of Ya’an City, Sichuan Province, Ya’an, China
| | - Jian Wang
- People’s Hospital of Ya’an City, Sichuan Province, Ya’an, China
| | - Jiong Mu
- College of Information Engineering, Sichuan Agricultural University, Ya’an, China
- Artificial Intelligence Laboratory, Sichuan Agricultural University, Ya’an, China
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12
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Radaelli D, Di Maria S, Jakovski Z, Alempijevic D, Al-Habash I, Concato M, Bolcato M, D’Errico S. Advancing Patient Safety: The Future of Artificial Intelligence in Mitigating Healthcare-Associated Infections: A Systematic Review. Healthcare (Basel) 2024; 12:1996. [PMID: 39408177 PMCID: PMC11477207 DOI: 10.3390/healthcare12191996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2024] [Revised: 10/03/2024] [Accepted: 10/03/2024] [Indexed: 10/20/2024] Open
Abstract
BACKGROUND Healthcare-associated infections are infections that patients acquire during hospitalization or while receiving healthcare in other facilities. They represent the most frequent negative outcome in healthcare, can be entirely prevented, and pose a burden in terms of financial and human costs. With the development of new AI and ML algorithms, hospitals could develop new and automated surveillance and prevention models for HAIs, leading to improved patient safety. The aim of this review is to systematically retrieve, collect, and summarize all available information on the application and impact of AI in HAI surveillance and/or prevention. METHODS We conducted a systematic review of the literature using PubMed and Scopus to find articles related to the implementation of artificial intelligence in the surveillance and/or prevention of HAIs. RESULTS We identified a total of 218 articles, of which only 35 were included in the review. Most studies were conducted in the US (n = 10, 28.6%) and China (n = 5; 14.3%) and were published between 2021 and 2023 (26 articles, 74.3%) with an increasing trend over time. Most focused on the development of ML algorithms for the identification/prevention of surgical site infections (n = 18; 51%), followed by HAIs in general (n = 9; 26%), hospital-acquired urinary tract infections (n = 5; 9%), and healthcare-associated pneumonia (n = 3; 9%). Only one study focused on the proper use of personal protective equipment (PPE) and included healthcare workers as the study population. Overall, the trend indicates that several AI/ML models can effectively assist clinicians in everyday decisions, by identifying HAIs early or preventing them through personalized risk factors with good performance. However, only a few studies have reported an actual implementation of these models, which proved highly successful. In one case, manual workload was reduced by nearly 85%, while another study observed a decrease in the local hospital's HAI incidence from 1.31% to 0.58%. CONCLUSIONS AI has significant potential to improve the prevention, diagnosis, and management of healthcare-associated infections, offering benefits such as increased accuracy, reduced workloads, and cost savings. Although some AI applications have already been tested and validated, adoption in healthcare is hindered by barriers such as high implementation costs, technological limitations, and resistance from healthcare workers. Overcoming these challenges could allow AI to be more widely and cost-effectively integrated, ultimately improving patient care and infection management.
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Affiliation(s)
- Davide Radaelli
- Department of Medical Surgical and Health Sciences, University of Trieste, 34127 Trieste, Italy; (D.R.); (S.D.M.); (M.C.)
| | - Stefano Di Maria
- Department of Medical Surgical and Health Sciences, University of Trieste, 34127 Trieste, Italy; (D.R.); (S.D.M.); (M.C.)
| | - Zlatko Jakovski
- Institute of Forensic Medicine, Criminalistic and Medical Deontology, University Ss. Cyril and Methodius, 1000 Skopje, North Macedonia;
| | - Djordje Alempijevic
- Institute of Forensic Medicine ‘Milovan Milovanovic’, School of Medicine, University of Belgrade, 11000 Belgrade, Serbia;
| | - Ibrahim Al-Habash
- Forensic Medicine Department, Mutah University, Karak 61710, Jordan;
| | - Monica Concato
- Department of Medical Surgical and Health Sciences, University of Trieste, 34127 Trieste, Italy; (D.R.); (S.D.M.); (M.C.)
| | - Matteo Bolcato
- Department of Medicine, Saint Camillus International University of Health and Medical Sciences, 00131 Rome, Italy
| | - Stefano D’Errico
- Department of Medical Surgical and Health Sciences, University of Trieste, 34127 Trieste, Italy; (D.R.); (S.D.M.); (M.C.)
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13
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Lal R, Singh A, Watts S, Chopra K. Experimental models of Parkinson's disease: Challenges and Opportunities. Eur J Pharmacol 2024; 980:176819. [PMID: 39029778 DOI: 10.1016/j.ejphar.2024.176819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 05/29/2024] [Accepted: 07/17/2024] [Indexed: 07/21/2024]
Abstract
Parkinson's disease (PD) is a widespread neurodegenerative disorder occurs due to the degradation of dopaminergic neurons present in the substantia nigra pars compacta (SNpc). Millions of people are affected by this devastating disorder globally, and the frequency of the condition increases with the increase in the elderly population. A significant amount of progress has been made in acquiring more knowledge about the etiology and the pathogenesis of PD over the past decades. Animal models have been regarded to be a vital tool for the exploration of complex molecular mechanisms involved in PD. Various animals used as models for disease monitoring include vertebrates (zebrafish, rats, mice, guinea pigs, rabbits and monkeys) and invertebrate models (Drosophila, Caenorhabditis elegans). The animal models most relevant for study of PD are neurotoxin induction-based models (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP), 6-Hydroxydopamine (6-OHDA) and agricultural pesticides (rotenone, paraquat), pharmacological models (reserpine or haloperidol treated rats), genetic models (α-synuclein, Leucine-rich repeat kinase 2 (LRRK2), DJ-1, PINK-1 and Parkin). Several non-mammalian genetic models such as zebrafish, Drosophila and Caenorhabditis elegance have also gained popularity in recent years due to easy genetic manipulation, presence of genes homologous to human PD, and rapid screening of novel therapeutic molecules. In addition, in vitro models (SH-SY5Y, PC12, Lund human mesencephalic (LUHMES) cells, Human induced pluripotent stem cell (iPSC), Neural organoids, organ-on-chip) are also currently in trend providing edge in investigating molecular mechanisms involved in PD as they are derived from PD patients. In this review, we explain the current situation and merits and demerits of the various animal models.
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Affiliation(s)
- Roshan Lal
- Pharmacology Division, University Institute of Pharmaceutical Sciences (UIPS), Panjab University, Chandigarh, 160014, India.
| | - Aditi Singh
- TR(i)P for Health Laboratory, Centre for Excellence in Functional Foods, Department of Food and Nutritional Biotechnology, National Agri-Food Biotechnology Institute (NABI), Knowledge City, Sector 81, SAS Nagar, Punjab, 140306, India.
| | - Shivam Watts
- Pharmacology Division, University Institute of Pharmaceutical Sciences (UIPS), Panjab University, Chandigarh, 160014, India.
| | - Kanwaljit Chopra
- Pharmacology Division, University Institute of Pharmaceutical Sciences (UIPS), Panjab University, Chandigarh, 160014, India.
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Chinni BK, Manlhiot C. Emerging Analytical Approaches for Personalized Medicine Using Machine Learning In Pediatric and Congenital Heart Disease. Can J Cardiol 2024; 40:1880-1896. [PMID: 39097187 DOI: 10.1016/j.cjca.2024.07.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Revised: 07/25/2024] [Accepted: 07/26/2024] [Indexed: 08/05/2024] Open
Abstract
Precision and personalized medicine, the process by which patient management is tailored to individual circumstances, are now terms that are familiar to cardiologists, despite it still being an emerging field. Although precision medicine relies most often on the underlying biology and pathophysiology of a patient's condition, personalized medicine relies on digital biomarkers generated through algorithms. Given the complexity of the underlying data, these digital biomarkers are most often generated through machine-learning algorithms. There are a number of analytic considerations regarding the creation of digital biomarkers that are discussed in this review, including data preprocessing, time dependency and gating, dimensionality reduction, and novel methods, both in the realm of supervised and unsupervised machine learning. Some of these considerations, such as sample size requirements and measurements of model performance, are particularly challenging in small and heterogeneous populations with rare outcomes such as children with congenital heart disease. Finally, we review analytic considerations for the deployment of digital biomarkers in clinical settings, including the emerging field of clinical artificial intelligence (AI) operations, computational needs for deployment, efforts to increase the explainability of AI, algorithmic drift, and the needs for distributed surveillance and federated learning. We conclude this review by discussing a recent simulation study that shows that, despite these analytic challenges and complications, the use of digital biomarkers in managing clinical care might have substantial benefits regarding individual patient outcomes.
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Affiliation(s)
- Bhargava K Chinni
- The Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Cedric Manlhiot
- The Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA; Research Institute, SickKids Hospital, Department of Pediatrics, University of Toronto, Toronto, Ontario, Canada.
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15
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Long C, Subburam D, Lowe K, Dos Santos A, Zhang J, Hwang S, Saduka N, Horev Y, Su T, Côté DWJ, Wright ED. ChatENT: Augmented Large Language Model for Expert Knowledge Retrieval in Otolaryngology-Head and Neck Surgery. Otolaryngol Head Neck Surg 2024; 171:1042-1051. [PMID: 38895862 DOI: 10.1002/ohn.864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 05/05/2024] [Accepted: 05/09/2024] [Indexed: 06/21/2024]
Abstract
OBJECTIVE The recent surge in popularity of large language models (LLMs), such as ChatGPT, has showcased their proficiency in medical examinations and potential applications in health care. However, LLMs possess inherent limitations, including inconsistent accuracy, specific prompting requirements, and the risk of generating harmful hallucinations. A domain-specific model might address these limitations effectively. STUDY DESIGN Developmental design. SETTING Virtual. METHODS Otolaryngology-head and neck surgery (OHNS) relevant data were systematically gathered from open-access Internet sources and indexed into a knowledge database. We leveraged Retrieval-Augmented Language Modeling to recall this information and utilized it for pretraining, which was then integrated into ChatGPT4.0, creating an OHNS-specific knowledge question & answer platform known as ChatENT. The model is further tested on different types of questions. RESULTS ChatENT showed enhanced performance in the analysis and interpretation of OHNS information, outperforming ChatGPT4.0 in both the Canadian Royal College OHNS sample examination questions challenge and the US board practice questions challenge, with a 58.4% and 26.0% error reduction, respectively. ChatENT generated fewer hallucinations and demonstrated greater consistency. CONCLUSION To the best of our knowledge, ChatENT is the first specialty-specific knowledge retrieval artificial intelligence in the medical field that utilizes the latest LLM. It appears to have considerable promise in areas such as medical education, patient education, and clinical decision support. The model has demonstrated the capacity to overcome the limitations of existing LLMs, thereby signaling a future of more precise, safe, and user-friendly applications in the realm of OHNS and other medical fields.
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Affiliation(s)
- Cai Long
- Division of Otolaryngology-Head and Neck Surgery, University of Alberta, Edmonton, Alberta, Canada
| | | | - Kayle Lowe
- Faculty of Medicine, University of Alberta, Edmonton, Alberta, Canada
| | - André Dos Santos
- Alberta Machine Intelligence Institute, Edmonton, Alberta, Canada
| | - Jessica Zhang
- Faculty of Medicine, University of Alberta, Edmonton, Alberta, Canada
| | - Sang Hwang
- Division of Otolaryngology-Head and Neck Surgery, University of Alberta, Edmonton, Alberta, Canada
| | | | - Yoav Horev
- Division of Otolaryngology-Head and Neck Surgery, University of Alberta, Edmonton, Alberta, Canada
| | - Tao Su
- Copula AI, New York, New York, USA
| | - David W J Côté
- Division of Otolaryngology-Head and Neck Surgery, University of Alberta, Edmonton, Alberta, Canada
| | - Erin D Wright
- Division of Otolaryngology-Head and Neck Surgery, University of Alberta, Edmonton, Alberta, Canada
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Bagaria V, Chhabra HS. The balancing act: Adopting AI and robotics in medicine with cautious optimism. J Clin Orthop Trauma 2024; 57:102550. [PMID: 39398288 PMCID: PMC11466566 DOI: 10.1016/j.jcot.2024.102550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/15/2024] Open
Affiliation(s)
- Vaibhav Bagaria
- Corresponding author. Dept Of Orthopedics Sir HN Reliance Foundation Hospital, Mumbai, India.
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17
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Bopche R, Gustad LT, Afset JE, Ehrnström B, Damås JK, Nytrø Ø. In-hospital mortality, readmission, and prolonged length of stay risk prediction leveraging historical electronic patient records. JAMIA Open 2024; 7:ooae074. [PMID: 39282081 PMCID: PMC11401612 DOI: 10.1093/jamiaopen/ooae074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Revised: 07/16/2024] [Accepted: 07/26/2024] [Indexed: 09/18/2024] Open
Abstract
Objective This study aimed to investigate the predictive capabilities of historical patient records to predict patient adverse outcomes such as mortality, readmission, and prolonged length of stay (PLOS). Methods Leveraging a de-identified dataset from a tertiary care university hospital, we developed an eXplainable Artificial Intelligence (XAI) framework combining tree-based and traditional machine learning (ML) models with interpretations and statistical analysis of predictors of mortality, readmission, and PLOS. Results Our framework demonstrated exceptional predictive performance with a notable area under the receiver operating characteristic (AUROC) of 0.9625 and an area under the precision-recall curve (AUPRC) of 0.8575 for 30-day mortality at discharge and an AUROC of 0.9545 and AUPRC of 0.8419 at admission. For the readmission and PLOS risk, the highest AUROC achieved were 0.8198 and 0.9797, respectively. The tree-based models consistently outperformed the traditional ML models in all 4 prediction tasks. The key predictors were age, derived temporal features, routine laboratory tests, and diagnostic and procedural codes. Conclusion The study underscores the potential of leveraging medical history for enhanced hospital predictive analytics. We present an accurate and intuitive framework for early warning models that can be easily implemented in the current and developing digital health platforms to predict adverse outcomes accurately.
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Affiliation(s)
- Rajeev Bopche
- Department of Computer Science, Norwegian University of Science and Technology, Trondheim, 7491, Norway
| | - Lise Tuset Gustad
- Faculty of Nursing and Health Sciences, Nord University, Levanger, 7600, Norway
- Department of Medicine and Rehabilitation, Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, 7601, Norway
| | - Jan Egil Afset
- Department of Medical Microbiology, St Olavs Hospital, Trondheim University Hospital, Trondheim, 7030, Norway
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, 7491, Norway
| | - Birgitta Ehrnström
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, 7491, Norway
- Department of Infectious Diseases, Clinic of Medicine, St Olavs Hospital, Trondheim, 7006, Norway
- Clinic of Anaesthesia and Intensive Care, St Olavs Hospital, Trondheim University Hospital, Trondheim, 7006, Norway
| | - Jan Kristian Damås
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, 7491, Norway
- Department of Infectious Diseases, Clinic of Medicine, St Olavs Hospital, Trondheim, 7006, Norway
| | - Øystein Nytrø
- Department of Computer Science, Norwegian University of Science and Technology, Trondheim, 7491, Norway
- Department of Computer Science, The Arctic University of Norway, Tromsø, 9037, Norway
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18
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Kumari K, Pahuja SK, Kumar S. A Comprehensive Examination of ChatGPT's Contribution to the Healthcare Sector and Hepatology. Dig Dis Sci 2024:10.1007/s10620-024-08659-4. [PMID: 39354272 DOI: 10.1007/s10620-024-08659-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Accepted: 09/20/2024] [Indexed: 10/03/2024]
Abstract
Artificial Intelligence and Natural Language Processing technology have demonstrated significant promise across several domains within the medical and healthcare sectors. This technique has numerous uses in the field of healthcare. One of the primary challenges in implementing ChatGPT in healthcare is the requirement for precise and up-to-date data. In the case of the involvement of sensitive medical information, it is imperative to carefully address concerns regarding privacy and security when using GPT in the healthcare sector. This paper outlines ChatGPT and its relevance in the healthcare industry. It discusses the important aspects of ChatGPT's workflow and highlights the usual features of ChatGPT specifically designed for the healthcare domain. The present review uses the ChatGPT model within the research domain to investigate disorders associated with the hepatic system. This review demonstrates the possible use of ChatGPT in supporting researchers and clinicians in analyzing and interpreting liver-related data, thereby improving disease diagnosis, prognosis, and patient care.
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Affiliation(s)
- Kabita Kumari
- Department of Instrumentation and Control Engineering, Dr B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, 144011, India.
| | - Sharvan Kumar Pahuja
- Department of Instrumentation and Control Engineering, Dr B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, 144011, India
| | - Sanjeev Kumar
- Biomedical Instrumentation Unit, CSIR-Central Scientific Instruments Organisation (CSIR-CSIO), Chandigarh, India
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Sadeghi P, Karimi H, Lavafian A, Rashedi R, Samieefar N, Shafiekhani S, Rezaei N. Machine learning and artificial intelligence within pediatric autoimmune diseases: applications, challenges, future perspective. Expert Rev Clin Immunol 2024; 20:1219-1236. [PMID: 38771915 DOI: 10.1080/1744666x.2024.2359019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Accepted: 05/20/2024] [Indexed: 05/23/2024]
Abstract
INTRODUCTION Autoimmune disorders affect 4.5% to 9.4% of children, significantly reducing their quality of life. The diagnosis and prognosis of autoimmune diseases are uncertain because of the variety of onset and development. Machine learning can identify clinically relevant patterns from vast amounts of data. Hence, its introduction has been beneficial in the diagnosis and management of patients. AREAS COVERED This narrative review was conducted through searching various electronic databases, including PubMed, Scopus, and Web of Science. This study thoroughly explores the current knowledge and identifies the remaining gaps in the applications of machine learning specifically in the context of pediatric autoimmune and related diseases. EXPERT OPINION Machine learning algorithms have the potential to completely change how pediatric autoimmune disorders are identified, treated, and managed. Machine learning can assist physicians in making more precise and fast judgments, identifying new biomarkers and therapeutic targets, and personalizing treatment strategies for each patient by utilizing massive datasets and powerful analytics.
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Affiliation(s)
- Parniyan Sadeghi
- Network of Interdisciplinarity in Neonates and Infants (NINI), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- Student Research Committee, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hanie Karimi
- Network of Interdisciplinarity in Neonates and Infants (NINI), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Atiye Lavafian
- Network of Interdisciplinarity in Neonates and Infants (NINI), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- School of Medicine, Semnan University of Medical Science, Semnan, Iran
| | - Ronak Rashedi
- Network of Interdisciplinarity in Neonates and Infants (NINI), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- USERN Office, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Noosha Samieefar
- Network of Interdisciplinarity in Neonates and Infants (NINI), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- USERN Office, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Sajad Shafiekhani
- Department of Biomedical Engineering, Buein Zahra Technical University, Qazvin, Iran
| | - Nima Rezaei
- Network of Interdisciplinarity in Neonates and Infants (NINI), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- Research Center for Immunodeficiencies, Children's Medical Center, Tehran University of Medical Sciences, Tehran, Iran
- Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
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20
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Jauk S, Kramer D, Sumerauer S, Veeranki SPK, Schrempf M, Puchwein P. Machine learning-based delirium prediction in surgical in-patients: a prospective validation study. JAMIA Open 2024; 7:ooae091. [PMID: 39297150 PMCID: PMC11408728 DOI: 10.1093/jamiaopen/ooae091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2024] [Revised: 08/20/2024] [Accepted: 09/12/2024] [Indexed: 09/21/2024] Open
Abstract
Objective Delirium is a syndrome that leads to severe complications in hospitalized patients, but is considered preventable in many cases. One of the biggest challenges is to identify patients at risk in a hectic clinical routine, as most screening tools cause additional workload. The aim of this study was to validate a machine learning (ML)-based delirium prediction tool on surgical in-patients undergoing a systematic assessment of delirium. Materials and Methods 738 in-patients of a vascular surgery, a trauma surgery and an orthopedic surgery department were screened for delirium using the DOS scale twice a day over their hospital stay. Concurrently, delirium risk was predicted by the ML algorithm in real-time for all patients at admission and evening of admission. The prediction was performed automatically based on existing EHR data and without any additional documentation needed. Results 103 patients (14.0%) were screened positive for delirium using the DOS scale. Out of them, 85 (82.5%) were correctly identified by the ML algorithm. Specificity was slightly lower, detecting 463 (72.9%) out of 635 patients without delirium. The AUROC of the algorithm was 0.883 (95% CI, 0.8523-0.9147). Discussion In this prospective validation study, the implemented machine-learning algorithm was able to detect patients with delirium in surgical departments with high discriminative performance. Conclusion In future, this tool or similar decision support systems may help to replace time-intensive screening tools and enable efficient prevention of delirium.
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Affiliation(s)
- Stefanie Jauk
- Division of Technology and IT, Steiermärkische Krankenanstaltengesellschaft m.b.H. (KAGes), 8010 Graz, Austria
- PH Predicting Health GmbH, 8010 Graz, Austria
| | - Diether Kramer
- Division of Technology and IT, Steiermärkische Krankenanstaltengesellschaft m.b.H. (KAGes), 8010 Graz, Austria
- PH Predicting Health GmbH, 8010 Graz, Austria
| | - Stefan Sumerauer
- Department of Neurology, Steiermärkische Krankenanstaltengesellschaft m.b.H. (KAGes), 8036 Graz, Austria
| | - Sai Pavan Kumar Veeranki
- Division of Technology and IT, Steiermärkische Krankenanstaltengesellschaft m.b.H. (KAGes), 8010 Graz, Austria
- PH Predicting Health GmbH, 8010 Graz, Austria
| | | | - Paul Puchwein
- Department of Orthopaedics and Trauma, Medical University of Graz, 8036 Graz, Austria
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Nayak S, Amin A, Reghunath SR, Thunga G, Acharya U D, Shivashankara KN, Prabhu Attur R, Acharya LD. Development of a machine learning-based model for the prediction and progression of diabetic kidney disease: A single centred retrospective study. Int J Med Inform 2024; 190:105546. [PMID: 39003788 DOI: 10.1016/j.ijmedinf.2024.105546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 07/02/2024] [Accepted: 07/08/2024] [Indexed: 07/16/2024]
Abstract
BACKGROUND Diabetic kidney disease (DKD) is a diabetic microvascular complication often characterized by an unpredictable progression. Hence, early detection and recognition of patients vulnerable to progression is crucial. OBJECTIVE To develop a prediction model to identify the stages of DKD and the factors contributing to progression to each stage using machine learning. METHODOLOGY A retrospective study was conducted in a South Indian tertiary care hospital and collected the details of patients diagnosed with DKD from January 2017 to January 2022. Bayesian optimization-based machine learning techniques such as classification and regression were employed. The model was developed with the help of an optimization framework that effectively balances classification, prediction accuracy, and explainability. RESULTS Of the 311 patients diagnosed with DKD, 227 were selected for the study. A system for predicting DKD has been created for a patient dataset utilizing a variety of machine-learning approaches. The eXtreme gradient (XG) Boost method excelled, achieving 88.75% accuracy, 88.57% precision, 91.4% sensitivity,100% specificity, and 89.49% F1-score. An interpretable data-driven method highlights significant features for early DKD diagnosis. The best explainable prediction model uses the XG Boost classifier, revealing serum uric acid, urea, phosphorous, red blood cells, calcium, and absolute eosinophil count as the major predictors influencing the progression of DKD. In the case of regression models, the gradient boost regressor performed the best, with an R2 score of 0.97. CONCLUSION Machine learning algorithms can effectively predict the stages of DKD and thus help physicians in providing patients with personalized care at the right time.
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Affiliation(s)
- Sandhya Nayak
- Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Udupi District, Karnataka 576 104, India.
| | - Ashwini Amin
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Udupi District, Karnataka 576 104, India.
| | - Swetha R Reghunath
- Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Udupi District, Karnataka 576 104, India.
| | - Girish Thunga
- Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Udupi District, Karnataka 576 104, India.
| | - Dinesh Acharya U
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Udupi District, Karnataka 576 104, India.
| | - K N Shivashankara
- Department of General Medicine, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, Udupi District, Karnataka 576 104, India.
| | - Ravindra Prabhu Attur
- Department of Nephrology, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, Udupi District, Karnataka 576 104, India.
| | - Leelavathi D Acharya
- Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Udupi District, Karnataka 576 104, India.
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Brako F, Nkwo M. Leveraging artificial intelligence for better translation of fibre-based pharmaceutical systems into real-world benefits. Pharm Dev Technol 2024; 29:793-804. [PMID: 39166418 DOI: 10.1080/10837450.2024.2395422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2024] [Revised: 08/19/2024] [Accepted: 08/19/2024] [Indexed: 08/22/2024]
Abstract
The increasing prominence of biologics in the pharmaceutical market requires more advanced delivery systems to deliver these delicate and complex drug molecules for better therapeutic outcomes. Fibre technology has emerged as a promising approach for creating controlled and targeted drug delivery systems. Fibre-based drug delivery systems offer unprecedented opportunities for improving drug administration, fine-tuning release profiles, and advancing the realm of personalized medicine. These applications range from localized delivery at specific tissue sites to systemic drug administration while safeguarding the stability and integrity of delicate therapeutic compounds. Notwithstanding the promise of fibre-based drug delivery, several challenges such as non-scalability impede cost-effectiveness in the mass production of fibre systems. Biocompatibility and toxicity concerns must also be addressed. Furthermore, issues relating to stability, in-vitro in-vivo correlations, degradation rates, and by-product safety present additional hurdles. Pharmacoinformatics shows the impact of technologies in pharmaceutical processes. Emerging technologies such as Artificial Intelligence (AI) are a transformative force, progressively being applied to enhance various facets of pharmacy, medication development, and clinical healthcare support. However, there is a dearth of studies about the integration of AI in facilitating the translation of predominantly lab-scale pharmaceutical technologies into real-world healthcare interventions. This article explores the application of AI in fibre technology, its potential, challenges, and practical applications within the pharmaceutical field. Through a comprehensive analysis, it presents how the immense capabilities of AI can be leveraged with existing fibre technologies to revolutionize drug delivery and shape the future of therapeutic interventions by enhancing scalability, material integrity, synthesis, and development.
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Affiliation(s)
- Francis Brako
- Department of Engineering and Science, University of Greenwich, London, UK
| | - Makuochi Nkwo
- Department of Engineering and Science, School of Computing and Mathematical Sciences, University of Greenwich, Old Royal Naval College, London, UK
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Busch F, Hoffmann L, Truhn D, Ortiz-Prado E, Makowski MR, Bressem KK, Adams LC. Global cross-sectional student survey on AI in medical, dental, and veterinary education and practice at 192 faculties. BMC MEDICAL EDUCATION 2024; 24:1066. [PMID: 39342231 PMCID: PMC11439199 DOI: 10.1186/s12909-024-06035-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Accepted: 09/17/2024] [Indexed: 10/01/2024]
Abstract
BACKGROUND The successful integration of artificial intelligence (AI) in healthcare depends on the global perspectives of all stakeholders. This study aims to answer the research question: What are the attitudes of medical, dental, and veterinary students towards AI in education and practice, and what are the regional differences in these perceptions? METHODS An anonymous online survey was developed based on a literature review and expert panel discussions. The survey assessed students' AI knowledge, attitudes towards AI in healthcare, current state of AI education, and preferences for AI teaching. It consisted of 16 multiple-choice items, eight demographic queries, and one free-field comment section. Medical, dental, and veterinary students from various countries were invited to participate via faculty newsletters and courses. The survey measured technological literacy, AI knowledge, current state of AI education, preferences for AI teaching, and attitudes towards AI in healthcare using Likert scales. Data were analyzed using descriptive statistics, Mann-Whitney U-test, Kruskal-Wallis test, and Dunn-Bonferroni post hoc test. RESULTS The survey included 4313 medical, 205 dentistry, and 78 veterinary students from 192 faculties and 48 countries. Most participants were from Europe (51.1%), followed by North/South America (23.3%) and Asia (21.3%). Students reported positive attitudes towards AI in healthcare (median: 4, IQR: 3-4) and a desire for more AI teaching (median: 4, IQR: 4-5). However, they had limited AI knowledge (median: 2, IQR: 2-2), lack of AI courses (76.3%), and felt unprepared to use AI in their careers (median: 2, IQR: 1-3). Subgroup analyses revealed significant differences between the Global North and South (r = 0.025 to 0.185, all P < .001) and across continents (r = 0.301 to 0.531, all P < .001), with generally small effect sizes. CONCLUSIONS This large-scale international survey highlights medical, dental, and veterinary students' positive perceptions of AI in healthcare, their strong desire for AI education, and the current lack of AI teaching in medical curricula worldwide. The study identifies a need for integrating AI education into medical curricula, considering regional differences in perceptions and educational needs. TRIAL REGISTRATION Not applicable (no clinical trial).
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Affiliation(s)
- Felix Busch
- Department of Neuroradiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität Zu Berlin, Luisenstraße 7, 10117, Berlin, Germany.
- School of Medicine and Health, Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, TUM University Hospital, Technical University of Munich, Munich, Germany.
| | - Lena Hoffmann
- Department of Neuroradiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität Zu Berlin, Luisenstraße 7, 10117, Berlin, Germany
| | - Daniel Truhn
- Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany
| | | | - Marcus R Makowski
- School of Medicine and Health, Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, TUM University Hospital, Technical University of Munich, Munich, Germany
| | - Keno K Bressem
- School of Medicine and Health, Institute for Cardiovascular Radiology and Nuclear Medicine, German Heart Center Munich, TUM University Hospital, Technical University of Munich, Munich, Germany
| | - Lisa C Adams
- School of Medicine and Health, Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, TUM University Hospital, Technical University of Munich, Munich, Germany
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Strika Z, Petkovic K, Likic R, Batenburg R. Bridging healthcare gaps: a scoping review on the role of artificial intelligence, deep learning, and large language models in alleviating problems in medical deserts. Postgrad Med J 2024:qgae122. [PMID: 39323384 DOI: 10.1093/postmj/qgae122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2024] [Revised: 08/08/2024] [Accepted: 09/04/2024] [Indexed: 09/27/2024]
Abstract
"Medical deserts" are areas with low healthcare service levels, challenging the access, quality, and sustainability of care. This qualitative narrative review examines how artificial intelligence (AI), particularly large language models (LLMs), can address these challenges by integrating with e-Health and the Internet of Medical Things to enhance services in under-resourced areas. It explores AI-driven telehealth platforms that overcome language and cultural barriers, increasing accessibility. The utility of LLMs in providing diagnostic assistance where specialist deficits exist is highlighted, demonstrating AI's role in supplementing medical expertise and improving outcomes. Additionally, the development of AI chatbots offers preliminary medical advice, serving as initial contact points in remote areas. The review also discusses AI's role in enhancing medical education and training, supporting the professional development of healthcare workers in these regions. It assesses AI's strategic use in data analysis for effective resource allocation, identifying healthcare provision gaps. AI, especially LLMs, is seen as a promising solution for bridging healthcare gaps in "medical deserts," improving service accessibility, quality, and distribution. However, continued research and development are essential to fully realize AI's potential in addressing the challenges of medical deserts.
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Affiliation(s)
- Zdeslav Strika
- University of Zagreb School of Medicine, Salata 3, Zagreb 10000, Croatia
| | - Karlo Petkovic
- University of Zagreb School of Medicine, Salata 3, Zagreb 10000, Croatia
| | - Robert Likic
- University of Zagreb School of Medicine, Salata 3, Zagreb 10000, Croatia
- Department of Internal Medicine, Division of Clinical Pharmacology, Clinical Hospital Centre Zagreb, Kispaticeva 12, Zagreb 10000, Croatia
| | - Ronald Batenburg
- Netherlands Institute for Health Services Research (NIVEL), Otterstraat 118, Utrecht 3553, The Netherlands
- Department of Sociology, Radboud University, Thomas Van Aquinostraat 4, Nijmegen 6524, The Netherlands
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Sarafidis K, Agakidou E, Kontou A, Agakidis C, Neu J. Struggling to Understand the NEC Spectrum-Could the Integration of Metabolomics, Clinical-Laboratory Data, and Other Emerging Technologies Help Diagnosis? Metabolites 2024; 14:521. [PMID: 39452903 PMCID: PMC11509608 DOI: 10.3390/metabo14100521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2024] [Revised: 09/14/2024] [Accepted: 09/24/2024] [Indexed: 10/26/2024] Open
Abstract
Necrotizing enterocolitis (NEC) is the most prevalent and potentially fatal intestinal injury mainly affecting premature infants, with significant long-term consequences for those who survive. This review explores the scale of the problem, highlighting advancements in epidemiology, the understanding of pathophysiology, and improvements in the prediction and diagnosis of this complex, multifactorial, and multifaced disease. Additionally, we focus on the potential role of metabolomics in distinguishing NEC from other conditions, which could allow for an earlier and more accurate classification of intestinal injuries in infants. By integrating metabolomic data with other diagnostic approaches, it is hoped to enhance our ability to predict outcomes and tailor treatments, ultimately improving care for affected infants.
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Affiliation(s)
- Kosmas Sarafidis
- 1st Department of Neonatology, School of Medicine, Aristotle University of Thessaloniki, 54642 Thessaloniki, Greece; (E.A.); (A.K.)
| | - Eleni Agakidou
- 1st Department of Neonatology, School of Medicine, Aristotle University of Thessaloniki, 54642 Thessaloniki, Greece; (E.A.); (A.K.)
| | - Angeliki Kontou
- 1st Department of Neonatology, School of Medicine, Aristotle University of Thessaloniki, 54642 Thessaloniki, Greece; (E.A.); (A.K.)
| | - Charalampos Agakidis
- 1st Department of Pediatrics, School of Medicine, Aristotle University of Thessaloniki, 54642 Thessaloniki, Greece;
| | - Josef Neu
- Department of Pediatrics, Division of Neonatology, University of Florida, Gainesville, FL 32611, USA;
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26
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Alnasser AH, Hassanain MA, Alnasser MA, Alnasser AH. Critical factors challenging the integration of AI technologies in healthcare workplaces: a stakeholder assessment. J Health Organ Manag 2024; ahead-of-print. [PMID: 39300711 DOI: 10.1108/jhom-04-2024-0135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/22/2024]
Abstract
PURPOSE This study aims to identify and assess the factors challenging the integration of artificial intelligence (AI) technologies in healthcare workplaces. DESIGN/METHODOLOGY/APPROACH The study utilized a mixed approach, that starts with a literature review, then developing and testing a questionnaire survey of the factors challenging the integration of AI technologies in healthcare workplaces. In total, 46 factors were identified and classified under 6 groups. These factors were assessed by four different stakeholder categories: facilities managers, medical staff, operational staff and patients/visitors. The evaluations gathered were examined to determine the relative importance index (RII), importance rating (IR) and ranking of each factor. FINDINGS All 46 factors were assessed as "Very Important" through the overall assessment by the four stakeholder categories. The results indicated that the most important factors, across all groups, are "AI ability to learn from patient data", "insufficient data privacy measures for patients", "availability of technical support and maintenance services", "physicians' acceptance of AI in healthcare", "reliability and uptime of AI systems" and "ability to reduce medical errors". PRACTICAL IMPLICATIONS Determining the importance ratings of the factors can lead to better resource allocation and the development of strategies to facilitate the adoption and implementation of these technologies, thus promoting the development of innovative solutions to improve healthcare practices. ORIGINALITY/VALUE This study contributes to the body of knowledge in the domain of technology adoption and implementation in the medical workplace, through improving stakeholders' comprehension of the factors challenging the integration of AI technologies.
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Affiliation(s)
- Abdullah H Alnasser
- Architectural Engineering and Construction Management Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
| | - Mohammad A Hassanain
- Architectural Engineering and Construction Management Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
- Interdisciplinary Research Center for Smart Mobility and Logistics, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
| | | | - Ali H Alnasser
- Primary Healthcare Units, Al Ahsa Health Cluster, Al Ahsa, Saudi Arabia
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Abbas S, Asif M, Rehman A, Alharbi M, Khan MA, Elmitwally N. Emerging research trends in artificial intelligence for cancer diagnostic systems: A comprehensive review. Heliyon 2024; 10:e36743. [PMID: 39263113 PMCID: PMC11387343 DOI: 10.1016/j.heliyon.2024.e36743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2024] [Revised: 08/20/2024] [Accepted: 08/21/2024] [Indexed: 09/13/2024] Open
Abstract
This review article offers a comprehensive analysis of current developments in the application of machine learning for cancer diagnostic systems. The effectiveness of machine learning approaches has become evident in improving the accuracy and speed of cancer detection, addressing the complexities of large and intricate medical datasets. This review aims to evaluate modern machine learning techniques employed in cancer diagnostics, covering various algorithms, including supervised and unsupervised learning, as well as deep learning and federated learning methodologies. Data acquisition and preprocessing methods for different types of data, such as imaging, genomics, and clinical records, are discussed. The paper also examines feature extraction and selection techniques specific to cancer diagnosis. Model training, evaluation metrics, and performance comparison methods are explored. Additionally, the review provides insights into the applications of machine learning in various cancer types and discusses challenges related to dataset limitations, model interpretability, multi-omics integration, and ethical considerations. The emerging field of explainable artificial intelligence (XAI) in cancer diagnosis is highlighted, emphasizing specific XAI techniques proposed to improve cancer diagnostics. These techniques include interactive visualization of model decisions and feature importance analysis tailored for enhanced clinical interpretation, aiming to enhance both diagnostic accuracy and transparency in medical decision-making. The paper concludes by outlining future directions, including personalized medicine, federated learning, deep learning advancements, and ethical considerations. This review aims to guide researchers, clinicians, and policymakers in the development of efficient and interpretable machine learning-based cancer diagnostic systems.
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Affiliation(s)
- Sagheer Abbas
- Department of Computer Science, Prince Mohammad Bin Fahd University, Al-Khobar, KSA
| | - Muhammad Asif
- Department of Computer Science, Education University Lahore, Attock Campus, Pakistan
| | - Abdur Rehman
- School of Computer Science, National College of Business Administration and Economics, Lahore, 54000, Pakistan
| | - Meshal Alharbi
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, 11942, Alkharj, Saudi Arabia
| | - Muhammad Adnan Khan
- Riphah School of Computing & Innovation, Faculty of Computing, Riphah International University, Lahore Campus, Lahore, 54000, Pakistan
- School of Computing, Skyline University College, University City Sharjah, 1797, Sharjah, United Arab Emirates
- Department of Software, Faculty of Artificial Intelligence and Software, Gachon University, Seongnam-si, 13120, Republic of Korea
| | - Nouh Elmitwally
- Department of Computer Science, Faculty of Computers and Artificial Intelligence, Cairo University, Giza, 12613, Egypt
- School of Computing and Digital Technology, Birmingham City University, Birmingham, B4 7XG, UK
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28
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Reis FJJ, Carvalho MBLD, Neves GDA, Nogueira LC, Meziat-Filho N. Machine learning methods in physical therapy: A scoping review of applications in clinical context. Musculoskelet Sci Pract 2024; 74:103184. [PMID: 39278141 DOI: 10.1016/j.msksp.2024.103184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 08/13/2024] [Accepted: 09/10/2024] [Indexed: 09/17/2024]
Abstract
BACKGROUND Machine learning (ML) efficiently processes large datasets, showing promise in enhancing clinical practice within physical therapy. OBJECTIVE The aim of this scoping review is to provide an overview of studies using ML approaches in clinical settings of physical therapy. DATA SOURCES A scoping review was performed in PubMed, EMBASE, PEDro, Cochrane, Web of Science, and Scopus. SELECTION CRITERIA We included studies utilizing ML methods. ML was defined as the utilization of computational systems to encode patterns and relationships, enabling predictions or classifications with minimal human interference. DATA EXTRACTION AND DATA SYNTHESIS Data were extracted regarding methods, data types, performance metrics, and model availability. RESULTS Forty-two studies were included. The majority were published after 2020 (n = 25). Fourteen studies (33.3%) were in the musculoskeletal physical therapy field, nine (21.4%) in neurological, and eight (19%) in sports physical therapy. We identified 44 different ML models, with random forest being the most used. Three studies reported on model availability. We identified several clinical applications for ML-based tools, including diagnosis (n = 14), prognosis (n = 7), treatment outcomes prediction (n = 7), clinical decision support (n = 5), movement analysis (n = 4), patient monitoring (n = 3), and personalized care plan (n = 2). LIMITATION Model performance metrics, costs, model interpretability, and explainability were not reported. CONCLUSION This scope review mapped the emerging landscape of machine learning applications in physical therapy. Despite the growing interest, the field still lacks high-quality studies on validation, model availability, and acceptability to advance from research to clinical practice.
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Affiliation(s)
- Felipe J J Reis
- Physical Therapy Department, Instituto Federal do Rio de Janeiro (IFRJ), Rio de Janeiro, RJ, Brazil; School of Physical and Occupational Therapy, Faculty of Medicine, McGill University, Montreal, Canada; Pain in Motion Research Group, Department of Physiotherapy, Human Physiology and Anatomy, Faculty of Physical Education & Physiotherapy, Vrije Universiteit Brussel, Brussels, Belgium.
| | | | - Gabriela de Assis Neves
- Physical Therapy Department, Instituto Federal do Rio de Janeiro (IFRJ), Rio de Janeiro, RJ, Brazil
| | - Leandro Calazans Nogueira
- Physical Therapy Department, Instituto Federal do Rio de Janeiro (IFRJ), Rio de Janeiro, RJ, Brazil; Postgraduate Program in Rehabilitation Sciences, Centro Universitário Augusto Motta (UNISUAM), Rio de Janeiro, RJ, Brazil
| | - Ney Meziat-Filho
- Postgraduate Program in Rehabilitation Sciences, Centro Universitário Augusto Motta (UNISUAM), Rio de Janeiro, RJ, Brazil; School of Rehabilitation Sciences, Faculty of Health Sciences, Institute of Applied Health Sciences, McMaster University, Hamilton, ON, Canada
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Musbahi O, Nurek M, Pouris K, Vella-Baldacchino M, Bottle A, Hing C, Kostopoulou O, Cobb JP, Jones GG. Can ChatGPT make surgical decisions with confidence similar to experienced knee surgeons? Knee 2024; 51:120-129. [PMID: 39255525 DOI: 10.1016/j.knee.2024.08.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 08/04/2024] [Accepted: 08/15/2024] [Indexed: 09/12/2024]
Abstract
BACKGROUND Unicompartmental knee replacements (UKRs) have become an increasingly attractive option for end-stage single-compartment knee osteoarthritis (OA). However, there remains controversy in patient selection. Natural language processing (NLP) is a form of artificial intelligence (AI). We aimed to determine whether general-purpose open-source natural language programs can make decisions regarding a patient's suitability for a total knee replacement (TKR) or a UKR and how confident AI NLP programs are in surgical decision making. METHODS We conducted a case-based cohort study using data from a separate study, where participants (73 surgeons and AI NLP programs) were presented with 32 fictitious clinical case scenarios that simulated patients with predominantly medial knee OA who would require surgery. Using the overall UKR/TKR judgments of the 73 experienced knee surgeons as the gold standard reference, we calculated the sensitivity, specificity, and positive predictive value of AI NLP programs to identify whether a patient should undergo UKR. RESULTS There was disagreement between the surgeons and ChatGPT in only five scenarios (15.6%). With the 73 surgeons' decision as the gold standard, the sensitivity of ChatGPT in determining whether a patient should undergo UKR was 0.91 (95% confidence interval (CI): 0.71 to 0.98). The positive predictive value for ChatGPT was 0.87 (95% CI: 0.72 to 0.94). ChatGPT was more confident in its UKR decision making (surgeon mean confidence = 1.7, ChatGPT mean confidence = 2.4). CONCLUSIONS It has been demonstrated that ChatGPT can make surgical decisions, and exceeded the confidence of experienced knee surgeons with substantial inter-rater agreement when deciding whether a patient was most appropriate for a UKR.
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Affiliation(s)
- Omar Musbahi
- MSk Lab, Sir Michael Uren Hub, Imperial College London, London, UK.
| | - Martine Nurek
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - Kyriacos Pouris
- MSk Lab, Sir Michael Uren Hub, Imperial College London, London, UK
| | | | - Alex Bottle
- School of Public Health, Imperial College London, London, UK
| | - Caroline Hing
- St George's University Hospitals NHS Foundation Trust, London, UK
| | - Olga Kostopoulou
- Department of Surgery and Cancer, Imperial College London, London, UK; Institute of Global Health Innovation, Imperial College London, London, UK
| | - Justin P Cobb
- MSk Lab, Sir Michael Uren Hub, Imperial College London, London, UK
| | - Gareth G Jones
- MSk Lab, Sir Michael Uren Hub, Imperial College London, London, UK
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30
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Pham JH, Thongprayoon C, Miao J, Suppadungsuk S, Koirala P, Craici IM, Cheungpasitporn W. Large language model triaging of simulated nephrology patient inbox messages. Front Artif Intell 2024; 7:1452469. [PMID: 39315245 PMCID: PMC11417033 DOI: 10.3389/frai.2024.1452469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2024] [Accepted: 08/29/2024] [Indexed: 09/25/2024] Open
Abstract
Background Efficient triage of patient communications is crucial for timely medical attention and improved care. This study evaluates ChatGPT's accuracy in categorizing nephrology patient inbox messages, assessing its potential in outpatient settings. Methods One hundred and fifty simulated patient inbox messages were created based on cases typically encountered in everyday practice at a nephrology outpatient clinic. These messages were triaged as non-urgent, urgent, and emergent by two nephrologists. The messages were then submitted to ChatGPT-4 for independent triage into the same categories. The inquiry process was performed twice with a two-week period in between. ChatGPT responses were graded as correct (agreement with physicians), overestimation (higher priority), or underestimation (lower priority). Results In the first trial, ChatGPT correctly triaged 140 (93%) messages, overestimated the priority of 4 messages (3%), and underestimated the priority of 6 messages (4%). In the second trial, it correctly triaged 140 (93%) messages, overestimated the priority of 9 (6%), and underestimated the priority of 1 (1%). The accuracy did not depend on the urgency level of the message (p = 0.19). The internal agreement of ChatGPT responses was 92% with an intra-rater Kappa score of 0.88. Conclusion ChatGPT-4 demonstrated high accuracy in triaging nephrology patient messages, highlighting the potential for AI-driven triage systems to enhance operational efficiency and improve patient care in outpatient clinics.
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Affiliation(s)
- Justin H Pham
- Mayo Clinic College of Medicine and Science, Mayo Clinic, Rochester, MN, United States
| | - Charat Thongprayoon
- Department of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, United States
| | - Jing Miao
- Department of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, United States
| | - Supawadee Suppadungsuk
- Department of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, United States
- Faculty of Medicine Ramathibodi Hospital, Chakri Naruebodindra Medical Institute, Mahidol University, Samut Prakan, Thailand
| | - Priscilla Koirala
- Department of Internal Medicine, Mayo Clinic, Rochester, MN, United States
| | - Iasmina M Craici
- Department of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, United States
| | - Wisit Cheungpasitporn
- Department of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, United States
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Khan A, Zubair S, Shuaib M, Sheneamer A, Alam S, Assiri B. Development of a robust parallel and multi-composite machine learning model for improved diagnosis of Alzheimer's disease: correlation with dementia-associated drug usage and AT(N) protein biomarkers. Front Neurosci 2024; 18:1391465. [PMID: 39308946 PMCID: PMC11412962 DOI: 10.3389/fnins.2024.1391465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Accepted: 08/12/2024] [Indexed: 09/25/2024] Open
Abstract
Introduction Machine learning (ML) algorithms and statistical modeling offer a potential solution to offset the challenge of diagnosing early Alzheimer's disease (AD) by leveraging multiple data sources and combining information on neuropsychological, genetic, and biomarker indicators. Among others, statistical models are a promising tool to enhance the clinical detection of early AD. In the present study, early AD was diagnosed by taking into account characteristics related to whether or not a patient was taking specific drugs and a significant protein as a predictor of Amyloid-Beta (Aβ), tau, and ptau [AT(N)] levels among participants. Methods In this study, the optimization of predictive models for the diagnosis of AD pathologies was carried out using a set of baseline features. The model performance was improved by incorporating additional variables associated with patient drugs and protein biomarkers into the model. The diagnostic group consisted of five categories (cognitively normal, significant subjective memory concern, early mildly cognitively impaired, late mildly cognitively impaired, and AD), resulting in a multinomial classification challenge. In particular, we examined the relationship between AD diagnosis and the use of various drugs (calcium and vitamin D supplements, blood-thinning drugs, cholesterol-lowering drugs, and cognitive drugs). We propose a hybrid-clinical model that runs multiple ML models in parallel and then takes the majority's votes, enhancing the accuracy. We also assessed the significance of three cerebrospinal fluid biomarkers, Aβ, tau, and ptau in the diagnosis of AD. We proposed that a hybrid-clinical model be used to simulate the MRI-based data, with five diagnostic groups of individuals, with further refinement that includes preclinical characteristics of the disorder. The proposed design builds a Meta-Model for four different sets of criteria. The set criteria are as follows: to diagnose from baseline features, baseline and drug features, baseline and protein features, and baseline, drug and protein features. Results We were able to attain a maximum accuracy of 97.60% for baseline and protein data. We observed that the constructed model functioned effectively when all five drugs were included and when any single drug was used to diagnose the response variable. Interestingly, the constructed Meta-Model worked well when all three protein biomarkers were included, as well as when a single protein biomarker was utilized to diagnose the response variable. Discussion It is noteworthy that we aimed to construct a pipeline design that incorporates comprehensive methodologies to detect Alzheimer's over wide-ranging input values and variables in the current study. Thus, the model that we developed could be used by clinicians and medical experts to advance Alzheimer's diagnosis and as a starting point for future research into AD and other neurodegenerative syndromes.
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Affiliation(s)
- Afreen Khan
- Department of Computer Application, Faculty of Engineering & IT, Integral University, Lucknow, India
| | - Swaleha Zubair
- Department of Computer Science, Faculty of Science, Aligarh Muslim University, Aligarh, India
| | - Mohammed Shuaib
- Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Saudi Arabia
| | - Abdullah Sheneamer
- Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Saudi Arabia
| | - Shadab Alam
- Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Saudi Arabia
| | - Basem Assiri
- Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Saudi Arabia
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Wilhelm C, Steckelberg A, Rebitschek FG. Is artificial intelligence for medical professionals serving the patients? : Protocol for a systematic review on patient-relevant benefits and harms of algorithmic decision-making. Syst Rev 2024; 13:228. [PMID: 39242544 PMCID: PMC11378383 DOI: 10.1186/s13643-024-02646-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2024] [Accepted: 08/22/2024] [Indexed: 09/09/2024] Open
Abstract
BACKGROUND Algorithmic decision-making (ADM) utilises algorithms to collect and process data and develop models to make or support decisions. Advances in artificial intelligence (AI) have led to the development of support systems that can be superior to medical professionals without AI support in certain tasks. However, whether patients can benefit from this remains unclear. The aim of this systematic review is to assess the current evidence on patient-relevant benefits and harms, such as improved survival rates and reduced treatment-related complications, when healthcare professionals use ADM systems (developed using or working with AI) compared to healthcare professionals without AI-related ADM (standard care)-regardless of the clinical issues. METHODS Following the PRISMA statement, MEDLINE and PubMed (via PubMed), Embase (via Elsevier) and IEEE Xplore will be searched using English free text terms in title/abstract, Medical Subject Headings (MeSH) terms and Embase Subject Headings (Emtree fields). Additional studies will be identified by contacting authors of included studies and through reference lists of included studies. Grey literature searches will be conducted in Google Scholar. Risk of bias will be assessed by using Cochrane's RoB 2 for randomised trials and ROBINS-I for non-randomised trials. Transparent reporting of the included studies will be assessed using the CONSORT-AI extension statement. Two researchers will screen, assess and extract from the studies independently, with a third in case of conflicts that cannot be resolved by discussion. DISCUSSION It is expected that there will be a substantial shortage of suitable studies that compare healthcare professionals with and without ADM systems concerning patient-relevant endpoints. This can be attributed to the prioritisation of technical quality criteria and, in some cases, clinical parameters over patient-relevant endpoints in the development of study designs. Furthermore, it is anticipated that a significant portion of the identified studies will exhibit relatively poor methodological quality and provide only limited generalisable results. SYSTEMATIC REVIEW REGISTRATION This study is registered within PROSPERO (CRD42023412156).
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Affiliation(s)
- Christoph Wilhelm
- Institute of Health and Nursing Sciences, Martin Luther University Halle-Wittenberg, Magdeburger Str. 8, Halle, 06112, Germany.
- Harding Center for Risk Literacy, Faculty of Health Sciences Brandenburg, University of Potsdam, Virchowstr. 2, Potsdam, 14482, Germany.
| | - Anke Steckelberg
- Institute of Health and Nursing Sciences, Martin Luther University Halle-Wittenberg, Magdeburger Str. 8, Halle, 06112, Germany
| | - Felix G Rebitschek
- Harding Center for Risk Literacy, Faculty of Health Sciences Brandenburg, University of Potsdam, Virchowstr. 2, Potsdam, 14482, Germany
- Max Planck Institute for Human Development, Lentzeallee 94, Berlin, 14195, Germany
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Yang D, Miao Y, Liu C, Zhang N, Zhang D, Guo Q, Gao S, Li L, Wang J, Liang S, Li P, Bai X, Zhang K. Advances in artificial intelligence applications in the field of lung cancer. Front Oncol 2024; 14:1449068. [PMID: 39309740 PMCID: PMC11412794 DOI: 10.3389/fonc.2024.1449068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2024] [Accepted: 08/19/2024] [Indexed: 09/25/2024] Open
Abstract
Lung cancer remains a leading cause of cancer-related deaths globally, with its incidence steadily rising each year, representing a significant threat to human health. Early detection, diagnosis, and timely treatment play a crucial role in improving survival rates and reducing mortality. In recent years, significant and rapid advancements in artificial intelligence (AI) technology have found successful applications in various clinical areas, especially in the diagnosis and treatment of lung cancer. AI not only improves the efficiency and accuracy of physician diagnosis but also aids in patient treatment and management. This comprehensive review presents an overview of fundamental AI-related algorithms and highlights their clinical applications in lung nodule detection, lung cancer pathology classification, gene mutation prediction, treatment strategies, and prognosis. Additionally, the rapidly advancing field of AI-based three-dimensional (3D) reconstruction in lung cancer surgical resection is discussed. Lastly, the limitations of AI and future prospects are addressed.
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Affiliation(s)
- Di Yang
- Clinical Medical College of Hebei University, Affiliated Hospital of Hebei University, Baoding, China
- Thoracic Surgery Department, Affiliated Hospital of Hebei University, Baoding, China
| | - Yafei Miao
- Clinical Medical College of Hebei University, Affiliated Hospital of Hebei University, Baoding, China
- Thoracic Surgery Department, Affiliated Hospital of Hebei University, Baoding, China
| | - Changjiang Liu
- Thoracic Surgery Department, Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Nan Zhang
- Thoracic Surgery Department, Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Duo Zhang
- Thoracic Surgery Department, Affiliated Hospital of Hebei University, Baoding, China
| | - Qiang Guo
- Thoracic Surgery Department, Affiliated Hospital of Hebei University, Baoding, China
| | - Shuo Gao
- Basic Research Key Laboratory of General Surgery for Digital Medicine, Affiliated Hospital of Hebei University, Baoding, China
- Information center, Affiliated Hospital of Hebei University, Baoding, China
| | - Linqian Li
- Basic Research Key Laboratory of General Surgery for Digital Medicine, Affiliated Hospital of Hebei University, Baoding, China
- Institute of Life Science and Green Development, Hebei University, Baoding, China
- 3D Image and 3D Printing Center, Affiliated Hospital of Hebei University, Baoding, China
| | - Jianing Wang
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
| | - Si Liang
- Basic Research Key Laboratory of General Surgery for Digital Medicine, Affiliated Hospital of Hebei University, Baoding, China
- Institute of Life Science and Green Development, Hebei University, Baoding, China
| | - Peng Li
- Basic Research Key Laboratory of General Surgery for Digital Medicine, Affiliated Hospital of Hebei University, Baoding, China
- Institute of Life Science and Green Development, Hebei University, Baoding, China
| | - Xuan Bai
- Basic Research Key Laboratory of General Surgery for Digital Medicine, Affiliated Hospital of Hebei University, Baoding, China
- Institute of Life Science and Green Development, Hebei University, Baoding, China
| | - Ke Zhang
- Thoracic Surgery Department, Affiliated Hospital of Hebei University, Baoding, China
- Basic Research Key Laboratory of General Surgery for Digital Medicine, Affiliated Hospital of Hebei University, Baoding, China
- Institute of Life Science and Green Development, Hebei University, Baoding, China
- 3D Image and 3D Printing Center, Affiliated Hospital of Hebei University, Baoding, China
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Matsuoka M, Onodera T, Fukuda R, Iwasaki K, Hamasaki M, Ebata T, Hosokawa Y, Kondo E, Iwasaki N. Evaluating the Alignment of Artificial Intelligence-Generated Recommendations With Clinical Guidelines Focused on Soft Tissue Tumors. J Surg Oncol 2024. [PMID: 39233558 DOI: 10.1002/jso.27874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2024] [Accepted: 08/24/2024] [Indexed: 09/06/2024]
Abstract
BACKGROUND The integration of artificial intelligence (AI), particularly, in oncology, has significantly shifted the paradigms of medical diagnostics and treatment planning. However, the utility of AI, specifically OpenAI's ChatGPT, in soft tissue sarcoma treatment, remains unclear. METHODS We evaluated ChatGPT's alignment with the Japanese Orthopaedic Association (JOA) clinical practice guidelines on the management of soft tissue tumors 2020. Twenty-two clinical questions (CQs) were formulated to encompass various aspects of sarcoma diagnosis, treatment, and management. ChatGPT's responses were classified into "Complete Alignment," "Partial Alignment," or "Nonalignment" based on the recommendation and strength of evidence. RESULTS ChatGPT demonstrated an 86% alignment rate with the JOA guidelines. The AI provided two instances of complete alignment and 17 instances of partial alignment, indicating a strong capability to match guideline criteria for most questions. However, three discrepancies were identified in areas concerning the treatment of atypical lipomatous tumors, perioperative chemotherapy for synovial sarcoma, and treatment strategies for elderly patients with malignant soft tissue tumors. Reassessment with guideline input led to some adjustments, revealing both the potential and limitations of AI in complex sarcoma care. CONCLUSION Our study demonstrates that AI, specifically ChatGPT, can align with clinical guidelines for soft tissue sarcoma treatment. It also underscores the need for continuous refinement and cautious integration of AI in medical decision-making, particularly in the context of treatment for soft tissue sarcoma.
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Affiliation(s)
- Masatake Matsuoka
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Tomohiro Onodera
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Ryuichi Fukuda
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Koji Iwasaki
- Department of Functional Reconstruction for the Knee Joint, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Masanari Hamasaki
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Taku Ebata
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Yoshiaki Hosokawa
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Eiji Kondo
- Centre for Sports Medicine, Hokkaido University Hospital, Sapporo, Hokkaido, Japan
| | - Norimasa Iwasaki
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
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Wang X, Huang X. Risk factors and predictive indicators of rupture in cerebral aneurysms. Front Physiol 2024; 15:1454016. [PMID: 39301423 PMCID: PMC11411460 DOI: 10.3389/fphys.2024.1454016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2024] [Accepted: 08/23/2024] [Indexed: 09/22/2024] Open
Abstract
Cerebral aneurysms are abnormal dilations of blood vessels in the brain that have the potential to rupture, leading to subarachnoid hemorrhage and other serious complications. Early detection and prediction of aneurysm rupture are crucial for effective management and prevention of rupture-related morbidities and mortalities. This review aims to summarize the current knowledge on risk factors and predictive indicators of rupture in cerebral aneurysms. Morphological characteristics such as aneurysm size, shape, and location, as well as hemodynamic factors including blood flow patterns and wall shear stress, have been identified as important factors influencing aneurysm stability and rupture risk. In addition to these traditional factors, emerging evidence suggests that biological and genetic factors, such as inflammation, extracellular matrix remodeling, and genetic polymorphisms, may also play significant roles in aneurysm rupture. Furthermore, advancements in computational fluid dynamics and machine learning algorithms have enabled the development of novel predictive models for rupture risk assessment. However, challenges remain in accurately predicting aneurysm rupture, and further research is needed to validate these predictors and integrate them into clinical practice. By elucidating and identifying the various risk factors and predictive indicators associated with aneurysm rupture, we can enhance personalized risk assessment and optimize treatment strategies for patients with cerebral aneurysms.
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Affiliation(s)
- Xiguang Wang
- Department of Research & Development Management, Shanghai Aohua Photoelectricity Endoscope Co., Ltd., Shanghai, China
| | - Xu Huang
- Department of Research & Development Management, Shanghai Aohua Photoelectricity Endoscope Co., Ltd., Shanghai, China
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Akyon SH, Akyon FC, Camyar AS, Hızlı F, Sari T, Hızlı Ş. Evaluating the Capabilities of Generative AI Tools in Understanding Medical Papers: Qualitative Study. JMIR Med Inform 2024; 12:e59258. [PMID: 39230947 PMCID: PMC11411230 DOI: 10.2196/59258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2024] [Revised: 06/16/2024] [Accepted: 07/05/2024] [Indexed: 09/05/2024] Open
Abstract
BACKGROUND Reading medical papers is a challenging and time-consuming task for doctors, especially when the papers are long and complex. A tool that can help doctors efficiently process and understand medical papers is needed. OBJECTIVE This study aims to critically assess and compare the comprehension capabilities of large language models (LLMs) in accurately and efficiently understanding medical research papers using the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklist, which provides a standardized framework for evaluating key elements of observational study. METHODS The study is a methodological type of research. The study aims to evaluate the understanding capabilities of new generative artificial intelligence tools in medical papers. A novel benchmark pipeline processed 50 medical research papers from PubMed, comparing the answers of 6 LLMs (GPT-3.5-Turbo, GPT-4-0613, GPT-4-1106, PaLM 2, Claude v1, and Gemini Pro) to the benchmark established by expert medical professors. Fifteen questions, derived from the STROBE checklist, assessed LLMs' understanding of different sections of a research paper. RESULTS LLMs exhibited varying performance, with GPT-3.5-Turbo achieving the highest percentage of correct answers (n=3916, 66.9%), followed by GPT-4-1106 (n=3837, 65.6%), PaLM 2 (n=3632, 62.1%), Claude v1 (n=2887, 58.3%), Gemini Pro (n=2878, 49.2%), and GPT-4-0613 (n=2580, 44.1%). Statistical analysis revealed statistically significant differences between LLMs (P<.001), with older models showing inconsistent performance compared to newer versions. LLMs showcased distinct performances for each question across different parts of a scholarly paper-with certain models like PaLM 2 and GPT-3.5 showing remarkable versatility and depth in understanding. CONCLUSIONS This study is the first to evaluate the performance of different LLMs in understanding medical papers using the retrieval augmented generation method. The findings highlight the potential of LLMs to enhance medical research by improving efficiency and facilitating evidence-based decision-making. Further research is needed to address limitations such as the influence of question formats, potential biases, and the rapid evolution of LLM models.
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Affiliation(s)
| | - Fatih Cagatay Akyon
- SafeVideo AI, San Francisco, CA, United States
- Graduate School of Informatics, Middle East Technical University, Ankara, Turkey
| | - Ahmet Sefa Camyar
- Department of Internal Medicine, Ankara Etlik City Hospital, Ankara, Turkey
| | - Fatih Hızlı
- Faculty of Medicine, Ankara Yildirim Beyazit University, Ankara, Turkey
| | - Talha Sari
- SafeVideo AI, San Francisco, CA, United States
- Department of Computer Science, Istanbul Technical University, Istanbul, Turkey
| | - Şamil Hızlı
- Department of Pediatric Gastroenterology, Children Hospital, Ankara Bilkent City Hospital, Ankara Yildirim Beyazit University, Ankara, Turkey
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Zhang Y, Gao W, Yu H, Dong J, Xia Y. Artificial Intelligence-Based Facial Palsy Evaluation: A Survey. IEEE Trans Neural Syst Rehabil Eng 2024; 32:3116-3134. [PMID: 39172615 DOI: 10.1109/tnsre.2024.3447881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/24/2024]
Abstract
Facial palsy evaluation (FPE) aims to assess facial palsy severity of patients, which plays a vital role in facial functional treatment and rehabilitation. The traditional manners of FPE are based on subjective judgment by clinicians, which may ultimately depend on individual experience. Compared with subjective and manual evaluation, objective and automated evaluation using artificial intelligence (AI) has shown great promise in improving traditional manners and recently received significant attention. The motivation of this survey paper is mainly to provide a systemic review that would guide researchers in conducting their future research work and thus make automatic FPE applicable in real-life situations. In this survey, we comprehensively review the state-of-the-art development of AI-based FPE. First, we summarize the general pipeline of FPE systems with the related background introduction. Following this pipeline, we introduce the existing public databases and give the widely used objective evaluation metrics of FPE. In addition, the preprocessing methods in FPE are described. Then, we provide an overview of selected key publications from 2008 and summarize the state-of-the-art methods of FPE that are designed based on AI techniques. Finally, we extensively discuss the current research challenges faced by FPE and provide insights about potential future directions for advancing state-of-the-art research in this field.
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Parinitha MS, Doddawad VG, Kalgeri SH, Gowda SS, Patil S. Impact of Artificial Intelligence in Endodontics: Precision, Predictions, and Prospects. JOURNAL OF MEDICAL SIGNALS & SENSORS 2024; 14:25. [PMID: 39380771 PMCID: PMC11460994 DOI: 10.4103/jmss.jmss_7_24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2024] [Revised: 04/16/2024] [Accepted: 04/22/2024] [Indexed: 10/10/2024]
Abstract
Artificial intelligence (AI) has become increasingly prevalent and significant across many industries, including the dental field. AI has shown accuracy and precision in detecting, evaluating, and predicting diseases. It can imitate human intelligence to carry out sophisticated predictions and decision-making in the health-care industry, especially in endodontics. AI models have demonstrated a wide range of applications in the field of endodontics. These include examining the anatomy of the root canal system, predicting the survival of dental pulp stem cells, gauging working lengths, identifying per apical lesions and root fractures, and predicting the outcome of retreatment treatments. Future uses of this technology were discussed in terms of robotic endodontic surgery, drug-drug interactions, patient care, scheduling, and prognostic diagnosis.
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Affiliation(s)
- M. S. Parinitha
- Department of Conservative Dentistry and Endodontics, JSS Dental College and Hospital, A Constituent College of JSS Academy of Higher Education and Research, Mysore, Karnataka, India
| | - Vidya Gowdappa Doddawad
- Department of Oral Pathology and Microbiology, JSS Dental College and Hospital, A Constituent College of JSS Academy of Higher Education and Research, Mysore, Karnataka, India
| | - Sowmya Halasabalu Kalgeri
- Department of Conservative Dentistry and Endodontics, JSS Dental College and Hospital, JSS Academy of Higher Education and Research, Mysore, Karnataka, India
| | - Samyuka S. Gowda
- Department of Conservative Dentistry and Endodontics, JSS Dental College and Hospital, JSS Academy of Higher Education and Research, Mysore, Karnataka, India
| | - Sahana Patil
- Department of Conservative Dentistry and Endodontics, JSS Dental College and Hospital, JSS Academy of Higher Education and Research, Mysore, Karnataka, India
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Abdelgadir Y, Thongprayoon C, Miao J, Suppadungsuk S, Pham JH, Mao MA, Craici IM, Cheungpasitporn W. AI integration in nephrology: evaluating ChatGPT for accurate ICD-10 documentation and coding. Front Artif Intell 2024; 7:1457586. [PMID: 39286549 PMCID: PMC11402808 DOI: 10.3389/frai.2024.1457586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2024] [Accepted: 08/12/2024] [Indexed: 09/19/2024] Open
Abstract
Background Accurate ICD-10 coding is crucial for healthcare reimbursement, patient care, and research. AI implementation, like ChatGPT, could improve coding accuracy and reduce physician burden. This study assessed ChatGPT's performance in identifying ICD-10 codes for nephrology conditions through case scenarios for pre-visit testing. Methods Two nephrologists created 100 simulated nephrology cases. ChatGPT versions 3.5 and 4.0 were evaluated by comparing AI-generated ICD-10 codes against predetermined correct codes. Assessments were conducted in two rounds, 2 weeks apart, in April 2024. Results In the first round, the accuracy of ChatGPT for assigning correct diagnosis codes was 91 and 99% for version 3.5 and 4.0, respectively. In the second round, the accuracy of ChatGPT for assigning the correct diagnosis code was 87% for version 3.5 and 99% for version 4.0. ChatGPT 4.0 had higher accuracy than ChatGPT 3.5 (p = 0.02 and 0.002 for the first and second round respectively). The accuracy did not significantly differ between the two rounds (p > 0.05). Conclusion ChatGPT 4.0 can significantly improve ICD-10 coding accuracy in nephrology through case scenarios for pre-visit testing, potentially reducing healthcare professionals' workload. However, the small error percentage underscores the need for ongoing review and improvement of AI systems to ensure accurate reimbursement, optimal patient care, and reliable research data.
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Affiliation(s)
- Yasir Abdelgadir
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, United States
| | - Charat Thongprayoon
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, United States
| | - Jing Miao
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, United States
| | - Supawadee Suppadungsuk
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, United States
- Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Samut Prakan, Thailand
| | - Justin H Pham
- Mayo Clinic College of Medicine and Science, Mayo Clinic, Rochester, MN, United States
| | - Michael A Mao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, FL, United States
| | - Iasmina M Craici
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, United States
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Kalra N, Verma P, Verma S. Advancements in AI based healthcare techniques with FOCUS ON diagnostic techniques. Comput Biol Med 2024; 179:108917. [PMID: 39059212 DOI: 10.1016/j.compbiomed.2024.108917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 07/15/2024] [Accepted: 07/15/2024] [Indexed: 07/28/2024]
Abstract
Since the past decade, the interest towards more precise and efficient healthcare techniques with special emphasis on diagnostic techniques has increased. Artificial Intelligence has proved to be instrumental in development of various such techniques. The various types of AI like ML, NLP, RPA etc. are being used, which have streamlined and organised the Electronic Health Records (EHR) along with aiding the healthcare provider with decision making and sample and data analysis. This article also deals with the 3 major categories of diagnostic techniques - Imaging based, Pathology based and Preventive diagnostic techniques and what all changes and modifications were brought upon them, due to use of AI. Due to such a high demand, the investment in AI based healthcare techniques has increased substantially, with predicted market size of almost 188 billon USD by 2030. In India itself, AI in healthcare is expected to raise the GDP by 25 billion USD by 2028. But there are also several challenges associated with this like unavailability of quality data, black box issue etc. One of the major challenges is the ethical considerations and issues during use of medical records as it is a very sensitive document. Due to this, there is several trust issues associated with adoption of AI by many organizations. These challenges have also been discussed in this article. Need for further development in the AI based diagnostic techniques is also done in the article. Alongside, the production of such techniques and devices which are easy to use and simple to incorporate into the daily workflows have immense scope in the upcoming times. The increasing scope of Clinical Decision Support System, Telemedicine etc. make AI a promising field in the healthcare and diagnostics arena. Concluding the article, it can be said that despite the presence of various challenges to the implementation and usage, the future prospects for AI in healthcare is immense and work needs to be done in order to ensure the availability of resources for same so that high level of accuracy can be achieved and better health outcomes can be provided to patients. Ethical concerns need to be addressed for smooth implementation and to reduce the burden of the developers, which has been discussed in this narrative review article.
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Affiliation(s)
- Nishita Kalra
- Department of Pharmaceutical Chemistry/Analysis, Delhi Pharmaceutical Sciences & Research University, Pushp Vihar, Sector 3, New Delhi, 110017, India
| | - Prachi Verma
- Department of Pharmaceutical Chemistry/Analysis, Delhi Pharmaceutical Sciences & Research University, Pushp Vihar, Sector 3, New Delhi, 110017, India
| | - Surajpal Verma
- Department of Pharmaceutical Chemistry/Analysis, Delhi Pharmaceutical Sciences & Research University, Pushp Vihar, Sector 3, New Delhi, 110017, India.
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Basile FV, Oliveira TS. Using Machine Learning to Select Breast Implant Volume. Plast Reconstr Surg 2024; 154:470e-477e. [PMID: 37843252 DOI: 10.1097/prs.0000000000011146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Abstract
BACKGROUND In breast augmentation surgery, selection of the appropriate breast implant size is a crucial step that can greatly affect patient satisfaction and the outcome of the procedure. However, this decision is often based on the subjective judgment of the surgeon and the patient, which can lead to suboptimal results. The authors aimed to develop a machine-learning approach that can accurately predict the size of breast implants selected for breast augmentation surgery. METHODS The authors collected data on patient demographic characteristics, medical history, and surgeon preferences from a sample of 1000 consecutive patients who underwent breast augmentation. This information was used to train and test a supervised machine-learning model to predict the size of breast implant needed. RESULTS The study demonstrated the effectiveness of the algorithm in predicting breast implant size, achieving a Pearson correlation coefficient of 0.9335 ( P < 0.001). The model generated accurate predictions in 86% of instances, with a mean absolute error of 27.10 mL. Its effectiveness was confirmed in the reoperation group, in which 36 of 57 patients (63%) would have received a more suitable implant size if the model's suggestion had been followed, potentially avoiding reoperation. CONCLUSIONS The findings show that machine learning can accurately predict the needed size of breast implants in augmentation surgery. By integrating the artificial intelligence model into a decision support system for breast augmentation surgery, essential guidance can be provided to surgeons and patients. This approach not only streamlines the implant selection process but also facilitates enhanced communication and decision-making, ultimately leading to more reliable outcomes and improved patient satisfaction.
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Choudhary A, Anand A, Singh A, Roy P, Singh N, Kumar V, Sharma S, Baranwal M. Machine learning-based ensemble approach in prediction of lung cancer predisposition using XRCC1 gene polymorphism. J Biomol Struct Dyn 2024; 42:7828-7837. [PMID: 37545160 DOI: 10.1080/07391102.2023.2242492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Accepted: 07/23/2023] [Indexed: 08/08/2023]
Abstract
The employment of machine learning approaches has shown promising results in predicting cancer. In the current study, polymorphisms data of five single nucleotide polymorphisms (SNPs) of DNA repair gene XRCC1 (XRCC1 399, XRCC1 194, XRCC1 206, XRCC1 632, XRCC1 280) of the north Indian population along with four smoking status data is considered as an input to the proposed ensemble model to predict the risk of individual susceptibility to the lung cancer. The prediction accuracy of the proposed ensemble model for cancer predisposition was found to be 85%. The model performance is also evaluated using sensitivity, specificity, precision and the Gini index, which is found in the range of 0.83-0.87. The proposed model also outperformed in all evaluation parameters when compared with the individual Model (LM, SVM, RF, KNN and baseline neural net). Collectively, current results suggest the potential of the proposed ensemble model in predicting the risk of cancer based on XRCC1 SNPs data.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Abhishek Choudhary
- Department of Computer Science, Thapar Institute of Engineering & Technology, India
| | - Adarsh Anand
- Department of Electronics & Communication Engineering, Thapar Institute of Engineering & Technology, India
| | - Amrita Singh
- Department of Biotechnology, Thapar Institute of Engineering & Technology, Patiala, Punjab, India
| | - Pratima Roy
- Department of Biotechnology, Thapar Institute of Engineering & Technology, Patiala, Punjab, India
| | - Navneet Singh
- Department of Pulmonary Medicine, Post Graduate Institute of Education and Medical Research (PGIMER), Chandigarh, India
| | - Vinay Kumar
- Department of Electronics & Communication Engineering, Thapar Institute of Engineering & Technology, India
| | - Siddharth Sharma
- Department of Biotechnology, Thapar Institute of Engineering & Technology, Patiala, Punjab, India
| | - Manoj Baranwal
- Department of Biotechnology, Thapar Institute of Engineering & Technology, Patiala, Punjab, India
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Gharibshahian M, Torkashvand M, Bavisi M, Aldaghi N, Alizadeh A. Recent advances in artificial intelligent strategies for tissue engineering and regenerative medicine. Skin Res Technol 2024; 30:e70016. [PMID: 39189880 PMCID: PMC11348508 DOI: 10.1111/srt.70016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2024] [Accepted: 08/05/2024] [Indexed: 08/28/2024]
Abstract
BACKGROUND Tissue engineering and regenerative medicine (TERM) aim to repair or replace damaged or lost tissues or organs due to accidents, diseases, or aging, by applying different sciences. For this purpose, an essential part of TERM is the designing, manufacturing, and evaluating of scaffolds, cells, tissues, and organs. Artificial intelligence (AI) or the intelligence of machines or software can be effective in all areas where computers play a role. METHODS The "artificial intelligence," "machine learning," "tissue engineering," "clinical evaluation," and "scaffold" keywords used for searching in various databases and articles published from 2000 to 2024 were evaluated. RESULTS The combination of tissue engineering and AI has created a new generation of technological advancement in the biomedical industry. Experience in TERM has been refined using advanced design and manufacturing techniques. Advances in AI, particularly deep learning, offer an opportunity to improve scientific understanding and clinical outcomes in TERM. CONCLUSION The findings of this research show the high potential of AI, machine learning, and robots in the selection, design, and fabrication of scaffolds, cells, tissues, or organs, and their analysis, characterization, and evaluation after their implantation. AI can be a tool to accelerate the introduction of tissue engineering products to the bedside. HIGHLIGHTS The capabilities of artificial intelligence (AI) can be used in different ways in all the different stages of TERM and not only solve the existing limitations, but also accelerate the processes, increase efficiency and precision, reduce costs, and complications after transplantation. ML predicts which technologies have the most efficient and easiest path to enter the market and clinic. The use of AI along with these imaging techniques can lead to the improvement of diagnostic information, the reduction of operator errors when reading images, and the improvement of image analysis (such as classification, localization, regression, and segmentation).
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Affiliation(s)
- Maliheh Gharibshahian
- Nervous System Stem Cells Research CenterSemnan University of Medical SciencesSemnanIran
- Department of Tissue Engineering and Applied Cell SciencesSchool of MedicineSemnan University of Medical SciencesSemnanIran
| | | | - Mahya Bavisi
- Department of Tissue Engineering and Applied Cell SciencesSchool of Advanced Technologies in MedicineIran University of Medical SciencesTehranIran
| | - Niloofar Aldaghi
- Student Research CommitteeSchool of MedicineShahroud University of Medical SciencesShahroudIran
| | - Akram Alizadeh
- Nervous System Stem Cells Research CenterSemnan University of Medical SciencesSemnanIran
- Department of Tissue Engineering and Applied Cell SciencesSchool of MedicineSemnan University of Medical SciencesSemnanIran
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Hatherley J, Kinderlerer A, Bjerring JC, Munch LA, Threlfall L. The FHJ debate: Will artificial intelligence replace clinical decision making within our lifetimes? Future Healthc J 2024; 11:100178. [PMID: 39371529 PMCID: PMC11452837 DOI: 10.1016/j.fhj.2024.100178] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2024] [Accepted: 08/29/2024] [Indexed: 10/08/2024]
Affiliation(s)
- Joshua Hatherley
- Aarhus University, Department of Philosophy and History of Ideas, Denmark
| | | | | | | | - Lynsey Threlfall
- Royal Victoria Infirmary Newcastle, Newcastle University - BRC, England
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N A, Chowdhury RR, L P, Peter RM, Vv A. Perception of the Adoption of Artificial Intelligence in Healthcare Practices Among Healthcare Professionals in a Tertiary Care Hospital: A Cross-Sectional Study. Cureus 2024; 16:e69910. [PMID: 39439624 PMCID: PMC11495239 DOI: 10.7759/cureus.69910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2024] [Accepted: 09/22/2024] [Indexed: 10/25/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) has the potential to transform healthcare practices, but the successful adoption of AI depends on the perception and acceptance of healthcare professionals. This study aimed to assess the understanding of AI concepts, recognize the attitude toward AI integration, and identify the barriers to AI adoption among healthcare professionals in a tertiary care hospital. METHODS A cross-sectional study was conducted among 200 healthcare professionals, including doctors, nurses, and paramedics, in a tertiary care teaching hospital in Chengalpattu district, India. A semi-structured questionnaire was used to collect data on sociodemographic characteristics, knowledge, attitude, and perceived barriers to AI adoption. The chi-square test was used to analyze the associations between variables. RESULTS The majority of the participants, i.e., 54% (n = 108), had moderate knowledge about AI adoption, while 48% (n = 96) had a low attitude toward it. Barriers to the adoption of AI in healthcare practices among healthcare professionals were low among the majority, i.e., 49% (n = 98), of the participants. A statistically significant association was found between knowledge and attitude (X² = 18.052, df = 4, p = 0.001), i.e., healthcare professionals with moderate knowledge levels had low attitudes toward the adoption of AI. A statistically significant association was also found between knowledge and perceived barriers (X² = 31.235, df = 4, p = 0.00), i.e., healthcare professionals with higher knowledge levels perceived lower barriers to the adoption of AI. CONCLUSION The study highlights the need for education and training to improve knowledge, foster positive attitudes, and address the perceived barriers to AI adoption among healthcare professionals. Future research should focus on developing targeted interventions to enhance the understanding and acceptance of AI in healthcare practices.
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Affiliation(s)
- Adithyan N
- Community Medicine, SRM Medical College Hospital and Research Centre, SRM Institute of Science and Technology, Chengalpattu, IND
| | - Rupal Roy Chowdhury
- Community Medicine, SRM Medical College Hospital and Research Centre, SRM Institute of Science and Technology, Chengalpattu, IND
| | - Padmavathy L
- Community Medicine, SRM Medical College Hospital and Research Centre, SRM Institute of Science and Technology, Chengalpattu, IND
| | - Roshni Mary Peter
- Community Medicine, SRM Medical College Hospital and Research Centre, SRM Institute of Science and Technology, Chengalpattu, IND
| | - Anantharaman Vv
- Community Medicine, SRM Medical College Hospital and Research Centre, SRM Institute of Science and Technology, Chengalpattu, IND
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Omidian H. Synergizing blockchain and artificial intelligence to enhance healthcare. Drug Discov Today 2024; 29:104111. [PMID: 39034026 DOI: 10.1016/j.drudis.2024.104111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 07/09/2024] [Accepted: 07/16/2024] [Indexed: 07/23/2024]
Abstract
This perspective paper explores the synergistic potential of blockchain and artificial intelligence (AI) in transforming healthcare. It begins with an overview of blockchain's role in healthcare data management, security, the pharmaceutical supply chain, clinical trials, and health insurance. The discussion then shifts to the impact of AI on healthcare, followed by an examination of integrated AI-blockchain platforms and their benefits. Technical challenges, limitations, and solutions related to these technologies are scrutinized. The paper addresses regulatory compliance and ethical considerations, and proposes future directions for their implementation. It concludes with research and implementation guidelines, offering a roadmap for harnessing blockchain and AI to enhance healthcare outcomes.
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Affiliation(s)
- Hossein Omidian
- Barry & Judy Silverman College of Pharmacy, Nova Southeastern University, Fort Lauderdale, FL 33328, USA.
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Sun P, Qian L, Wang Z. Preliminary experiments on interpretable ChatGPT-assisted diagnosis for breast ultrasound radiologists. Quant Imaging Med Surg 2024; 14:6601-6612. [PMID: 39281130 PMCID: PMC11400651 DOI: 10.21037/qims-24-141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 07/31/2024] [Indexed: 09/18/2024]
Abstract
Background Ultrasound is essential for detecting breast lesions. The American College of Radiology's Breast Imaging Reporting and Data System (BI-RADS) classification system is widely used, but its subjectivity can lead to inconsistency in diagnostic outcomes. Artificial intelligence (AI) models, such as ChatGPT-3.5, may potentially enhance diagnostic accuracy and efficiency in medical settings. This study aimed to assess the utility of the ChatGPT-3.5 model in generating BI-RADS classifications for breast ultrasound reports and its ability to replicate the "chain of thought" (CoT) in clinical decision-making to improve model interpretability. Methods Breast ultrasound reports were collected, and ChatGPT-3.5 was used to generate diagnoses and treatment plans. We evaluated GPT-4's performance by comparing its generated reports to those from doctors with various levels of experience. We also conducted a Turing test and a consistency analysis. To enhance the interpretability of the model, we applied the CoT method to deconstruct the decision-making chain of the GPT model. Results A total of 131 patients were evaluated, with 57 doctors participating in the experiment. ChatGPT-3.5 showed promising performance in structure and organization (S&O), professional terminology and expression (PTE), treatment recommendations (TR), and clarity and comprehensibility (C&C). However, improvements are needed in BI-RADS classification, malignancy diagnosis (MD), likelihood of being written by a physician (LWBP), and ultrasound doctor artificial intelligence acceptance (UDAIA). Turing test results indicated that AI-generated reports convincingly resembled human-authored reports. Reproducibility experiments displayed consistent performance. Erroneous report analysis revealed issues related to incorrect diagnosis, inconsistencies, and overdiagnosis. The CoT investigation supports the potential of ChatGPT to replicate the clinical decision-making process and offers insights into AI interpretability. Conclusions The ChatGPT-3.5 model holds potential as a valuable tool for assisting in the efficient determination of BI-RADS classifications and enhancing diagnostic performance.
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Affiliation(s)
- Pengfei Sun
- Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Linxue Qian
- Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Zhixiang Wang
- Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, China
- Department of Medical Imaging, Beijing Friendship Hospital, Capital Medical University, Beijing, China
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Nair PP, Keskar M, Borghare PT, Methwani DA, Nasre Y, Chaudhary M. Artificial Intelligence in Dry Eye Disease: A Narrative Review. Cureus 2024; 16:e70056. [PMID: 39449873 PMCID: PMC11499626 DOI: 10.7759/cureus.70056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2024] [Accepted: 09/23/2024] [Indexed: 10/26/2024] Open
Abstract
Dry eye disease (DED) is a multifactorial condition affecting millions worldwide, characterized by discomfort, visual disturbance, and potential damage to the ocular surface. The complexity of its diagnosis and management, driven by the diversity of symptoms and underlying causes, presents significant challenges to clinicians. Artificial intelligence (AI) has emerged as a transformative tool in healthcare, offering potential solutions to these challenges through its data analysis, pattern recognition, and predictive modeling capabilities. This narrative review explores the role of AI in diagnosing, treating, and managing dry eye disease. AI-driven tools such as machine learning algorithms, imaging technologies, and diagnostic platforms are examined for their ability to enhance diagnostic accuracy, personalize treatment approaches, and optimize patient outcomes. Furthermore, the review addresses the limitations of AI technologies in ophthalmology, including the need for robust clinical validation, data privacy concerns, and the ethical considerations of integrating AI into clinical practice. The findings suggest that while AI holds promise for improving the care of patients with DED, ongoing research and development are crucial to realizing its full potential.
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Affiliation(s)
- Praveena P Nair
- Ophthalmology, Mandsaur Institute of Ayurved Education and Research, Bhunyakhedi, IND
- Ophthalmology, Parul institute of Ayurved, Parul University, Limda, IND
| | - Manjiri Keskar
- Ophthalmology, Parul institute of Ayurved, Parul University, Limda, IND
| | - Pramod T Borghare
- Otolaryngology, Mahatma Gandhi Ayurved College Hospital and Research, Wardha, IND
| | - Disha A Methwani
- Otolaryngology, NKP Salve Institute Of Medical Sciences & Research Centre And Lata Mangeshkar Hospital, Nagpur, IND
| | | | - Minakshi Chaudhary
- Nursing, Shalinitai Meghe College of Nursing, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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Garcia-Moreno FM, Ruiz-Espigares J, Gutiérrez-Naranjo MA, Marchal JA. Using deep learning for predicting the dynamic evolution of breast cancer migration. Comput Biol Med 2024; 180:108890. [PMID: 39068903 DOI: 10.1016/j.compbiomed.2024.108890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 07/11/2024] [Accepted: 07/12/2024] [Indexed: 07/30/2024]
Abstract
BACKGROUND Breast cancer (BC) remains a prevalent health concern, with metastasis as the main driver of mortality. A detailed understanding of metastatic processes, particularly cell migration, is fundamental to improve therapeutic strategies. The wound healing assay, a traditional two-dimensional (2D) model, offers insights into cell migration but presents scalability issues due to data scarcity, arising from its manual and labor-intensive nature. METHOD To overcome these limitations, this study introduces the Prediction Wound Progression Framework (PWPF), an innovative approach utilizing Deep Learning (DL) and artificial data generation. The PWPF comprises a DL model initially trained on artificial data that simulates wound healing in MCF-7 BC cell monolayers and spheres, which is subsequently fine-tuned on real-world data. RESULTS Our results underscore the model's effectiveness in analyzing and predicting cell migration dynamics within the wound healing context, thus enhancing the usability of 2D models. The PWPF significantly contributes to a better understanding of cell migration processes in BC and expands the possibilities for research into wound healing mechanisms. CONCLUSIONS These advancements in automated cell migration analysis hold the potential for more comprehensive and scalable studies in the future. Our dataset, models, and code are publicly available at https://github.com/frangam/wound-healing.
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Affiliation(s)
- Francisco M Garcia-Moreno
- Department of Software Engineering, Computer Science School, University of Granada, C/ Periodista Daniel Saucedo Aranda, s/n, Granada, 18014, Spain; Research Centre for Information and Communication Technologies (CITIC-UGR), University of Granada, Granada, Spain.
| | - Jesús Ruiz-Espigares
- Biopathology and Regenerative Medicine Institute (IBIMER), Centre for Biomedical Research (CIBM), University of Granada, Granada, E-18016, Spain; Excellence Research Unit "Modeling Nature" (MNat), University of Granada, Granada, 18016, Spain; Department of Human Anatomy and Embryology, Faculty of Medicine, University of Granada, Granada, E-18016, Spain; Biosanitary Research Institute of Granada (ibs.GRANADA), University Hospitals of Granada-University of Granada, Granada, E-18071, Spain
| | - Miguel A Gutiérrez-Naranjo
- Department of Computer Sciences and Artificial Intelligence, University of Sevilla, Avda. Reina Mercedes, s/n, Sevilla, 41012, Spain
| | - Juan Antonio Marchal
- Biopathology and Regenerative Medicine Institute (IBIMER), Centre for Biomedical Research (CIBM), University of Granada, Granada, E-18016, Spain; Excellence Research Unit "Modeling Nature" (MNat), University of Granada, Granada, 18016, Spain; Department of Human Anatomy and Embryology, Faculty of Medicine, University of Granada, Granada, E-18016, Spain; Biosanitary Research Institute of Granada (ibs.GRANADA), University Hospitals of Granada-University of Granada, Granada, E-18071, Spain
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Zhong X. AI-assisted assessment and treatment of aphasia: a review. Front Public Health 2024; 12:1401240. [PMID: 39281082 PMCID: PMC11394183 DOI: 10.3389/fpubh.2024.1401240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 08/19/2024] [Indexed: 09/18/2024] Open
Abstract
Aphasia is a language disorder caused by brain injury that often results in difficulties with speech production and comprehension, significantly impacting the affected individuals' lives. Recently, artificial intelligence (AI) has been advancing in medical research. Utilizing machine learning and related technologies, AI develops sophisticated algorithms and predictive models, and can employ tools such as speech recognition and natural language processing to autonomously identify and analyze language deficits in individuals with aphasia. These advancements provide new insights and methods for assessing and treating aphasia. This article explores current AI-supported assessment and treatment approaches for aphasia and highlights key application areas. It aims to uncover how AI can enhance the process of assessment, tailor therapeutic interventions, and track the progress and outcomes of rehabilitation efforts. The article also addresses the current limitations of AI's application in aphasia and discusses prospects for future research.
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Affiliation(s)
- Xiaoyun Zhong
- School of Humanities and Foreign Languages, Qingdao University of Technology, Qingdao, China
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