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Jones CH, Madhavan S, Natarajan K, Corbo M, True JM, Dolsten M. Rewriting the textbook for pharma: how to adapt and thrive in a digital, personalized and collaborative world. Drug Discov Today 2024; 29:104112. [PMID: 39053620 DOI: 10.1016/j.drudis.2024.104112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Revised: 07/01/2024] [Accepted: 07/18/2024] [Indexed: 07/27/2024]
Abstract
The pharmaceutical industry is undergoing a sweeping transformation, driven by technological innovations, demographic shifts, regulatory changes and consumer expectations. For adaptive players in pharma to excel in this rapidly changing landscape, which will be markedly different from today by 2030 and beyond, they will require a different set of skills, capabilities and mindsets, as well as a willingness to collaborate and co-create value with multiple stakeholders. The industry needs to rewrite the textbook for pharma by embracing and implementing four key dimensions of change: digitalization, personalization, collaboration and innovation. In this article, we will examine how these dimensions of change are reshaping the industry, and provide practical and strategic guidance based on best practices and examples. Specifically, adaptive pharma companies should embrace the use of advanced digital technologies, such as artificial intelligence and machine learning, to streamline processes and solve challenges rapidly. Personalization, both in medicine and patient engagement, will also be key to success in the 'digital revolution', and a collaborative approach involving partnerships with tech start-ups, health-care providers and regulatory bodies will also be essential to create an integrated and responsive health-care ecosystem. Using these ideas for a rewritten textbook for pharma, adaptive players in pharma will evolve to be personalized and digitized health-focused organizations that provide comprehensive solutions which go beyond drugs and devices.
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Affiliation(s)
| | | | | | - Michael Corbo
- Pfizer, 66 Hudson Boulevard, New York, NY 10018, USA
| | - Jane M True
- Pfizer, 66 Hudson Boulevard, New York, NY 10018, USA
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Sheerah HA, AlSalamah S, Alsalamah SA, Lu CT, Arafa A, Zaatari E, Alhomod A, Pujari S, Labrique A. The Rise of Virtual Health Care: Transforming the Health Care Landscape in the Kingdom of Saudi Arabia: A Review Article. Telemed J E Health 2024. [PMID: 38984415 DOI: 10.1089/tmj.2024.0114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/11/2024] Open
Abstract
BACKGROUND The rise of virtual healthcare underscores the transformative influence of digital technologies in reshaping the healthcare landscape. As technology advances and the global demand for accessible and convenient healthcare services escalates, the virtual healthcare sector is gaining unprecedented momentum. Saudi Arabia, with its ambitious Vision 2030 initiative, is actively embracing digital innovation in the healthcare sector. METHODS In this narrative review, we discussed the key drivers and prospects of virtual healthcare in Saudi Arabia, highlighting its potential to enhance healthcare accessibility, quality, and patient outcomes. We also summarized the role of the COVID-19 pandemic in the digital transformation of healthcare in the country. Healthcare services provided by Seha Virtual Hospital in Saudi Arabia, the world's largest and Middle East's first virtual hospital, were also described. Finally, we proposed a roadmap for the future development of virtual health in the country. RESULTS AND CONCLUSIONS The integration of virtual healthcare into the existing healthcare system can enhance patient experiences, improve outcomes, and contribute to the overall well-being of the population. However, careful planning, collaboration, and investment are essential to overcome the challenges and ensure the successful implementation and sustainability of virtual healthcare in the country.
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Affiliation(s)
- Haytham A Sheerah
- Ministry of Health, Office of the Vice Minister of Health, Riyadh, Saudi Arabia
| | - Shada AlSalamah
- Information Systems Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
- Department of Digital Health and Innovation, Science Division, World Health Organization, Geneva, Switzerland
| | - Sara A Alsalamah
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
- Department of Computer Science, Virginia Tech, Blacksburg, Virginia, USA
| | - Chang-Tien Lu
- Department of Computer Science, Virginia Tech, Blacksburg, Virginia, USA
| | - Ahmed Arafa
- Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, Suita, Japan
- Department of Public Health and Community Medicine, Faculty of Medicine, Beni-Suef University, Beni-Suef, Egypt
| | - Ezzedine Zaatari
- Ministry of Health, Office of the Vice Minister of Health, Riyadh, Saudi Arabia
| | - Abdulaziz Alhomod
- Ministry of Health, SEHA Virtual Hospital, Riyadh, Saudi Arabia
- Emergency Medicine Administration, King Fahad Medical City, Riyadh, Saudi Arabia
| | - Sameer Pujari
- Department of Digital Health and Innovation, Science Division, World Health Organization, Geneva, Switzerland
| | - Alain Labrique
- Department of International Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland,United States
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3
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Kumar S, Mohan A, Sharma NR, Kumar A, Girdhar M, Malik T, Verma AK. Computational Frontiers in Aptamer-Based Nanomedicine for Precision Therapeutics: A Comprehensive Review. ACS OMEGA 2024; 9:26838-26862. [PMID: 38947800 PMCID: PMC11209897 DOI: 10.1021/acsomega.4c02466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 05/09/2024] [Accepted: 05/28/2024] [Indexed: 07/02/2024]
Abstract
In the rapidly evolving landscape of nanomedicine, aptamers have emerged as powerful molecular tools, demonstrating immense potential in targeted therapeutics, diagnostics, and drug delivery systems. This paper explores the computational features of aptamers in nanomedicine, highlighting their advantages over antibodies, including selectivity, low immunogenicity, and a simple production process. A comprehensive overview of the aptamer development process, specifically the Systematic Evolution of Ligands by Exponential Enrichment (SELEX) process, sheds light on the intricate methodologies behind aptamer selection. The historical evolution of aptamers and their diverse applications in nanomedicine are discussed, emphasizing their pivotal role in targeted drug delivery, precision medicine and therapeutics. Furthermore, we explore the integration of artificial intelligence (AI), machine learning (ML), Internet of Things (IoT), Internet of Medical Things (IoMT), and nanotechnology in aptameric development, illustrating how these cutting-edge technologies are revolutionizing the selection and optimization of aptamers for tailored biomedical applications. This paper also discusses challenges in computational methods for advancing aptamers, including reliable prediction models, extensive data analysis, and multiomics data incorporation. It also addresses ethical concerns and restrictions related to AI and IoT use in aptamer research. The paper examines progress in computer simulations for nanomedicine. By elucidating the importance of aptamers, understanding their superiority over antibodies, and exploring the historical context and challenges, this review serves as a valuable resource for researchers and practitioners aiming to harness the full potential of aptamers in the rapidly evolving field of nanomedicine.
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Affiliation(s)
- Shubham Kumar
- School
of Bioengineering and Biosciences, Lovely
Professional University, Phagwara, Punjab 144001, India
| | - Anand Mohan
- School
of Bioengineering and Biosciences, Lovely
Professional University, Phagwara, Punjab 144001, India
| | - Neeta Raj Sharma
- School
of Bioengineering and Biosciences, Lovely
Professional University, Phagwara, Punjab 144001, India
| | - Anil Kumar
- Gene
Regulation Laboratory, National Institute
of Immunology, Aruna Asaf Ali Marg, New Delhi 110067, India
| | - Madhuri Girdhar
- Division
of Research and Development, Lovely Professional
University, Phagwara 144401, Punjab, India
| | - Tabarak Malik
- Department
of Biomedical Sciences, Institute of Health, Jimma University, MVJ4+R95 Jimma, Ethiopia
| | - Awadhesh Kumar Verma
- School
of Bioengineering and Biosciences, Lovely
Professional University, Phagwara, Punjab 144001, India
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4
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Li Y, Jie C, Wang J, Zhang W, Wang J, Deng Y, Liu Z, Hou X, Bi X. Global research trends and future directions in diabetic macular edema research: A bibliometric and visualized analysis. Medicine (Baltimore) 2024; 103:e38596. [PMID: 38905408 PMCID: PMC11191902 DOI: 10.1097/md.0000000000038596] [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: 04/06/2024] [Accepted: 05/24/2024] [Indexed: 06/23/2024] Open
Abstract
BACKGROUND Diabetic Macular Edema (DME) significantly impairs vision in diabetics, with varied patient responses to current treatments like anti-vascular endothelial growth factor (VEGF) therapy underscoring the necessity for continued research into more effective strategies. This study aims to evaluate global research trends and identify emerging frontiers in DME to guide future research and clinical management. METHODS A qualitative and quantitative analysis of publications related to diabetic macular edema retrieved from the Web of Science Core Collection (WoSCC) between its inception and September 4, 2023, was conducted. Microsoft Excel, CiteSpace, VOSviewer, Bibliometrix Package, and Tableau were used for the bibliometric analysis and visualization. This encompasses an examination of the overall distribution of annual output, major countries, regions, institutions, authors, core journals, co-cited references, and keyword analyses. RESULTS Overall, 5624 publications were analyzed, indicating an increasing trend in DME research. The United States was identified as the leading country in DME research, with the highest h-index of 135 and 91,841 citations. Francesco Bandello emerged as the most prolific author with 97 publications. Neil M. Bressler has the highest h-index and highest total citation count of 46 and 9692, respectively. The journals "Retina - the Journal of Retinal and Vitreous Diseases" and "Ophthalmology" were highlighted as the most prominent in this field. "Retina" leads with 354 publications, a citation count of 11,872, and an h-index of 59. Meanwhile, "Ophthalmology" stands out with the highest overall citation count of 31,558 and the highest h-index of 90. The primary research focal points in diabetic macular edema included "prevalence and risk factors," "pathological mechanisms," "imaging modalities," "treatment strategies," and "clinical trials." Emerging research areas encompassed "deep learning and artificial intelligence," "novel treatment modalities," and "biomarkers." CONCLUSION Our bibliometric analysis delineates the leading role of the United States in DME research. We identified current research hotspots, including epidemiological studies, pathophysiological mechanisms, imaging advancements, and treatment innovations. Emerging trends, such as the integration of artificial intelligence and novel therapeutic approaches, highlight future directions. These insights underscore the importance of collaborative and interdisciplinary approaches in advancing DME research and clinical management.
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Affiliation(s)
- Yuanyuan Li
- Eye Hospital China Academy of Chinese Medical Sciences, Beijing, China
| | - Chuanhong Jie
- Eye Hospital China Academy of Chinese Medical Sciences, Beijing, China
| | - Jianwei Wang
- Eye Hospital China Academy of Chinese Medical Sciences, Beijing, China
| | - Weiqiong Zhang
- Eye Hospital China Academy of Chinese Medical Sciences, Beijing, China
| | - Jingying Wang
- Eye Hospital China Academy of Chinese Medical Sciences, Beijing, China
| | - Yu Deng
- Eye Hospital China Academy of Chinese Medical Sciences, Beijing, China
| | - Ziqiang Liu
- Eye Hospital China Academy of Chinese Medical Sciences, Beijing, China
| | - Xiaoyu Hou
- Eye Hospital China Academy of Chinese Medical Sciences, Beijing, China
| | - Xuqi Bi
- Eye Hospital China Academy of Chinese Medical Sciences, Beijing, China
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Juyal A, Bisht S, Singh MF. Smart solutions in hypertension diagnosis and management: a deep dive into artificial intelligence and modern wearables for blood pressure monitoring. Blood Press Monit 2024:00126097-990000000-00112. [PMID: 38958493 DOI: 10.1097/mbp.0000000000000711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/04/2024]
Abstract
Hypertension, a widespread cardiovascular issue, presents a major global health challenge. Traditional diagnosis and treatment methods involve periodic blood pressure monitoring and prescribing antihypertensive drugs. Smart technology integration in healthcare offers promising results in optimizing the diagnosis and treatment of various conditions. We investigate its role in improving hypertension diagnosis and treatment effectiveness using machine learning algorithms for early and accurate detection. Intelligent models trained on diverse datasets (encompassing physiological parameters, lifestyle factors, and genetic information) to detect subtle hypertension risk patterns. Adaptive algorithms analyze patient-specific data, optimizing treatment plans based on medication responses and lifestyle habits. This personalized approach ensures effective, minimally invasive interventions tailored to each patient. Wearables and smart sensors provide real-time health insights for proactive treatment adjustments and early complication detection.
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Affiliation(s)
- Anubhuti Juyal
- Department of Pharmacology, Amity Institute of Pharmacy, Amity University, Lucknow, Uttar Pradesh
| | - Shradha Bisht
- Department of Pharmacology, Amity Institute of Pharmacy, Amity University, Lucknow, Uttar Pradesh
| | - Mamta F Singh
- Department of Pharmacology, College of Pharmacy, COER University, Roorkee, Uttarakhand, India
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Zabihiyeganeh M, Mirzaei A, Tabrizian P, Rezaee A, Sheikhtaheri A, Kadijani AA, Kadijani BA, Sharifi Kia A. Prediction of subsequent fragility fractures: application of machine learning. BMC Musculoskelet Disord 2024; 25:438. [PMID: 38834975 DOI: 10.1186/s12891-024-07559-y] [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: 11/29/2023] [Accepted: 05/29/2024] [Indexed: 06/06/2024] Open
Abstract
BACKGROUND Machine learning (ML) has shown exceptional promise in various domains of medical research. However, its application in predicting subsequent fragility fractures is still largely unknown. In this study, we aim to evaluate the predictive power of different ML algorithms in this area and identify key features associated with the risk of subsequent fragility fractures in osteoporotic patients. METHODS We retrospectively analyzed data from patients presented with fragility fractures at our Fracture Liaison Service, categorizing them into index fragility fracture (n = 905) and subsequent fragility fracture groups (n = 195). We independently trained ML models using 27 features for both male and female cohorts. The algorithms tested include Random Forest, XGBoost, CatBoost, Logistic Regression, LightGBM, AdaBoost, Multi-Layer Perceptron, and Support Vector Machine. Model performance was evaluated through 10-fold cross-validation. RESULTS The CatBoost model outperformed other models, achieving 87% accuracy and an AUC of 0.951 for females, and 93.4% accuracy with an AUC of 0.990 for males. The most significant predictors for females included age, serum C-reactive protein (CRP), 25(OH)D, creatinine, blood urea nitrogen (BUN), parathyroid hormone (PTH), femoral neck Z-score, menopause age, number of pregnancies, phosphorus, calcium, and body mass index (BMI); for males, the predictors were serum CRP, femoral neck T-score, PTH, hip T-score, BMI, BUN, creatinine, alkaline phosphatase, and spinal Z-score. CONCLUSION ML models, especially CatBoost, offer a valuable approach for predicting subsequent fragility fractures in osteoporotic patients. These models hold the potential to enhance clinical decision-making by supporting the development of personalized preventative strategies.
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Affiliation(s)
- Mozhdeh Zabihiyeganeh
- Bone and Joint Reconstruction Research Center, Department of Orthopedics, School of Medicine, University of Medical Sciences, Baharestan Sq, Tehran, Iran
| | - Alireza Mirzaei
- Bone and Joint Reconstruction Research Center, Department of Orthopedics, School of Medicine, University of Medical Sciences, Baharestan Sq, Tehran, Iran
- Department of Orthopaedic Surgery, University of Minnesota, Minneapolis, MN, USA
| | - Pouria Tabrizian
- Bone and Joint Reconstruction Research Center, Department of Orthopedics, School of Medicine, University of Medical Sciences, Baharestan Sq, Tehran, Iran
| | - Aryan Rezaee
- Bone and Joint Reconstruction Research Center, Department of Orthopedics, School of Medicine, University of Medical Sciences, Baharestan Sq, Tehran, Iran
- Student Research Committee, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Abbas Sheikhtaheri
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Azade Amini Kadijani
- Bone and Joint Reconstruction Research Center, Department of Orthopedics, School of Medicine, University of Medical Sciences, Baharestan Sq, Tehran, Iran
| | - Bahare Amini Kadijani
- Department of Medical Physics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Ali Sharifi Kia
- Bone and Joint Reconstruction Research Center, Department of Orthopedics, School of Medicine, University of Medical Sciences, Baharestan Sq, Tehran, Iran.
- Department of Computer Science, Faculty of Science, Western University, London, ON, Canada.
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7
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Choubey A, Choubey SB, K P, Daulatabad VS, John N. Healthcare Transformation: Artificial Intelligence Is the Dire Imperative of the Day. Cureus 2024; 16:e62652. [PMID: 39036139 PMCID: PMC11258957 DOI: 10.7759/cureus.62652] [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] [Accepted: 06/18/2024] [Indexed: 07/23/2024] Open
Abstract
At present, healthcare systems around the world are confronted with unprecedented challenges caused by aging demographics, increasing chronic diseases, and resource challenges. In this scenario, artificial intelligence (AI) emerges as a disruptive technology that can provide solutions to these complicated problems. This review article outlines the vital role played by AI in altering the health landscape. The constant demand for effective and accessible healthcare demands the use of new solutions. AI can be described as an important imperative, enabling advancements in many areas of the delivery of healthcare. This review article explores the possibilities of use of AI to aid in the field of healthcare assistants, diagnosing, disease prediction, and personalized treatment and the discovery of drugs, telemedicine and remote monitoring of patients, robotic-assisted procedures imaging for pathology and radiology analysis, and the analysis of genomic data. By analyzing the existing research and cases, we explain how AI-driven technology can optimize processes in healthcare, improve diagnosis accuracy, improve the quality of treatment, and simplify administrative tasks. By highlighting the most successful AI applications and laying out possible future developments, the review article will provide insight for healthcare professionals, policymakers, researchers, and other stakeholders in harnessing the power of AI to transform healthcare delivery and enhance the quality of care for patients.
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Affiliation(s)
- Abhishek Choubey
- Electronic Communication, Sreenidhi Institute of Science & Technology, Hyderabad, IND
| | | | - Prafull K
- Physiology, All India Institute of Medical Sciences, Bibinagar, Hyderabad, IND
| | | | - Nitin John
- Physiology, All India Institute of Medical Sciences, Bibinagar, Hyderabad, IND
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8
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Alhuwaydi AM. Exploring the Role of Artificial Intelligence in Mental Healthcare: Current Trends and Future Directions - A Narrative Review for a Comprehensive Insight. Risk Manag Healthc Policy 2024; 17:1339-1348. [PMID: 38799612 PMCID: PMC11127648 DOI: 10.2147/rmhp.s461562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 05/10/2024] [Indexed: 05/29/2024] Open
Abstract
Mental health is an essential component of the health and well-being of a person and community, and it is critical for the individual, society, and socio-economic development of any country. Mental healthcare is currently in the health sector transformation era, with emerging technologies such as artificial intelligence (AI) reshaping the screening, diagnosis, and treatment modalities of psychiatric illnesses. The present narrative review is aimed at discussing the current landscape and the role of AI in mental healthcare, including screening, diagnosis, and treatment. Furthermore, this review attempted to highlight the key challenges, limitations, and prospects of AI in providing mental healthcare based on existing works of literature. The literature search for this narrative review was obtained from PubMed, Saudi Digital Library (SDL), Google Scholar, Web of Science, and IEEE Xplore, and we included only English-language articles published in the last five years. Keywords used in combination with Boolean operators ("AND" and "OR") were the following: "Artificial intelligence", "Machine learning", Deep learning", "Early diagnosis", "Treatment", "interventions", "ethical consideration", and "mental Healthcare". Our literature review revealed that, equipped with predictive analytics capabilities, AI can improve treatment planning by predicting an individual's response to various interventions. Predictive analytics, which uses historical data to formulate preventative interventions, aligns with the move toward individualized and preventive mental healthcare. In the screening and diagnostic domains, a subset of AI, such as machine learning and deep learning, has been proven to analyze various mental health data sets and predict the patterns associated with various mental health problems. However, limited studies have evaluated the collaboration between healthcare professionals and AI in delivering mental healthcare, as these sensitive problems require empathy, human connections, and holistic, personalized, and multidisciplinary approaches. Ethical issues, cybersecurity, a lack of data analytics diversity, cultural sensitivity, and language barriers remain concerns for implementing this futuristic approach in mental healthcare. Considering these sensitive problems require empathy, human connections, and holistic, personalized, and multidisciplinary approaches, it is imperative to explore these aspects. Therefore, future comparative trials with larger sample sizes and data sets are warranted to evaluate different AI models used in mental healthcare across regions to fill the existing knowledge gaps.
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Affiliation(s)
- Ahmed M Alhuwaydi
- Department of Internal Medicine, Division of Psychiatry, College of Medicine, Jouf University, Sakaka, Saudi Arabia
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9
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Savchenko E, Bunimovich-Mendrazitsky S. Investigation toward the economic feasibility of personalized medicine for healthcare service providers: the case of bladder cancer. Front Med (Lausanne) 2024; 11:1388685. [PMID: 38808135 PMCID: PMC11130437 DOI: 10.3389/fmed.2024.1388685] [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: 02/20/2024] [Accepted: 04/26/2024] [Indexed: 05/30/2024] Open
Abstract
In today's complex healthcare landscape, the pursuit of delivering optimal patient care while navigating intricate economic dynamics poses a significant challenge for healthcare service providers (HSPs). In this already complex dynamic, the emergence of clinically promising personalized medicine-based treatment aims to revolutionize medicine. While personalized medicine holds tremendous potential for enhancing therapeutic outcomes, its integration within resource-constrained HSPs presents formidable challenges. In this study, we investigate the economic feasibility of implementing personalized medicine. The central objective is to strike a balance between catering to individual patient needs and making economically viable decisions. Unlike conventional binary approaches to personalized treatment, we propose a more nuanced perspective by treating personalization as a spectrum. This approach allows for greater flexibility in decision-making and resource allocation. To this end, we propose a mathematical framework to investigate our proposal, focusing on Bladder Cancer (BC) as a case study. Our results show that while it is feasible to introduce personalized medicine, a highly efficient but highly expensive one would be short-lived relative to its less effective but cheaper alternative as the latter can be provided to a larger cohort of patients, optimizing the HSP's objective better.
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Moulson R, Feugère G, Moreira-Lucas TS, Dequen F, Weiss J, Smith J, Brezden-Masley C. Real-World Treatment Patterns and Clinical Outcomes among Patients Receiving CDK4/6 Inhibitors for Metastatic Breast Cancer in a Canadian Setting Using AI-Extracted Data. Curr Oncol 2024; 31:2172-2184. [PMID: 38668064 PMCID: PMC11049664 DOI: 10.3390/curroncol31040161] [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: 02/20/2024] [Revised: 03/20/2024] [Accepted: 04/07/2024] [Indexed: 04/28/2024] Open
Abstract
Cyclin-dependent kinase 4/6 inhibitors (CDK4/6i) are widely used in patients with hormone receptor-positive (HR+)/human epidermal growth factor receptor 2 negative (HER2-) advanced/metastatic breast cancer (ABC/MBC) in first line (1L), but little is known about their real-world use and clinical outcomes long-term, in Canada. This study used Pentavere's previously validated artificial intelligence (AI) to extract real-world data on the treatment patterns and outcomes of patients receiving CDK4/6i+endocrine therapy (ET) for HR+/HER2- ABC/MBC at Sinai Health in Toronto, Canada. Between 1 January 2016 and 1 July 2021, 48 patients were diagnosed with HR+/HER2- ABC/MBC and received CDK4/6i + ET. A total of 38 out of 48 patients received CDK4/6i + ET in 1L, of which 34 of the 38 (89.5%) received palbociclib + ET. In 2L, 12 of the 21 (57.1%) patients received CDK4/6i + ET, of which 58.3% received abemaciclib. In 3L, most patients received chemotherapy (10/12, 83.3%). For the patients receiving CDK4/6i in 1L, the median (95% CI) time to the next treatment was 42.3 (41.2, NA) months. The median (95% CI) time to chemotherapy was 46.5 (41.4, NA) months. The two-year overall survival (95% CI) was 97.4% (92.4, 100.0), and the median (range) follow-up was 28.7 (3.4-67.6) months. Despite the limitations inherent in real-world studies and a limited number of patients, these AI-extracted data complement previous studies, demonstrating the effectiveness of CDK4/6i + ET in the Canadian real-world 1L, with most patients receiving palbociclib as CDK4/6i in 1L.
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Affiliation(s)
| | | | | | | | | | - Janet Smith
- Mount Sinai Hospital, Toronto, ON M5G 1X5, Canada (C.B.-M.)
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11
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Zhang B, Liu H, Wu F, Ding Y, Wu J, Lu L, Bajpai AK, Sang M, Wang X. Identification of hub genes and potential molecular mechanisms related to drug sensitivity in acute myeloid leukemia based on machine learning. Front Pharmacol 2024; 15:1359832. [PMID: 38650628 PMCID: PMC11033397 DOI: 10.3389/fphar.2024.1359832] [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: 12/22/2023] [Accepted: 03/21/2024] [Indexed: 04/25/2024] Open
Abstract
Background: Acute myeloid leukemia (AML) is the most common form of leukemia among adults and is characterized by uncontrolled proliferation and clonal expansion of hematopoietic cells. There has been a significant improvement in the treatment of younger patients, however, prognosis in the elderly AML patients remains poor. Methods: We used computational methods and machine learning (ML) techniques to identify and explore the differential high-risk genes (DHRGs) in AML. The DHRGs were explored through multiple in silico approaches including genomic and functional analysis, survival analysis, immune infiltration, miRNA co-expression and stemness features analyses to reveal their prognostic importance in AML. Furthermore, using different ML algorithms, prognostic models were constructed and validated using the DHRGs. At the end molecular docking studies were performed to identify potential drug candidates targeting the selected DHRGs. Results: We identified a total of 80 DHRGs by comparing the differentially expressed genes derived between AML patients and normal controls and high-risk AML genes identified by Cox regression. Genetic and epigenetic alteration analyses of the DHRGs revealed a significant association of their copy number variations and methylation status with overall survival (OS) of AML patients. Out of the 137 models constructed using different ML algorithms, the combination of Ridge and plsRcox maintained the highest mean C-index and was used to build the final model. When AML patients were classified into low- and high-risk groups based on DHRGs, the low-risk group had significantly longer OS in the AML training and validation cohorts. Furthermore, immune infiltration, miRNA coexpression, stemness feature and hallmark pathway analyses revealed significant differences in the prognosis of the low- and high-risk AML groups. Drug sensitivity and molecular docking studies revealed top 5 drugs, including carboplatin and austocystin-D that may significantly affect the DHRGs in AML. Conclusion: The findings from the current study identified a set of high-risk genes that may be used as prognostic and therapeutic markers for AML patients. In addition, significant use of the ML algorithms in constructing and validating the prognostic models in AML was demonstrated. Although our study used extensive bioinformatics and machine learning methods to identify the hub genes in AML, their experimental validations using knock-out/-in methods would strengthen our findings.
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Affiliation(s)
- Boyu Zhang
- Department of Hematology, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, Jiangsu, China
| | - Haiyan Liu
- Department of Hematology, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, Jiangsu, China
| | - Fengxia Wu
- Department of Hematology, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, Jiangsu, China
| | - Yuhong Ding
- Department of Hematology, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, Jiangsu, China
| | - Jiarun Wu
- Department of Hematology, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, Jiangsu, China
| | - Lu Lu
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, TN, United States
| | - Akhilesh K. Bajpai
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, TN, United States
| | - Mengmeng Sang
- Department of Hematology, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, Jiangsu, China
| | - Xinfeng Wang
- Department of Hematology, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, Jiangsu, China
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12
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Giansanti D. Joint Expedition: Exploring the Intersection of Digital Health and AI in Precision Medicine with Team Integration. J Pers Med 2024; 14:388. [PMID: 38673015 PMCID: PMC11051462 DOI: 10.3390/jpm14040388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2024] [Accepted: 04/02/2024] [Indexed: 04/28/2024] Open
Abstract
Precision medicine stands as a transformative force in the orbit of healthcare, fundamentally reshaping traditional approaches by customizing therapeutic interventions to align with the distinctive attributes of individual patients [...].
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Affiliation(s)
- Daniele Giansanti
- Centro Nazionale Tecnologie Innovative in Sanità Pubblica, Istituto Superiore di Sanità, Via Regina Elena 299, 00161 Roma, Italy
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13
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Khalighi S, Reddy K, Midya A, Pandav KB, Madabhushi A, Abedalthagafi M. Artificial intelligence in neuro-oncology: advances and challenges in brain tumor diagnosis, prognosis, and precision treatment. NPJ Precis Oncol 2024; 8:80. [PMID: 38553633 PMCID: PMC10980741 DOI: 10.1038/s41698-024-00575-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 03/13/2024] [Indexed: 04/02/2024] Open
Abstract
This review delves into the most recent advancements in applying artificial intelligence (AI) within neuro-oncology, specifically emphasizing work on gliomas, a class of brain tumors that represent a significant global health issue. AI has brought transformative innovations to brain tumor management, utilizing imaging, histopathological, and genomic tools for efficient detection, categorization, outcome prediction, and treatment planning. Assessing its influence across all facets of malignant brain tumor management- diagnosis, prognosis, and therapy- AI models outperform human evaluations in terms of accuracy and specificity. Their ability to discern molecular aspects from imaging may reduce reliance on invasive diagnostics and may accelerate the time to molecular diagnoses. The review covers AI techniques, from classical machine learning to deep learning, highlighting current applications and challenges. Promising directions for future research include multimodal data integration, generative AI, large medical language models, precise tumor delineation and characterization, and addressing racial and gender disparities. Adaptive personalized treatment strategies are also emphasized for optimizing clinical outcomes. Ethical, legal, and social implications are discussed, advocating for transparency and fairness in AI integration for neuro-oncology and providing a holistic understanding of its transformative impact on patient care.
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Affiliation(s)
- Sirvan Khalighi
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Kartik Reddy
- Department of Radiology, Emory University, Atlanta, GA, USA
| | - Abhishek Midya
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Krunal Balvantbhai Pandav
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Anant Madabhushi
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
- Atlanta Veterans Administration Medical Center, Atlanta, GA, USA.
| | - Malak Abedalthagafi
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, GA, USA.
- The Cell and Molecular Biology Program, Winship Cancer Institute, Atlanta, GA, USA.
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14
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Young JA, Chang CW, Scales CW, Menon SV, Holy CE, Blackie CA. Machine Learning Methods Using Artificial Intelligence Deployed on Electronic Health Record Data for Identification and Referral of At-Risk Patients From Primary Care Physicians to Eye Care Specialists: Retrospective, Case-Controlled Study. JMIR AI 2024; 3:e48295. [PMID: 38875582 PMCID: PMC11041486 DOI: 10.2196/48295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 07/11/2023] [Accepted: 02/10/2024] [Indexed: 06/16/2024]
Abstract
BACKGROUND Identification and referral of at-risk patients from primary care practitioners (PCPs) to eye care professionals remain a challenge. Approximately 1.9 million Americans suffer from vision loss as a result of undiagnosed or untreated ophthalmic conditions. In ophthalmology, artificial intelligence (AI) is used to predict glaucoma progression, recognize diabetic retinopathy (DR), and classify ocular tumors; however, AI has not yet been used to triage primary care patients for ophthalmology referral. OBJECTIVE This study aimed to build and compare machine learning (ML) methods, applicable to electronic health records (EHRs) of PCPs, capable of triaging patients for referral to eye care specialists. METHODS Accessing the Optum deidentified EHR data set, 743,039 patients with 5 leading vision conditions (age-related macular degeneration [AMD], visually significant cataract, DR, glaucoma, or ocular surface disease [OSD]) were exact-matched on age and gender to 743,039 controls without eye conditions. Between 142 and 182 non-ophthalmic parameters per patient were input into 5 ML methods: generalized linear model, L1-regularized logistic regression, random forest, Extreme Gradient Boosting (XGBoost), and J48 decision tree. Model performance was compared for each pathology to select the most predictive algorithm. The area under the curve (AUC) was assessed for all algorithms for each outcome. RESULTS XGBoost demonstrated the best performance, showing, respectively, a prediction accuracy and an AUC of 78.6% (95% CI 78.3%-78.9%) and 0.878 for visually significant cataract, 77.4% (95% CI 76.7%-78.1%) and 0.858 for exudative AMD, 79.2% (95% CI 78.8%-79.6%) and 0.879 for nonexudative AMD, 72.2% (95% CI 69.9%-74.5%) and 0.803 for OSD requiring medication, 70.8% (95% CI 70.5%-71.1%) and 0.785 for glaucoma, 85.0% (95% CI 84.2%-85.8%) and 0.924 for type 1 nonproliferative diabetic retinopathy (NPDR), 82.2% (95% CI 80.4%-84.0%) and 0.911 for type 1 proliferative diabetic retinopathy (PDR), 81.3% (95% CI 81.0%-81.6%) and 0.891 for type 2 NPDR, and 82.1% (95% CI 81.3%-82.9%) and 0.900 for type 2 PDR. CONCLUSIONS The 5 ML methods deployed were able to successfully identify patients with elevated odds ratios (ORs), thus capable of patient triage, for ocular pathology ranging from 2.4 (95% CI 2.4-2.5) for glaucoma to 5.7 (95% CI 5.0-6.4) for type 1 NPDR, with an average OR of 3.9. The application of these models could enable PCPs to better identify and triage patients at risk for treatable ophthalmic pathology. Early identification of patients with unrecognized sight-threatening conditions may lead to earlier treatment and a reduced economic burden. More importantly, such triage may improve patients' lives.
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Affiliation(s)
- Joshua A Young
- Department of Ophthalmology, New York University School of Medicine, New York, NY, United States
| | - Chin-Wen Chang
- Data Science, Johnson & Johnson MedTech, Raritan, NJ, United States
| | - Charles W Scales
- Medical and Scientific Operations, Johnson & Johnson Medtech, Vision, Jacksonville, FL, United States
| | - Saurabh V Menon
- Mu Sigma Business Solutions Private Limited, Bangalore, India
| | - Chantal E Holy
- Epidemiology and Real-World Data Sciences, Johnson & Johnson MedTech, New Brunswick, NJ, United States
| | - Caroline Adrienne Blackie
- Medical and Scientific Operations, Johnson & Johnson MedTech, Vision, Jacksonville, FL, United States
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15
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Ng JY, Cramer H, Lee MS, Moher D. Traditional, complementary, and integrative medicine and artificial intelligence: Novel opportunities in healthcare. Integr Med Res 2024; 13:101024. [PMID: 38384497 PMCID: PMC10879672 DOI: 10.1016/j.imr.2024.101024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 02/02/2024] [Accepted: 02/07/2024] [Indexed: 02/23/2024] Open
Abstract
The convergence of traditional, complementary, and integrative medicine (TCIM) with artificial intelligence (AI) is a promising frontier in healthcare. TCIM is a patient-centric approach that combines conventional medicine with complementary therapies, emphasizing holistic well-being. AI can revolutionize healthcare through data-driven decision-making and personalized treatment plans. This article explores how AI technologies can complement and enhance TCIM, aligning with the shared objectives of researchers from both fields in improving patient outcomes, enhancing care quality, and promoting holistic wellness. This integration of TCIM and AI introduces exciting opportunities but also noteworthy challenges. AI may augment TCIM by assisting in early disease detection, providing personalized treatment plans, predicting health trends, and enhancing patient engagement. Challenges at the intersection of AI and TCIM include data privacy and security, regulatory complexities, maintaining the human touch in patient-provider relationships, and mitigating bias in AI algorithms. Patients' trust, informed consent, and legal accountability are all essential considerations. Future directions in AI-enhanced TCIM include advanced personalized medicine, understanding the efficacy of herbal remedies, and studying patient-provider interactions. Research on bias mitigation, patient acceptance, and trust in AI-driven TCIM healthcare is crucial. In this article, we outlined that the merging of TCIM and AI holds great promise in enhancing healthcare delivery, personalizing treatment plans, preventive care, and patient engagement. Addressing challenges and fostering collaboration between AI experts, TCIM practitioners, and policymakers, however, is vital to harnessing the full potential of this integration.
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Affiliation(s)
- Jeremy Y. Ng
- Centre for Journalology, Ottawa Hospital Research Institute, Ottawa, Canada
- Institute of General Practice and Interprofessional Care, University Hospital Tübingen, Tübingen, Germany
- Robert Bosch Center for Integrative Medicine and Health, Bosch Health Campus, Stuttgart, Germany
| | - Holger Cramer
- Institute of General Practice and Interprofessional Care, University Hospital Tübingen, Tübingen, Germany
- Robert Bosch Center for Integrative Medicine and Health, Bosch Health Campus, Stuttgart, Germany
| | - Myeong Soo Lee
- KM Science Research Division, Korea Institute of Oriental Medicine, Daejeon, South Korea
| | - David Moher
- Centre for Journalology, Ottawa Hospital Research Institute, Ottawa, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada
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16
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Alhudhaif A. A novel approach to recognition of Alzheimer's and Parkinson's diseases: random subspace ensemble classifier based on deep hybrid features with a super-resolution image. PeerJ Comput Sci 2024; 10:e1862. [PMID: 38435579 PMCID: PMC10909220 DOI: 10.7717/peerj-cs.1862] [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: 11/10/2023] [Accepted: 01/18/2024] [Indexed: 03/05/2024]
Abstract
Background Artificial intelligence technologies have great potential in classifying neurodegenerative diseases such as Alzheimer's and Parkinson's. These technologies can aid in early diagnosis, enhance classification accuracy, and improve patient access to appropriate treatments. For this purpose, we focused on AI-based auto-diagnosis of Alzheimer's disease, Parkinson's disease, and healthy MRI images. Methods In the current study, a deep hybrid network based on an ensemble classifier and convolutional neural network was designed. First, a very deep super-resolution neural network was adapted to improve the resolution of MRI images. Low and high-level features were extracted from the images processed with the hybrid deep convolutional neural network. Finally, these deep features are given as input to the k-nearest neighbor (KNN)-based random subspace ensemble classifier. Results A 3-class dataset containing publicly available MRI images was utilized to test the proposed architecture. In experimental works, the proposed model produced 99.11% accuracy, 98.75% sensitivity, 99.54% specificity, 98.65% precision, and 98.70% F1-score performance values. The results indicate that our AI system has the potential to provide valuable diagnostic assistance in clinical settings.
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Affiliation(s)
- Adi Alhudhaif
- Department of Computer Science, College of Computer Engineering and Sciences in Al-kharj, Prince Sattam Bin Abdulaziz University, Al-kharj, Saudi Arabia
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17
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Bhagat SV, Kanyal D. Navigating the Future: The Transformative Impact of Artificial Intelligence on Hospital Management- A Comprehensive Review. Cureus 2024; 16:e54518. [PMID: 38516434 PMCID: PMC10955674 DOI: 10.7759/cureus.54518] [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: 12/24/2023] [Accepted: 02/13/2024] [Indexed: 03/23/2024] Open
Abstract
This comprehensive review explores the transformative impact of artificial intelligence (AI) on hospital management, delving into its applications, challenges, and future trends. Integrating AI in administrative functions, clinical operations, and patient engagement holds significant promise for enhancing efficiency, optimizing resource allocation, and revolutionizing patient care. However, this evolution is accompanied by ethical, legal, and operational considerations that necessitate careful navigation. The review underscores key findings, emphasizing the implications for the future of hospital management. It calls for a proactive approach, urging stakeholders to invest in education, prioritize ethical guidelines, foster collaboration, advocate for thoughtful regulation, and embrace a culture of innovation. The healthcare industry can successfully navigate this transformative era through collective action, ensuring that AI contributes to more effective, accessible, and patient-centered healthcare delivery.
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Affiliation(s)
- Shefali V Bhagat
- Hospital Administration, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Deepika Kanyal
- Hospital Administration, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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18
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Mukherjee S, Vagha S, Gadkari P. Navigating the Future: A Comprehensive Review of Artificial Intelligence Applications in Gastrointestinal Cancer. Cureus 2024; 16:e54467. [PMID: 38510911 PMCID: PMC10953838 DOI: 10.7759/cureus.54467] [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: 02/05/2024] [Accepted: 02/18/2024] [Indexed: 03/22/2024] Open
Abstract
This comprehensive review explores the transformative role of artificial intelligence (AI) in the realm of gastrointestinal cancer. Gastrointestinal cancers present unique challenges, necessitating precise diagnostic tools and personalized treatment strategies. Leveraging AI, particularly machine learning and deep learning algorithms, has demonstrated remarkable potential in revolutionizing early detection, treatment planning, prognosis, and drug development. The analysis of current research and technological advancements underscores the capacity of AI to unravel intricate patterns within extensive datasets, providing actionable insights that enhance diagnostic accuracy and treatment efficacy. The transformative impact of AI on the landscape of gastrointestinal cancer is emphasized, signaling a paradigm shift towards more precise and targeted cancer care. The conclusion emphasizes the need for sustained research efforts and collaborative initiatives among AI researchers, healthcare professionals, and policymakers. By fostering interdisciplinary collaboration, we can navigate the evolving field of gastrointestinal cancer care, embracing the potential of AI to improve patient outcomes and contribute to a more effective and personalized approach to cancer management.
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Affiliation(s)
- Sreetama Mukherjee
- Pathology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Sunita Vagha
- Pathology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Pravin Gadkari
- Pathology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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19
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Tsai AY, Carter SR, Greene AC. Artificial intelligence in pediatric surgery. Semin Pediatr Surg 2024; 33:151390. [PMID: 38242061 DOI: 10.1016/j.sempedsurg.2024.151390] [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: 01/21/2024]
Abstract
Artificial intelligence (AI) is rapidly changing the landscape of medicine and is already being utilized in conjunction with medical diagnostics and imaging analysis. We hereby explore AI applications in surgery and examine its relevance to pediatric surgery, covering its evolution, current state, and promising future. The various fields of AI are explored including machine learning and applications to predictive analytics and decision support in surgery, computer vision and image analysis in preoperative planning, image segmentation, surgical navigation, and finally, natural language processing assist in expediting clinical documentation, identification of clinical indications, quality improvement, outcome research, and other types of automated data extraction. The purpose of this review is to familiarize the pediatric surgical community with the rise of AI and highlight the ongoing advancements and challenges in its adoption, including data privacy, regulatory considerations, and the imperative for interdisciplinary collaboration. We hope this review serves as a comprehensive guide to AI's transformative influence on surgery, demonstrating its potential to enhance pediatric surgical patient outcomes, improve precision, and usher in a new era of surgical excellence.
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Affiliation(s)
- Anthony Y Tsai
- Division of Pediatric Surgery, Penn State Health Children's Hospital, 500 University Drive, Hershey, PA 17033, United States.
| | - Stewart R Carter
- Division of Pediatric Surgery, University of Louisville School of Medicine, Louisville, KY, United States
| | - Alicia C Greene
- Division of Pediatric Surgery, Penn State Health Children's Hospital, 500 University Drive, Hershey, PA 17033, United States
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20
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Weidener L, Fischer M. Role of Ethics in Developing AI-Based Applications in Medicine: Insights From Expert Interviews and Discussion of Implications. JMIR AI 2024; 3:e51204. [PMID: 38875585 PMCID: PMC11041491 DOI: 10.2196/51204] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 11/20/2023] [Accepted: 12/09/2023] [Indexed: 06/16/2024]
Abstract
BACKGROUND The integration of artificial intelligence (AI)-based applications in the medical field has increased significantly, offering potential improvements in patient care and diagnostics. However, alongside these advancements, there is growing concern about ethical considerations, such as bias, informed consent, and trust in the development of these technologies. OBJECTIVE This study aims to assess the role of ethics in the development of AI-based applications in medicine. Furthermore, this study focuses on the potential consequences of neglecting ethical considerations in AI development, particularly their impact on patients and physicians. METHODS Qualitative content analysis was used to analyze the responses from expert interviews. Experts were selected based on their involvement in the research or practical development of AI-based applications in medicine for at least 5 years, leading to the inclusion of 7 experts in the study. RESULTS The analysis revealed 3 main categories and 7 subcategories reflecting a wide range of views on the role of ethics in AI development. This variance underscores the subjectivity and complexity of integrating ethics into the development of AI in medicine. Although some experts view ethics as fundamental, others prioritize performance and efficiency, with some perceiving ethics as potential obstacles to technological progress. This dichotomy of perspectives clearly emphasizes the subjectivity and complexity surrounding the role of ethics in AI development, reflecting the inherent multifaceted nature of this issue. CONCLUSIONS Despite the methodological limitations impacting the generalizability of the results, this study underscores the critical importance of consistent and integrated ethical considerations in AI development for medical applications. It advocates further research into effective strategies for ethical AI development, emphasizing the need for transparent and responsible practices, consideration of diverse data sources, physician training, and the establishment of comprehensive ethical and legal frameworks.
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Affiliation(s)
- Lukas Weidener
- Research Unit for Quality and Ethics in Health Care, UMIT TIROL - Private University for Health Sciences and Health Technology, Hall in Tirol, Austria
| | - Michael Fischer
- Research Unit for Quality and Ethics in Health Care, UMIT TIROL - Private University for Health Sciences and Health Technology, Hall in Tirol, Austria
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21
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Staab S, Cardénas A, Peixoto RS, Schreiber F, Voolstra CR. Coracle-a machine learning framework to identify bacteria associated with continuous variables. Bioinformatics 2024; 40:btad749. [PMID: 38123508 PMCID: PMC10766586 DOI: 10.1093/bioinformatics/btad749] [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: 08/10/2023] [Revised: 11/06/2023] [Accepted: 12/19/2023] [Indexed: 12/23/2023] Open
Abstract
SUMMARY We present Coracle, an artificial intelligence (AI) framework that can identify associations between bacterial communities and continuous variables. Coracle uses an ensemble approach of prominent feature selection methods and machine learning (ML) models to identify features, i.e. bacteria, associated with a continuous variable, e.g. host thermal tolerance. The results are aggregated into a score that incorporates the performances of the different ML models and the respective feature importance, while also considering the robustness of feature selection. Additionally, regression coefficients provide first insights into the direction of the association. We show the utility of Coracle by analyzing associations between bacterial composition data (i.e. 16S rRNA Amplicon Sequence Variants, ASVs) and coral thermal tolerance (i.e. standardized short-term heat stress-derived diagnostics). This analysis identified high-scoring bacterial taxa that were previously found associated with coral thermal tolerance. Coracle scales with feature number and performs well with hundreds to thousands of features, corresponding to the typical size of current datasets. Coracle performs best if run at a higher taxonomic level first (e.g. order or family) to identify groups of interest that can subsequently be run at the ASV level. AVAILABILITY AND IMPLEMENTATION Coracle can be accessed via a dedicated web server that allows free and simple access: http://www.micportal.org/coracle/index. The underlying code is open-source and available via GitHub https://github.com/SebastianStaab/coracle.git.
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Affiliation(s)
- Sebastian Staab
- Department of Biology, University of Konstanz, Konstanz 78457, Germany
| | - Anny Cardénas
- Department of Biology, University of Konstanz, Konstanz 78457, Germany
- Department of Biology, American University, Washington, DC, 20016, USA
| | - Raquel S Peixoto
- Computational Biology Research Center (CBRC) and Red Sea Research Center (RSRC), Biological and Environmental Sciences and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
| | - Falk Schreiber
- Department of Computer and Information Science, University of Konstanz, Konstanz 78457, Germany
- Faculty of Information Technology, Monash University, 3168, Australia
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22
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O'Reilly D, McGrath J, Martin-Loeches I. Optimizing artificial intelligence in sepsis management: Opportunities in the present and looking closely to the future. JOURNAL OF INTENSIVE MEDICINE 2024; 4:34-45. [PMID: 38263963 PMCID: PMC10800769 DOI: 10.1016/j.jointm.2023.10.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Revised: 09/29/2023] [Accepted: 10/01/2023] [Indexed: 01/25/2024]
Abstract
Sepsis remains a major challenge internationally for healthcare systems. Its incidence is rising due to poor public awareness and delays in its recognition and subsequent management. In sepsis, mortality increases with every hour left untreated. Artificial intelligence (AI) is transforming worldwide healthcare delivery at present. This review has outlined how AI can augment strategies to address this global disease burden. AI and machine learning (ML) algorithms can analyze vast quantities of increasingly complex clinical datasets from electronic medical records to assist clinicians in diagnosing and treating sepsis earlier than traditional methods. Our review highlights how these models can predict the risk of sepsis and organ failure even before it occurs. This gives providers additional time to plan and execute treatment plans, thereby avoiding increasing complications associated with delayed diagnosis of sepsis. The potential for cost savings with AI implementation is also discussed, including improving workflow efficiencies, reducing administrative costs, and improving healthcare outcomes. Despite these advantages, clinicians have been slow to adopt AI into clinical practice. Some of the limitations posed by AI solutions include the lack of diverse data sets for model building so that they are widely applicable for routine clinical use. Furthermore, the subsequent algorithms are often based on complex mathematics leading to clinician hesitancy to embrace such technologies. Finally, we highlight the need for robust political and regulatory frameworks in this area to achieve the trust and approval of clinicians and patients to implement this transformational technology.
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Affiliation(s)
- Darragh O'Reilly
- Department of Intensive Care Medicine, Multidisciplinary Intensive Care Research Organization (MICRO), St James’ Hospital, Dublin, Ireland
| | - Jennifer McGrath
- Department of Intensive Care Medicine, Multidisciplinary Intensive Care Research Organization (MICRO), St James’ Hospital, Dublin, Ireland
| | - Ignacio Martin-Loeches
- Department of Intensive Care Medicine, Multidisciplinary Intensive Care Research Organization (MICRO), St James’ Hospital, Dublin, Ireland
- Department of Respiratory Intensive care, Hospital Clinic, Universitat de Barcelona, IDIBAPS, CIBERES, Barcelona, Spain
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23
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S Alshuhri M, Al-Musawi SG, Al-Alwany AA, Uinarni H, Rasulova I, Rodrigues P, Alkhafaji AT, Alshanberi AM, Alawadi AH, Abbas AH. Artificial intelligence in cancer diagnosis: Opportunities and challenges. Pathol Res Pract 2024; 253:154996. [PMID: 38118214 DOI: 10.1016/j.prp.2023.154996] [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: 10/20/2023] [Revised: 11/20/2023] [Accepted: 11/27/2023] [Indexed: 12/22/2023]
Abstract
Since cancer is one of the world's top causes of death, early diagnosis is critical to improving patient outcomes. Artificial intelligence (AI) has become a viable technique for cancer diagnosis by using machine learning algorithms to examine large volumes of data for accurate and efficient diagnosis. AI has the potential to alter the way cancer is detected fundamentally. Still, it has several disadvantages, such as requiring a large amount of data, technological limitations, and ethical concerns. This overview looks at the possibilities and restrictions of AI in cancer detection, as well as current applications and possible future developments. We can better understand how to use AI to improve patient outcomes and reduce cancer mortality rates by looking at its potential for cancer detection.
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Affiliation(s)
- Mohammed S Alshuhri
- Radiology and Medical Imaging Department, College of Applied Medical Sciences, Prince Sattam bin Abdulaziz University, Kharj, Saudi Arabia
| | | | | | - Herlina Uinarni
- Department of Anatomy, School of Medicine and Health Sciences Atma Jaya Catholic University of Indonesia, Indonesia; Radiology department of Pantai Indah Kapuk Hospital Jakarta, Jakarta, Indonesia.
| | - Irodakhon Rasulova
- School of Humanities, Natural & Social Sciences, New Uzbekistan University, 54 Mustaqillik Ave., Tashkent 100007, Uzbekistan; Department of Public Health, Samarkand State Medical University, Amir Temur Street 18, Samarkand, Uzbekistan
| | - Paul Rodrigues
- Department of Computer Engineering, College of Computer Science, King Khalid University, Al-Faraa, Abha, Asir, Kingdom of Saudi Arabia
| | | | - Asim Muhammed Alshanberi
- Department of Community Medicine & Pilgrim Healthcare, Umm Alqura University, Makkah 24382, Saudi Arabia; General Medicine Practice Program, Batterjee Medical College, Jeddah 21442, Saudi Arabia
| | - Ahmed Hussien Alawadi
- College of Technical Engineering, the Islamic University, Najaf, Iraq; College of Technical Engineering, the Islamic University of Al Diwaniyah, Iraq; College of Technical Engineering, the Islamic University of Babylon, Iraq
| | - Ali Hashim Abbas
- College of Technical Engineering, Imam Ja'afar Al-Sadiq University, Al-Muthanna 66002, Iraq
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Hussain W, Mabrok M, Gao H, Rabhi FA, Rashed EA. Revolutionising healthcare with artificial intelligence: A bibliometric analysis of 40 years of progress in health systems. Digit Health 2024; 10:20552076241258757. [PMID: 38817839 PMCID: PMC11138196 DOI: 10.1177/20552076241258757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 05/14/2024] [Indexed: 06/01/2024] Open
Abstract
The development of artificial intelligence (AI) has revolutionised the medical system, empowering healthcare professionals to analyse complex nonlinear big data and identify hidden patterns, facilitating well-informed decisions. Over the last decade, there has been a notable trend of research in AI, machine learning (ML), and their associated algorithms in health and medical systems. These approaches have transformed the healthcare system, enhancing efficiency, accuracy, personalised treatment, and decision-making. Recognising the importance and growing trend of research in the topic area, this paper presents a bibliometric analysis of AI in health and medical systems. The paper utilises the Web of Science (WoS) Core Collection database, considering documents published in the topic area for the last four decades. A total of 64,063 papers were identified from 1983 to 2022. The paper evaluates the bibliometric data from various perspectives, such as annual papers published, annual citations, highly cited papers, and most productive institutions, and countries. The paper visualises the relationship among various scientific actors by presenting bibliographic coupling and co-occurrences of the author's keywords. The analysis indicates that the field began its significant growth in the late 1970s and early 1980s, with significant growth since 2019. The most influential institutions are in the USA and China. The study also reveals that the scientific community's top keywords include 'ML', 'Deep Learning', and 'Artificial Intelligence'.
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Affiliation(s)
- Walayat Hussain
- Peter Faber Business School, Australian Catholic University, North Sydney, Australia
| | - Mohamed Mabrok
- Department of Mathematics and Statistics, Qatar University, Doha, Qatar
| | - Honghao Gao
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Fethi A. Rabhi
- School of Computer Science and Engineering, University of New South Wales (UNSW), Sydney, Australia
| | - Essam A. Rashed
- Graduate School of Information Science, University of Hyogo, Kobe, Japan
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25
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Iannone A, Giansanti D. Breaking Barriers-The Intersection of AI and Assistive Technology in Autism Care: A Narrative Review. J Pers Med 2023; 14:41. [PMID: 38248742 PMCID: PMC10817661 DOI: 10.3390/jpm14010041] [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: 11/04/2023] [Revised: 12/18/2023] [Accepted: 12/23/2023] [Indexed: 01/23/2024] Open
Abstract
(Background) Autism increasingly requires a multidisciplinary approach that can effectively harmonize the realms of diagnosis and therapy, tailoring both to the individual. Assistive technologies (ATs) play an important role in this context and hold significant potential when integrated with artificial intelligence (AI). (Objective) The objective of this study is to analyze the state of integration of AI with ATs in autism through a review. (Methods) A review was conducted on PubMed and Scopus, applying a standard checklist and a qualification process. The outcome reported 22 studies, including 7 reviews. (Key Content and Findings) The results reveal an early yet promising interest in integrating AI into autism assistive technologies. Exciting developments are currently underway at the intersection of AI and robotics, as well as in the creation of wearable automated devices like smart glasses. These innovations offer substantial potential for enhancing communication, interaction, and social engagement for individuals with autism. Presently, researchers are prioritizing innovation over establishing a solid presence within the healthcare domain, where issues such as regulation and acceptance demand increased attention. (Conclusions) As the field continues to evolve, it becomes increasingly clear that AI will play a pivotal role in bridging various domains, and integrated ATs with AI are positioned to act as crucial connectors.
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Affiliation(s)
- Antonio Iannone
- CREA, Italian National Research Body, Via Ardeatina, 546, 00178 Roma, Italy
| | - Daniele Giansanti
- Centro Nazionale TISP, Istituto Superiore di Sanità; Viale Regina Elena 299, 00161 Roma, Italy
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Pinto-Coelho L. How Artificial Intelligence Is Shaping Medical Imaging Technology: A Survey of Innovations and Applications. Bioengineering (Basel) 2023; 10:1435. [PMID: 38136026 PMCID: PMC10740686 DOI: 10.3390/bioengineering10121435] [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: 11/18/2023] [Revised: 12/12/2023] [Accepted: 12/15/2023] [Indexed: 12/24/2023] Open
Abstract
The integration of artificial intelligence (AI) into medical imaging has guided in an era of transformation in healthcare. This literature review explores the latest innovations and applications of AI in the field, highlighting its profound impact on medical diagnosis and patient care. The innovation segment explores cutting-edge developments in AI, such as deep learning algorithms, convolutional neural networks, and generative adversarial networks, which have significantly improved the accuracy and efficiency of medical image analysis. These innovations have enabled rapid and accurate detection of abnormalities, from identifying tumors during radiological examinations to detecting early signs of eye disease in retinal images. The article also highlights various applications of AI in medical imaging, including radiology, pathology, cardiology, and more. AI-based diagnostic tools not only speed up the interpretation of complex images but also improve early detection of disease, ultimately delivering better outcomes for patients. Additionally, AI-based image processing facilitates personalized treatment plans, thereby optimizing healthcare delivery. This literature review highlights the paradigm shift that AI has brought to medical imaging, highlighting its role in revolutionizing diagnosis and patient care. By combining cutting-edge AI techniques and their practical applications, it is clear that AI will continue shaping the future of healthcare in profound and positive ways.
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Affiliation(s)
- Luís Pinto-Coelho
- ISEP—School of Engineering, Polytechnic Institute of Porto, 4200-465 Porto, Portugal;
- INESCTEC, Campus of the Engineering Faculty of the University of Porto, 4200-465 Porto, Portugal
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Mohanasundari SK, Kalpana M, Madhusudhan U, Vasanthkumar K, B R, Singh R, Vashishtha N, Bhatia V. Can Artificial Intelligence Replace the Unique Nursing Role? Cureus 2023; 15:e51150. [PMID: 38283483 PMCID: PMC10811613 DOI: 10.7759/cureus.51150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/26/2023] [Indexed: 01/30/2024] Open
Abstract
Artificial intelligence (AI) is transforming healthcare, offering potential benefits and challenges. In healthcare, AI enhances efficiency, streamlines processes, and supports decision-making. However, challenges include potential errors and biases in algorithms, data privacy concerns, legal and ethical issues, and resistance to change. In nursing, a delicate balance emerges between AI and human touch. While AI aids in data-driven decision-making and administrative tasks, it lacks the emotional intelligence, empathy, and nuanced understanding crucial to nursing care. Nurses excel in critical thinking, adaptability to dynamic situations, patient advocacy, collaboration, and establishing human connections. AI supports these functions by automating routine tasks and offering decision support tools, yet its rigidity in dynamic situations and lack of human touch pose limitations. This review underscores the necessity of careful AI integration in healthcare, acknowledging its advantages while mitigating drawbacks. In nursing, the symbiosis between AI and human qualities is vital. The role of AI should be to complement, not replace, the unique skills and empathetic aspects of nursing care. Addressing concerns related to bias, transparency, data privacy, and professional training is essential for maximizing the benefits of AI in healthcare while preserving the human touch in patient care. This article explores whether AI can replace unique nursing roles.
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Affiliation(s)
- S K Mohanasundari
- Pediatric Nursing, All India Institute of Medical Sciences, Bibinagar, Bibinagar, IND
| | - M Kalpana
- Physiology, All India Institute of Medical Sciences, Bibinagar, Bibinagar, IND
| | - U Madhusudhan
- Physiology, All India Institute of Medical Sciences, Bibinagar, Bibinagar, IND
| | - Kasturi Vasanthkumar
- Psychiatric Nursing, All India Institute of Medical Sciences, Bibinagar, Bibinagar, IND
| | - Rani B
- Community Health Nursing, All India Institute of Medical Sciences, Bibinagar, Bibinagar, IND
| | - Rashmi Singh
- Obstetrics and Gynaecology Nursing, All India Institute of Medical Sciences, Bibinagar, Bibinagar, IND
| | - Neelam Vashishtha
- Medical Surgical Nursing, All India Institute of Medical Sciences, Bibinagar, Bibinagar, IND
| | - Vikas Bhatia
- Community Medicine and Family Medicine, All India Institute of Medical Sciences, Bibinagar, Bibinagar, IND
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Pham TD, Sun X. Wavelet scattering networks in deep learning for discovering protein markers in a cohort of Swedish rectal cancer patients. Cancer Med 2023; 12:21502-21518. [PMID: 38014709 PMCID: PMC10726782 DOI: 10.1002/cam4.6672] [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: 08/25/2023] [Revised: 09/25/2023] [Accepted: 10/20/2023] [Indexed: 11/29/2023] Open
Abstract
BACKGROUND Cancer biomarkers play a pivotal role in the diagnosis, prognosis, and treatment response prediction of the disease. In this study, we analyzed the expression levels of RhoB and DNp73 proteins in rectal cancer, as captured in immunohistochemical images, to predict the 5-year survival time of two patient groups: one with preoperative radiotherapy and one without. METHODS The utilization of deep convolutional neural networks in medical research, particularly in clinical cancer studies, has been gaining substantial attention. This success primarily stems from their ability to extract intricate image features that prove invaluable in machine learning. Another innovative method for extracting features at multiple levels is the wavelet-scattering network. Our study combines the strengths of these two convolution-based approaches to robustly extract image features related to protein expression. RESULTS The efficacy of our approach was evaluated across various tissue types, including tumor, biopsy, metastasis, and adjacent normal tissue. Statistical assessments demonstrated exceptional performance across a range of metrics, including prediction accuracy, classification accuracy, precision, and the area under the receiver operating characteristic curve. CONCLUSION These results underscore the potential of dual convolutional learning to assist clinical researchers in the timely validation and discovery of cancer biomarkers.
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Affiliation(s)
- Tuan D. Pham
- Barts and The London School of Medicine and Dentistry Queen MaryUniversity of London Turner StreetLondonUK
| | - Xiao‐Feng Sun
- Division of Oncology Department of Biomedical and Clinical SciencesLinkoping UniversityLinkopingSweden
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Giansanti D. An Umbrella Review of the Fusion of fMRI and AI in Autism. Diagnostics (Basel) 2023; 13:3552. [PMID: 38066793 PMCID: PMC10706112 DOI: 10.3390/diagnostics13233552] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 11/22/2023] [Accepted: 11/25/2023] [Indexed: 04/05/2024] Open
Abstract
The role of functional magnetic resonance imaging (fMRI) is assuming an increasingly central role in autism diagnosis. The integration of Artificial Intelligence (AI) into the realm of applications further contributes to its development. This study's objective is to analyze emerging themes in this domain through an umbrella review, encompassing systematic reviews. The research methodology was based on a structured process for conducting a literature narrative review, using an umbrella review in PubMed and Scopus. Rigorous criteria, a standard checklist, and a qualification process were meticulously applied. The findings include 20 systematic reviews that underscore key themes in autism research, particularly emphasizing the significance of technological integration, including the pivotal roles of fMRI and AI. This study also highlights the enigmatic role of oxytocin. While acknowledging the immense potential in this field, the outcome does not evade acknowledging the significant challenges and limitations. Intriguingly, there is a growing emphasis on research and innovation in AI, whereas aspects related to the integration of healthcare processes, such as regulation, acceptance, informed consent, and data security, receive comparatively less attention. Additionally, the integration of these findings into Personalized Medicine (PM) represents a promising yet relatively unexplored area within autism research. This study concludes by encouraging scholars to focus on the critical themes of health domain integration, vital for the routine implementation of these applications.
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Affiliation(s)
- Daniele Giansanti
- Centro Nazionale TISP, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Roma, Italy
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Parekh ADE, Shaikh OA, Simran, Manan S, Hasibuzzaman MA. Artificial intelligence (AI) in personalized medicine: AI-generated personalized therapy regimens based on genetic and medical history: short communication. Ann Med Surg (Lond) 2023; 85:5831-5833. [PMID: 37915639 PMCID: PMC10617817 DOI: 10.1097/ms9.0000000000001320] [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: 06/12/2023] [Accepted: 09/07/2023] [Indexed: 11/03/2023] Open
Abstract
Personalized medicine entails tailoring medical care to an individual's unique genomic and molecular characteristics. AI holds significant promise in advancing the field of personalized medicine. The challenge lies in effectively analyzing vast amounts of data to create tailored treatment approaches. The incorporation of AI into personalized treatment will require healthcare infrastructure adjustments. Upon patients' arrival, their personal data and clinical information (including images, electrophysiology findings, genetic data, blood pressure, medical notes, etc.) are gathered into the AI system with their consent. Subsequently, the AI system utilizes this patient-specific data to offer healthcare recommendations, aiding healthcare professionals in their clinical decision-making. Results and insights from these recommendations, whether accurate or not, are logged and fed back into the AI system to enhance its precision.
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Affiliation(s)
| | | | - Simran
- Department of Medicine, Shaheed Mohtarma Benazir Bhutto Medical University, Larkana, Pakistan
| | - Sadia Manan
- Department of Medicine, Ziauddin University, Karachi
| | - Md. Al Hasibuzzaman
- Ningbo First Hospital, Ningbo, Zhejiang, China
- Institute of Nutrition and Food Science, University of Dhaka, Dhaka, Bangladesh
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Liu Q, Zhang J, Bai Y. Mapping the landscape of artificial intelligence in skin cancer research: a bibliometric analysis. Front Oncol 2023; 13:1222426. [PMID: 37901316 PMCID: PMC10613074 DOI: 10.3389/fonc.2023.1222426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Accepted: 09/18/2023] [Indexed: 10/31/2023] Open
Abstract
Objective Artificial intelligence (AI), with its potential to diagnose skin cancer, has the potential to revolutionize future medical and dermatological practices. However, the current knowledge regarding the utilization of AI in skin cancer diagnosis remains somewhat limited, necessitating further research. This study employs visual bibliometric analysis to consolidate and present insights into the evolution and deployment of AI in the context of skin cancer. Through this analysis, we aim to shed light on the research developments, focal areas of interest, and emerging trends within AI and its application to skin cancer diagnosis. Methods On July 14, 2023, articles and reviews about the application of AI in skin cancer, spanning the years from 1900 to 2023, were selected from the Web of Science Core Collection. Co-authorship, co-citation, and co-occurrence analyses of countries, institutions, authors, references, and keywords within this field were conducted using a combination of tools, including CiteSpace V (version 6.2. R3), VOSviewer (version 1.6.18), SCImago, Microsoft Excel 2019, and R 4.2.3. Results A total of 512 papers matching the search terms and inclusion/exclusion criteria were published between 1991 and 2023. The United States leads in publications with 149, followed by India with 61. Germany holds eight positions among the top 10 institutions, while the United States has two. The most prevalent journals cited were Cancer, the European Journal of Cancer, and Sensors. The most frequently cited keywords include "skin cancer", "classification", "artificial intelligence", and "deep learning". Conclusions Research into the application of AI in skin cancer is rapidly expanding, and an increasing number of scholars are dedicating their efforts to this field. With the advancement of AI technology, new opportunities have arisen to enhance the accuracy of skin imaging diagnosis, treatment based on big data, and prognosis prediction. However, at present, the majority of AI research in the field of skin cancer diagnosis is still in the feasibility study stage. It has not yet made significant progress toward practical implementation in clinical settings. To make substantial strides in this field, there is a need to enhance collaboration between countries and institutions. Despite the potential benefits of AI in skin cancer research, numerous challenges remain to be addressed, including developing robust algorithms, resolving data quality issues, and enhancing results interpretability. Consequently, sustained efforts are essential to surmount these obstacles and facilitate the practical application of AI in skin cancer research.
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Affiliation(s)
- Qianwei Liu
- Graduate School, Beijing University of Chinese Medicine, Beijing, China
| | - Jie Zhang
- Library, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Yanping Bai
- Graduate School, Beijing University of Chinese Medicine, Beijing, China
- Department of Dermatology, China-Japan Friendship Hospital, Beijing, China
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Abu Hammour K, Alhamad H, Al-Ashwal FY, Halboup A, Abu Farha R, Abu Hammour A. ChatGPT in pharmacy practice: a cross-sectional exploration of Jordanian pharmacists' perception, practice, and concerns. J Pharm Policy Pract 2023; 16:115. [PMID: 37789443 PMCID: PMC10548710 DOI: 10.1186/s40545-023-00624-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 09/22/2023] [Indexed: 10/05/2023] Open
Abstract
OBJECTIVES The purpose of this study is to find out how much pharmacists know and have used ChatGPT in their practice. We investigated the advantages and disadvantages of utilizing ChatGPT in a pharmacy context, the amount of training necessary to use it proficiently, and the influence on patient care using a survey. METHODS This cross-sectional study was carried out between May and June 2023 to assess the potential and problems that pharmacists observed while integrating chatbots powered by AI (ChatGPT) in pharmacy practice. The correlation between perceived benefits and concerns was evaluated using Spearman's rho correlation due to the data's non-normal distribution.Any pharmacists licensed by the Jordanian Pharmacists Association were included in the study. A convenient sampling technique was used to choose the participants, and the study questionnaire was distributed utilizing an online medium (Facebook and WhatsApp). Anyone who expressed interest in taking part was given a link to the study's instructions so they may read them before giving their electronic consent and accessing the survey. RESULTS The potential advantages of ChatGPT in the pharmacy practice were widely acknowledged by the participants. The majority of participants (69.9%) concurred that educational material about pharmacy items or therapeutic areas can be provided using ChatGPT, with 66.9% of respondents believing that ChatGPT is a machine learning algorithm. Concerns about the accuracy of AI-generated responses were also prevalent. More than half of the participants (55.7%) raised the possibility that AI systems such as ChatGPT could pick up on and replicate prejudices and discriminatory patterns from the data they were trained on. Analysis shows a statistically significant positive link, albeit a minor one, between the perceived advantages of ChatGPT and its drawbacks (r = 0.255, p < 0.001). However, concerns were strongly correlated with knowledge of ChatGPT. In contrast to those who were either unsure or had not heard of ChatGPT (64.2%), individuals who had heard of it were more likely to have strong concerns (79.8%) (p = 0.002). Finally, the results show a statistically significant association between the frequency of ChatGPT use and positive perceptions of the tool (p < 0.001). CONCLUSIONS Although ChatGPT has shown promise in health and pharmaceutical practice, its application should be rigorously regulated by evidence-based law. According to the study's findings, pharmacists support the use of ChatGPT in pharmacy practice but have concerns about its use due to ethical reasons, legal problems, privacy concerns, worries about the accuracy of the data generated, data learning, and bias risk.
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Affiliation(s)
- Khawla Abu Hammour
- Department of Clinical Pharmacy and Biopharmaceutics, Faculty of Pharmacy, University of Jordan, Amman, Jordan
| | - Hamza Alhamad
- Department of Clinical Pharmacy, Faculty of Pharmacy, Zarqa University, Zarqa, Jordan
| | - Fahmi Y Al-Ashwal
- Department of Clinical Pharmacy, College of Pharmacy, Al-Ayen University, Thi-Qar, Iraq.
- Department of Clinical Pharmacy and Pharmacy Practice, Faculty of Pharmacy, University of Science and Technology, Sana'a, Yemen.
| | - Abdulsalam Halboup
- Department of Clinical Pharmacy and Pharmacy Practice, Faculty of Pharmacy, University of Science and Technology, Sana'a, Yemen
- Discipline of Clinical Pharmacy, School of Pharmaceutical Sciences, University Sains Malaysia, Gelugor, Pulau Pinang, Malaysia
| | - Rana Abu Farha
- Clinical Pharmacy and Therapeutics Department, Faculty of Pharmacy, Applied Science Private University, P.O. Box 11937, Amman, Jordan
| | - Adnan Abu Hammour
- Medrise Medical Center, Dubai Healthcare City, Dubai, United Arab Emirates
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Sarkar S, Jadhav U, Ghewade B, Sarkar S, Wagh P. Oscillometry in Lung Function Assessment: A Comprehensive Review of Current Insights and Challenges. Cureus 2023; 15:e47935. [PMID: 38034137 PMCID: PMC10685051 DOI: 10.7759/cureus.47935] [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: 09/18/2023] [Accepted: 10/29/2023] [Indexed: 12/02/2023] Open
Abstract
Oscillometry, a non-invasive technique for assessing lung function, has gained significant recognition and importance in modern pulmonary medicine. This comprehensive review thoroughly explores its principles, applications, advantages, limitations, recent innovations, and future directions. Oscillometry's primary strength lies in its ability to offer a holistic assessment of lung mechanics. Unlike traditional spirometry, oscillometry captures the natural airflow during quiet breathing, making it suitable for patients of all ages and those with severe respiratory conditions. It provides a comprehensive evaluation of airway resistance, reactance, and compliance, offering insights into lung function that were previously challenging to obtain. In clinical practice, oscillometry finds extensive application in diagnosing and managing respiratory diseases. It plays a pivotal role in asthma, chronic obstructive pulmonary disease (COPD), and interstitial lung diseases. By detecting subtle changes in lung function before symptoms manifest, oscillometry facilitates early interventions, improving disease management and patient outcomes. Oscillometry's non-invasive and patient-friendly nature is precious in pediatric care, where traditional spirometry may be challenging for young patients. It aids in diagnosing and monitoring pediatric respiratory disorders, ensuring that children receive the care they need from an early age. Despite its many advantages, oscillometry faces challenges, such as the need for standardized protocols and the complexity of data interpretation. However, ongoing efforts to establish global standards and provide education and training for healthcare professionals aim to address these issues. Looking ahead, oscillometry holds great promise in the field of personalized medicine. With its ability to tailor treatment plans based on individualized lung function data, healthcare providers can optimize therapy selection and dosing, ultimately improving patient care and quality of life. In conclusion, oscillometry is poised to play an increasingly pivotal role in modern pulmonary medicine. As standardization efforts continue and technology evolves, it is an indispensable tool in the clinician's arsenal for diagnosing, managing, and personalizing respiratory care, ultimately leading to improved patient outcomes and better respiratory health.
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Affiliation(s)
- Souvik Sarkar
- Respiratory Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education & Research, Wardha, IND
| | - Ulhas Jadhav
- Respiratory Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education & Research, Wardha, IND
| | - Babaji Ghewade
- Respiratory Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education & Research, Wardha, IND
| | - Syamal Sarkar
- Respiratory Medicine, Advanced Chest Care Centre, Ranchi, IND
| | - Pankaj Wagh
- Respiratory Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education & Research, Wardha, IND
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Kim JK, McCammon KA, Kim KJ, Rickard M, Lorenzo AJ, Chua ME. Development and use of machine learning models for prediction of male sling success A proof-of-concept institutional evaluation. Can Urol Assoc J 2023; 17:E309-E314. [PMID: 37494315 PMCID: PMC10581726 DOI: 10.5489/cuaj.8265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/28/2023]
Abstract
INTRODUCTION For mild to moderate male stress urinary incontinence (SUI), transobturator male slings remain an effective option for management. We aimed to use a machine learning (ML )-based model to predict those who will have a long-term success in managing SUI with male sling. METHODS All transobturator male sling cases from August 2006 to June 2012 by a single surgeon were reviewed. Outcome of interest was defined as 'cure': complete dryness with 0 pads used, without the need for additional procedures. Clinical variables included in ML models were: number of pads used daily, age, height, weight, race, incontinence type, etiology of incontinence, history of radiation, smoking, bladder neck contracture, and prostatectomy. Model performance was assessed using area under receiver operating characteristic curve (AUROC), area under precision-recall curve (AUPRC), and F1-score. RESULTS A total of 181 patients were included in the model. The mean followup was 56.4 months (standard deviation [SD ] 41.6). Slightly more than half (53.6%, 97/181) of patients had procedural success. Logistic regression, K-nearest neighbor (KNN ), naive Bayes, decision tree, and random forest models were developed using ML. KNN model had the best performance, with AUROC of 0.759, AUPRC of 0.916, and F1-score of 0.833. Following ensemble learning with bagging and calibration, KNN model was further improved, with AUROC of 0.821, AUPRC of 0.921, and F-1 score of 0.848. CONCLUSIONS ML-based prediction of long-term transobturator male sling is feasible. The low numbers of patients used to develop the model prompt further validation and development of the model but may serve as a decision-making aid for practitioners in the future.
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Affiliation(s)
- Jin K. Kim
- Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada
- Division of Urology, Department of Surgery, The Hospital for Sick Children, Toronto, ON, Canada
| | - Kurt A. McCammon
- Department of Urology, Eastern Virginia Medical School, Norfolk, VA; Devine-Jordan Center for Reconstructive Surgery and Pelvic Health, Urology of Virginia, Virginia Beach, VA, United States
| | - Kellie J. Kim
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Mandy Rickard
- Division of Urology, Department of Surgery, The Hospital for Sick Children, Toronto, ON, Canada
| | - Armando J. Lorenzo
- Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada
- Division of Urology, Department of Surgery, The Hospital for Sick Children, Toronto, ON, Canada
| | - Michael E. Chua
- Division of Urology, Department of Surgery, The Hospital for Sick Children, Toronto, ON, Canada
- Institute of Urology, St. Luke’s Medical Center, Quezon City, Philippines
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Vidhya KS, Sultana A, M NK, Rangareddy H. Artificial Intelligence's Impact on Drug Discovery and Development From Bench to Bedside. Cureus 2023; 15:e47486. [PMID: 37881323 PMCID: PMC10597591 DOI: 10.7759/cureus.47486] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/22/2023] [Indexed: 10/27/2023] Open
Abstract
Artificial intelligence (AI) techniques have the potential to revolutionize drug release modeling, optimize therapy for personalized medicine, and minimize side effects. By applying AI algorithms, researchers can predict drug release profiles, incorporate patient-specific factors, and optimize dosage regimens to achieve tailored and effective therapies. This AI-based approach has the potential to improve treatment outcomes, enhance patient satisfaction, and advance the field of pharmaceutical sciences. International collaborations and professional organizations play vital roles in establishing guidelines and best practices for data collection and sharing. Open data initiatives can enhance transparency and scientific progress, facilitating algorithm validation.
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Affiliation(s)
- K S Vidhya
- Bioinformatics, University of Visvesvaraya College of Engineering, Bangalore, IND
| | - Ayesha Sultana
- Pathology, St. George's University School of Medicine, St. George's, GRD
| | - Naveen Kumar M
- Pharmacology, Haveri Institute of Medical Sciences, Haveri, IND
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Frascarelli C, Bonizzi G, Musico CR, Mane E, Cassi C, Guerini Rocco E, Farina A, Scarpa A, Lawlor R, Reggiani Bonetti L, Caramaschi S, Eccher A, Marletta S, Fusco N. Revolutionizing Cancer Research: The Impact of Artificial Intelligence in Digital Biobanking. J Pers Med 2023; 13:1390. [PMID: 37763157 PMCID: PMC10532470 DOI: 10.3390/jpm13091390] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 09/05/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND Biobanks are vital research infrastructures aiming to collect, process, store, and distribute biological specimens along with associated data in an organized and governed manner. Exploiting diverse datasets produced by the biobanks and the downstream research from various sources and integrating bioinformatics and "omics" data has proven instrumental in advancing research such as cancer research. Biobanks offer different types of biological samples matched with rich datasets comprising clinicopathologic information. As digital pathology and artificial intelligence (AI) have entered the precision medicine arena, biobanks are progressively transitioning from mere biorepositories to integrated computational databanks. Consequently, the application of AI and machine learning on these biobank datasets holds huge potential to profoundly impact cancer research. METHODS In this paper, we explore how AI and machine learning can respond to the digital evolution of biobanks with flexibility, solutions, and effective services. We look at the different data that ranges from specimen-related data, including digital images, patient health records and downstream genetic/genomic data and resulting "Big Data" and the analytic approaches used for analysis. RESULTS These cutting-edge technologies can address the challenges faced by translational and clinical research, enhancing their capabilities in data management, analysis, and interpretation. By leveraging AI, biobanks can unlock valuable insights from their vast repositories, enabling the identification of novel biomarkers, prediction of treatment responses, and ultimately facilitating the development of personalized cancer therapies. CONCLUSIONS The integration of biobanking with AI has the potential not only to expand the current understanding of cancer biology but also to pave the way for more precise, patient-centric healthcare strategies.
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Affiliation(s)
- Chiara Frascarelli
- Division of Pathology, IEO, European Institute of Oncology IRCCS, 20139 Milan, Italy; (C.F.); (E.M.); (E.G.R.); (N.F.)
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Giuseppina Bonizzi
- Biobank for Translational and Digital Medicine, IEO, European Institute of Oncology IRCCS, 20139 Milan, Italy; (G.B.); (C.R.M.); (C.C.)
| | - Camilla Rosella Musico
- Biobank for Translational and Digital Medicine, IEO, European Institute of Oncology IRCCS, 20139 Milan, Italy; (G.B.); (C.R.M.); (C.C.)
| | - Eltjona Mane
- Division of Pathology, IEO, European Institute of Oncology IRCCS, 20139 Milan, Italy; (C.F.); (E.M.); (E.G.R.); (N.F.)
| | - Cristina Cassi
- Biobank for Translational and Digital Medicine, IEO, European Institute of Oncology IRCCS, 20139 Milan, Italy; (G.B.); (C.R.M.); (C.C.)
| | - Elena Guerini Rocco
- Division of Pathology, IEO, European Institute of Oncology IRCCS, 20139 Milan, Italy; (C.F.); (E.M.); (E.G.R.); (N.F.)
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Annarosa Farina
- Central Information Systems and Technology Directorate, IEO, European Institute of Oncology IRCCS, 20139 Milan, Italy;
| | - Aldo Scarpa
- Department of Diagnostics and Public Health, Section of Pathology, University of Verona, 37134 Verona, Italy; (A.S.); (S.M.)
| | - Rita Lawlor
- ARC-Net Research Centre and Department of Diagnostics and Public Health, University of Verona, 37134 Verona, Italy;
| | - Luca Reggiani Bonetti
- Section of Pathology, Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, University Hospital of Modena, 41121 Modena, Italy; (L.R.B.); (S.C.)
| | - Stefania Caramaschi
- Section of Pathology, Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, University Hospital of Modena, 41121 Modena, Italy; (L.R.B.); (S.C.)
| | - Albino Eccher
- Section of Pathology, Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, University Hospital of Modena, 41121 Modena, Italy; (L.R.B.); (S.C.)
| | - Stefano Marletta
- Department of Diagnostics and Public Health, Section of Pathology, University of Verona, 37134 Verona, Italy; (A.S.); (S.M.)
- Division of Pathology, Humanitas Cancer Center, 95045 Catania, Italy
| | - Nicola Fusco
- Division of Pathology, IEO, European Institute of Oncology IRCCS, 20139 Milan, Italy; (C.F.); (E.M.); (E.G.R.); (N.F.)
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
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Yang B, Teng M, You H, Dong Y, Chen S. A Nomogram for Predicting Survival in Advanced Non-Small-Cell Lung Carcinoma Patients: A Population-Based Study. Cancer Invest 2023; 41:672-685. [PMID: 37490629 DOI: 10.1080/07357907.2023.2241547] [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: 07/27/2022] [Revised: 12/17/2022] [Accepted: 07/21/2023] [Indexed: 07/27/2023]
Abstract
Non-small-cell lung cancer (NSCLC) remains the most common malignant cancer. We identified 43140 advanced NSCLC patients from the SEER database to develop and validate a new prognostic model. The prognostic performance was evaluated by P value, concordance index, net reclassification index, integrated discrimination improvement, and decision curve analysis. The following variables were contained in the final prognostic model: age, sex, race, TNM stage, and grade and treatment options. Compared to the AJCC staging system, this prognostic model is conducive to the implementation of individualized clinical treatment schemes and can be an important part of the precise medical care of NSCLC tumors.
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Affiliation(s)
- Bo Yang
- Department of Pharmacy, First Affiliated Hospital of Xi'an Jiaotong University, Shannxi, China
| | - Mengmeng Teng
- Department of Pharmacy, First Affiliated Hospital of Xi'an Jiaotong University, Shannxi, China
| | - Haisheng You
- Department of Pharmacy, First Affiliated Hospital of Xi'an Jiaotong University, Shannxi, China
| | - Yalin Dong
- Department of Pharmacy, First Affiliated Hospital of Xi'an Jiaotong University, Shannxi, China
| | - Siying Chen
- Department of Pharmacy, First Affiliated Hospital of Xi'an Jiaotong University, Shannxi, China
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Asan O, Choi E, Wang X. Artificial Intelligence-Based Consumer Health Informatics Application: Scoping Review. J Med Internet Res 2023; 25:e47260. [PMID: 37647122 PMCID: PMC10500367 DOI: 10.2196/47260] [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: 03/13/2023] [Revised: 07/02/2023] [Accepted: 07/18/2023] [Indexed: 09/01/2023] Open
Abstract
BACKGROUND There is no doubt that the recent surge in artificial intelligence (AI) research will change the trajectory of next-generation health care, making it more approachable and accessible to patients. Therefore, it is critical to research patient perceptions and outcomes because this trend will allow patients to be the primary consumers of health technology and decision makers for their own health. OBJECTIVE This study aimed to review and analyze papers on AI-based consumer health informatics (CHI) for successful future patient-centered care. METHODS We searched for all peer-reviewed papers in PubMed published in English before July 2022. Research on an AI-based CHI tool or system that reports patient outcomes or perceptions was identified for the scoping review. RESULTS We identified 20 papers that met our inclusion criteria. The eligible studies were summarized and discussed with respect to the role of the AI-based CHI system, patient outcomes, and patient perceptions. The AI-based CHI systems identified included systems in mobile health (13/20, 65%), robotics (5/20, 25%), and telemedicine (2/20, 10%). All the systems aimed to provide patients with personalized health care. Patient outcomes and perceptions across various clinical disciplines were discussed, demonstrating the potential of an AI-based CHI system to benefit patients. CONCLUSIONS This scoping review showed the trend in AI-based CHI systems and their impact on patient outcomes as well as patients' perceptions of these systems. Future studies should also explore how clinicians and health care professionals perceive these consumer-based systems and integrate them into the overall workflow.
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Affiliation(s)
- Onur Asan
- School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ, United States
| | - Euiji Choi
- Department of Computer Science, Stevens Institute of Technology, Hoboken, NJ, United States
| | - Xiaomei Wang
- Department of Industrial Engieering, University of Louisville, Louisville, KY, United States
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Kamrani A, Hosseinzadeh R, Shomali N, Heris JA, Shahabi P, Mohammadinasab R, Sadeghvand S, Ghahremanzadeh K, Sadeghi M, Akbari M. New immunotherapeutic approaches for cancer treatment. Pathol Res Pract 2023; 248:154632. [PMID: 37480597 DOI: 10.1016/j.prp.2023.154632] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 06/17/2023] [Accepted: 06/18/2023] [Indexed: 07/24/2023]
Abstract
Neoplasms are a worldwide recognized non-contagious disease which has the most mortality rate after cardiovascular diseases. For decades, there has been a vast amount of study on treatment methods of cancer which has led to conventional therapies such as chemotherapy, radiation therapy, surgery and so on. Clinicians and researchers believed that there is an urgent need, considering the high rate of incidence and prevalence, for an alternative treatment option which is more efficacious and has less adverse effects than the above-mentioned treatments. Immunotherapy has emerged as a potential treatment alternative in a few years and became one of the fastest developing therapeutic approaches. Different kinds of immunotherapies are FDA approved and available for treatment of various cancer types. In this review, we have summarized the major immunotherapy methods including checkpoint inhibitors, CAR T cell therapies and cancer vaccines. Furthermore, application of combination therapy, precision medicine, biomarker discovery, overcoming resistance and reduction of adverse effects are discussed in this study.
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Affiliation(s)
- Amin Kamrani
- Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran; Department of Immunology, Faculty of Medicine, Tabriz University of Medical Science, Tabriz, Iran
| | - Ramin Hosseinzadeh
- Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Navid Shomali
- Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran; Department of Immunology, Faculty of Medicine, Tabriz University of Medical Science, Tabriz, Iran
| | - Javad Ahmadian Heris
- Department of Allergy and Clinical Immunology, Pediatric Hospital, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Parviz Shahabi
- Department of Physiology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Reza Mohammadinasab
- Department of History of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Shahram Sadeghvand
- Pediatrics Health Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.
| | | | - Mohammadreza Sadeghi
- Department of molecular medicine, Tabriz University of Medical Science, Tabriz, Iran
| | - Morteza Akbari
- Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran; Department of Medical Biotechnology, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran.
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Nakamura K, Uchino E, Sato N, Araki A, Terayama K, Kojima R, Murashita K, Itoh K, Mikami T, Tamada Y, Okuno Y. Individual health-disease phase diagrams for disease prevention based on machine learning. J Biomed Inform 2023; 144:104448. [PMID: 37467834 DOI: 10.1016/j.jbi.2023.104448] [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: 01/07/2023] [Revised: 07/09/2023] [Accepted: 07/16/2023] [Indexed: 07/21/2023]
Abstract
Early disease detection and prevention methods based on effective interventions are gaining attention worldwide. Progress in precision medicine has revealed that substantial heterogeneity exists in health data at the individual level and that complex health factors are involved in chronic disease development. Machine-learning techniques have enabled precise personal-level disease prediction by capturing individual differences in multivariate data. However, it is challenging to identify what aspects should be improved for disease prevention based on future disease-onset prediction because of the complex relationships among multiple biomarkers. Here, we present a health-disease phase diagram (HDPD) that represents an individual's health state by visualizing the future-onset boundary values of multiple biomarkers that fluctuate early in the disease progression process. In HDPDs, future-onset predictions are represented by perturbing multiple biomarker values while accounting for dependencies among variables. We constructed HDPDs for 11 diseases using longitudinal health checkup cohort data of 3,238 individuals, comprising 3,215 measurement items and genetic data. The improvement of biomarker values to the non-onset region in HDPD remarkably prevented future disease onset in 7 out of 11 diseases. HDPDs can represent individual physiological states in the onset process and be used as intervention goals for disease prevention.
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Affiliation(s)
- Kazuki Nakamura
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan; Research and Business Development Department, Kyowa Hakko Bio Co., Ltd., Tokyo 100-0004, Japan
| | - Eiichiro Uchino
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Noriaki Sato
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Ayano Araki
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Kei Terayama
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan; Graduate School of Medical Life Science, Yokohama City University, Kanagawa 230-0045, Japan
| | - Ryosuke Kojima
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Koichi Murashita
- Center of Innovation Research Initiatives Organization (The Center of Healthy Aging Innovation), Graduate School of Medicine, Hirosaki University, Aomori 036-8562, Japan
| | - Ken Itoh
- Department of Stress Response Science, Graduate School of Medicine, Hirosaki University, Aomori 036-8562, Japan
| | - Tatsuya Mikami
- Innovation Center for Health Promotion, Graduate School of Medicine, Hirosaki University, Aomori 036-8562, Japan
| | - Yoshinori Tamada
- Innovation Center for Health Promotion, Graduate School of Medicine, Hirosaki University, Aomori 036-8562, Japan
| | - Yasushi Okuno
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan.
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Singh S, Kumar R, Payra S, Singh SK. Artificial Intelligence and Machine Learning in Pharmacological Research: Bridging the Gap Between Data and Drug Discovery. Cureus 2023; 15:e44359. [PMID: 37779744 PMCID: PMC10539991 DOI: 10.7759/cureus.44359] [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] [Accepted: 07/31/2023] [Indexed: 10/03/2023] Open
Abstract
Artificial intelligence (AI) has transformed pharmacological research through machine learning, deep learning, and natural language processing. These advancements have greatly influenced drug discovery, development, and precision medicine. AI algorithms analyze vast biomedical data identifying potential drug targets, predicting efficacy, and optimizing lead compounds. AI has diverse applications in pharmacological research, including target identification, drug repurposing, virtual screening, de novo drug design, toxicity prediction, and personalized medicine. AI improves patient selection, trial design, and real-time data analysis in clinical trials, leading to enhanced safety and efficacy outcomes. Post-marketing surveillance utilizes AI-based systems to monitor adverse events, detect drug interactions, and support pharmacovigilance efforts. Machine learning models extract patterns from complex datasets, enabling accurate predictions and informed decision-making, thus accelerating drug discovery. Deep learning, specifically convolutional neural networks (CNN), excels in image analysis, aiding biomarker identification and optimizing drug formulation. Natural language processing facilitates the mining and analysis of scientific literature, unlocking valuable insights and information. However, the adoption of AI in pharmacological research raises ethical considerations. Ensuring data privacy and security, addressing algorithm bias and transparency, obtaining informed consent, and maintaining human oversight in decision-making are crucial ethical concerns. The responsible deployment of AI necessitates robust frameworks and regulations. The future of AI in pharmacological research is promising, with integration with emerging technologies like genomics, proteomics, and metabolomics offering the potential for personalized medicine and targeted therapies. Collaboration among academia, industry, and regulatory bodies is essential for the ethical implementation of AI in drug discovery and development. Continuous research and development in AI techniques and comprehensive training programs will empower scientists and healthcare professionals to fully exploit AI's potential, leading to improved patient outcomes and innovative pharmacological interventions.
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Affiliation(s)
- Shruti Singh
- Department of Pharmacology, All India Institute of Medical Sciences, Patna, IND
| | - Rajesh Kumar
- Department of Pharmacology, All India Institute of Medical Sciences, Patna, IND
| | - Shuvasree Payra
- Department of Pharmacology, All India Institute of Medical Sciences, Patna, IND
| | - Sunil K Singh
- Department of Pharmacology, All India Institute of Medical Sciences, Patna, IND
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Alkhodari M, Xiong Z, Khandoker AH, Hadjileontiadis LJ, Leeson P, Lapidaire W. The role of artificial intelligence in hypertensive disorders of pregnancy: towards personalized healthcare. Expert Rev Cardiovasc Ther 2023; 21:531-543. [PMID: 37300317 DOI: 10.1080/14779072.2023.2223978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 06/06/2023] [Indexed: 06/12/2023]
Abstract
INTRODUCTION Guidelines advise ongoing follow-up of patients after hypertensive disorders of pregnancy (HDP) to assess cardiovascular risk and manage future patient-specific pregnancy conditions. However, there are limited tools available to monitor patients, with those available tending to be simple risk assessments that lack personalization. A promising approach could be the emerging artificial intelligence (AI)-based techniques, developed from big patient datasets to provide personalized recommendations for preventive advice. AREAS COVERED In this narrative review, we discuss the impact of integrating AI and big data analysis for personalized cardiovascular care, focusing on the management of HDP. EXPERT OPINION The pathophysiological response of women to pregnancy varies, and deeper insight into each response can be gained through a deeper analysis of the medical history of pregnant women based on clinical records and imaging data. Further research is required to be able to implement AI for clinical cases using multi-modality and multi-organ assessment, and this could expand both knowledge on pregnancy-related disorders and personalized treatment planning.
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Affiliation(s)
- Mohanad Alkhodari
- Cardiovascular Clinical Research Facility, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Healthcare Engineering Innovation Center (HEIC), Department of Biomedical Engineering, Khalifa University of Science and Tehcnology, Abu Dhabi, UAE
| | - Zhaohan Xiong
- Cardiovascular Clinical Research Facility, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Ahsan H Khandoker
- Healthcare Engineering Innovation Center (HEIC), Department of Biomedical Engineering, Khalifa University of Science and Tehcnology, Abu Dhabi, UAE
| | - Leontios J Hadjileontiadis
- Healthcare Engineering Innovation Center (HEIC), Department of Biomedical Engineering, Khalifa University of Science and Tehcnology, Abu Dhabi, UAE
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Paul Leeson
- Cardiovascular Clinical Research Facility, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Winok Lapidaire
- Cardiovascular Clinical Research Facility, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
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Singh AV, Chandrasekar V, Paudel N, Laux P, Luch A, Gemmati D, Tissato V, Prabhu KS, Uddin S, Dakua SP. Integrative toxicogenomics: Advancing precision medicine and toxicology through artificial intelligence and OMICs technology. Biomed Pharmacother 2023; 163:114784. [PMID: 37121152 DOI: 10.1016/j.biopha.2023.114784] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 04/15/2023] [Accepted: 04/24/2023] [Indexed: 05/02/2023] Open
Abstract
More information about a person's genetic makeup, drug response, multi-omics response, and genomic response is now available leading to a gradual shift towards personalized treatment. Additionally, the promotion of non-animal testing has fueled the computational toxicogenomics as a pivotal part of the next-gen risk assessment paradigm. Artificial Intelligence (AI) has the potential to provid new ways analyzing the patient data and making predictions about treatment outcomes or toxicity. As personalized medicine and toxicogenomics involve huge data processing, AI can expedite this process by providing powerful data processing, analysis, and interpretation algorithms. AI can process and integrate a multitude of data including genome data, patient records, clinical data and identify patterns to derive predictive models anticipating clinical outcomes and assessing the risk of any personalized medicine approaches. In this article, we have studied the current trends and future perspectives in personalized medicine & toxicology, the role of toxicogenomics in connecting the two fields, and the impact of AI on personalized medicine & toxicology. In this work, we also study the key challenges and limitations in personalized medicine, toxicogenomics, and AI in order to fully realize their potential.
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Affiliation(s)
- Ajay Vikram Singh
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), 10589 Berlin, Germany
| | | | - Namuna Paudel
- Department of Chemistry, Amrit Campus, Institute of Science and Technology, Tribhuvan University, Lainchaur, Kathmandu 44600 Nepal
| | - Peter Laux
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), 10589 Berlin, Germany
| | - Andreas Luch
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), 10589 Berlin, Germany
| | - Donato Gemmati
- Department of Translational Medicine, University of Ferrara, 44121 Ferrara, Italy; Centre Hemostasis & Thrombosis, University of Ferrara, 44121 Ferrara, Italy; Centre for Gender Medicine, University of Ferrara, 44121 Ferrara, Italy
| | - Veronica Tissato
- Department of Translational Medicine, University of Ferrara, 44121 Ferrara, Italy; Centre Hemostasis & Thrombosis, University of Ferrara, 44121 Ferrara, Italy; Centre for Gender Medicine, University of Ferrara, 44121 Ferrara, Italy
| | - Kirti S Prabhu
- Translational Research Institute, Academic Health System, Hamad Medical Corporation, Doha, Qatar
| | - Shahab Uddin
- Translational Research Institute, Academic Health System, Hamad Medical Corporation, Doha, Qatar
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She H, Tan L, Wang Y, Du Y, Zhou Y, Zhang J, Du Y, Guo N, Wu Z, Li Q, Bao D, Mao Q, Hu Y, Liu L, Li T. Integrative single-cell RNA sequencing and metabolomics decipher the imbalanced lipid-metabolism in maladaptive immune responses during sepsis. Front Immunol 2023; 14:1181697. [PMID: 37180171 PMCID: PMC10172510 DOI: 10.3389/fimmu.2023.1181697] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 04/13/2023] [Indexed: 05/15/2023] Open
Abstract
Background To identify differentially expressed lipid metabolism-related genes (DE-LMRGs) responsible for immune dysfunction in sepsis. Methods The lipid metabolism-related hub genes were screened using machine learning algorithms, and the immune cell infiltration of these hub genes were assessed by CIBERSORT and Single-sample GSEA. Next, the immune function of these hub genes at the single-cell level were validated by comparing multiregional immune landscapes between septic patients (SP) and healthy control (HC). Then, the support vector machine-recursive feature elimination (SVM-RFE) algorithm was conducted to compare the significantly altered metabolites critical to hub genes between SP and HC. Furthermore, the role of the key hub gene was verified in sepsis rats and LPS-induced cardiomyocytes, respectively. Results A total of 508 DE-LMRGs were identified between SP and HC, and 5 hub genes relevant to lipid metabolism (MAPK14, EPHX2, BMX, FCER1A, and PAFAH2) were screened. Then, we found an immunosuppressive microenvironment in sepsis. The role of hub genes in immune cells was further confirmed by the single-cell RNA landscape. Moreover, significantly altered metabolites were mainly enriched in lipid metabolism-related signaling pathways and were associated with MAPK14. Finally, inhibiting MAPK14 decreased the levels of inflammatory cytokines and improved the survival and myocardial injury of sepsis. Conclusion The lipid metabolism-related hub genes may have great potential in prognosis prediction and precise treatment for sepsis patients.
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Affiliation(s)
- Han She
- State Key Laboratory of Trauma, Burns and Combined Injury, Shock and Transfusion Department, Daping Hospital, Army Medical University, Chongqing, China
- Department of Anesthesiology, Daping Hospital, Army Medical University, Chongqing, China
| | - Lei Tan
- State Key Laboratory of Trauma, Burns and Combined Injury, Shock and Transfusion Department, Daping Hospital, Army Medical University, Chongqing, China
- Department of Anesthesiology, Daping Hospital, Army Medical University, Chongqing, China
| | - Yi Wang
- State Key Laboratory of Trauma, Burns and Combined Injury, Shock and Transfusion Department, Daping Hospital, Army Medical University, Chongqing, China
- Department of Anesthesiology, Daping Hospital, Army Medical University, Chongqing, China
| | - Yuanlin Du
- Department of Anesthesiology, Daping Hospital, Army Medical University, Chongqing, China
| | - Yuanqun Zhou
- State Key Laboratory of Trauma, Burns and Combined Injury, Shock and Transfusion Department, Daping Hospital, Army Medical University, Chongqing, China
| | - Jun Zhang
- Department of Anesthesiology, Daping Hospital, Army Medical University, Chongqing, China
| | - Yunxia Du
- State Key Laboratory of Trauma, Burns and Combined Injury, Shock and Transfusion Department, Daping Hospital, Army Medical University, Chongqing, China
- Department of Anesthesiology, Daping Hospital, Army Medical University, Chongqing, China
| | - Ningke Guo
- State Key Laboratory of Trauma, Burns and Combined Injury, Shock and Transfusion Department, Daping Hospital, Army Medical University, Chongqing, China
| | - Zhengbin Wu
- Department of Intensive Care Unit, Daping Hospital, Army Medical University, Chongqing, China
| | - Qinghui Li
- State Key Laboratory of Trauma, Burns and Combined Injury, Shock and Transfusion Department, Daping Hospital, Army Medical University, Chongqing, China
| | - Daiqin Bao
- Department of Anesthesiology, Daping Hospital, Army Medical University, Chongqing, China
| | - Qingxiang Mao
- Department of Anesthesiology, Daping Hospital, Army Medical University, Chongqing, China
| | - Yi Hu
- Department of Anesthesiology, Daping Hospital, Army Medical University, Chongqing, China
| | - Liangming Liu
- State Key Laboratory of Trauma, Burns and Combined Injury, Shock and Transfusion Department, Daping Hospital, Army Medical University, Chongqing, China
| | - Tao Li
- State Key Laboratory of Trauma, Burns and Combined Injury, Shock and Transfusion Department, Daping Hospital, Army Medical University, Chongqing, China
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Chow R, Midroni J, Kaur J, Boldt G, Liu G, Eng L, Liu FF, Haibe-Kains B, Lock M, Raman S. Use of artificial intelligence for cancer clinical trial enrollment: a systematic review and meta-analysis. J Natl Cancer Inst 2023; 115:365-374. [PMID: 36688707 PMCID: PMC10086633 DOI: 10.1093/jnci/djad013] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 12/13/2022] [Accepted: 01/11/2023] [Indexed: 01/24/2023] Open
Abstract
BACKGROUND The aim of this study is to provide a comprehensive understanding of the current landscape of artificial intelligence (AI) for cancer clinical trial enrollment and its predictive accuracy in identifying eligible patients for inclusion in such trials. METHODS Databases of PubMed, Embase, and Cochrane CENTRAL were searched until June 2022. Articles were included if they reported on AI actively being used in the clinical trial enrollment process. Narrative synthesis was conducted among all extracted data: accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. For studies where the 2x2 contingency table could be calculated or supplied by authors, a meta-analysis to calculate summary statistics was conducted using the hierarchical summary receiver operating characteristics curve model. RESULTS Ten articles reporting on more than 50 000 patients in 19 datasets were included. Accuracy, sensitivity, and specificity exceeded 80% in all but 1 dataset. Positive predictive value exceeded 80% in 5 of 17 datasets. Negative predictive value exceeded 80% in all datasets. Summary sensitivity was 90.5% (95% confidence interval [CI] = 70.9% to 97.4%); summary specificity was 99.3% (95% CI = 81.8% to 99.9%). CONCLUSIONS AI demonstrated comparable, if not superior, performance to manual screening for patient enrollment into cancer clinical trials. As well, AI is highly efficient, requiring less time and human resources to screen patients. AI should be further investigated and implemented for patient recruitment into cancer clinical trials. Future research should validate the use of AI for clinical trials enrollment in less resource-rich regions and ensure broad inclusion for generalizability to all sexes, ages, and ethnicities.
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Affiliation(s)
- Ronald Chow
- Princess Margaret Cancer Centre, University Health Network, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- London Regional Cancer Program, London Health Sciences Centre, Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON, Canada
- Institute of Biomedical Engineering, Faculty of Applied Science and Engineering, University of Toronto, Toronto, ON, Canada
| | - Julie Midroni
- Princess Margaret Cancer Centre, University Health Network, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Jagdeep Kaur
- London Regional Cancer Program, London Health Sciences Centre, Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON, Canada
| | - Gabriel Boldt
- London Regional Cancer Program, London Health Sciences Centre, Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON, Canada
| | - Geoffrey Liu
- Princess Margaret Cancer Centre, University Health Network, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Lawson Eng
- Princess Margaret Cancer Centre, University Health Network, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Fei-Fei Liu
- Princess Margaret Cancer Centre, University Health Network, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University Health Network, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Michael Lock
- London Regional Cancer Program, London Health Sciences Centre, Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON, Canada
| | - Srinivas Raman
- Princess Margaret Cancer Centre, University Health Network, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
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Xie S. Perspectives on development of biomedical polymer materials in artificial intelligence age. J Biomater Appl 2023; 37:1355-1375. [PMID: 36629787 DOI: 10.1177/08853282231151822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Polymer materials are widely used in biomedicine, chemistry and material science, whose traditional preparations are mainly based on experience, intuition and conceptual insight, having been applied to the development of many new materials, but facing great challenges due to the vast design space for biomedical polymers. So far, the best way to solve these problems is to accelerate material design through artificial intelligence, especially machine learning. Herein, this paper will introduce several successful cases, and analyze the latest progress of machine learning in the field of biomedical polymers, then discuss the opportunities of this novel method. In particular, this paper summarizes the material database, open-source determination tools, molecular generation methods and machine learning models that have been used for biopolymer synthesis and property prediction. Overall, machine learning could be more effectively deployed on the material design of biomedical polymers, and it is expected to become an extensive driving force to meet the huge demand for customized designs.
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Affiliation(s)
- Shijin Xie
- 2281The University of Melbourne, Melbourne, VIC, Australia
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Bentwich I. Pharma's Bio-AI revolution. Drug Discov Today 2023; 28:103515. [PMID: 36736581 DOI: 10.1016/j.drudis.2023.103515] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 01/13/2023] [Accepted: 01/25/2023] [Indexed: 02/05/2023]
Abstract
Drug development has become unbearably slow and expensive. A key underlying problem is the clinical prediction challenge: the inability to predict which drug candidates will be safe in the human body and for whom. Recently, a dramatic regulatory change has removed FDA's mandated reliance on antiquated, ineffective animal studies. A new frontier is an integration of several disruptive technologies [machine learning (ML), patient-on-chip, real-time sensing, and stem cells], which when integrated, have the potential to address this challenge, drastically cutting the time and cost of developing drugs, and tailoring them to individual patients.
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Affiliation(s)
- Isaac Bentwich
- Quris-AI, 6 HaNatsiv Street, Tel Aviv-Yafo 6701033, Israel.
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Automation: A revolutionary vision of artificial intelligence in theranostics. Bull Cancer 2023; 110:233-241. [PMID: 36509576 DOI: 10.1016/j.bulcan.2022.10.009] [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: 08/02/2022] [Revised: 10/12/2022] [Accepted: 10/26/2022] [Indexed: 12/13/2022]
Abstract
The last two decades have witnessed an extraordinary evolution of automation and artificial intelligence (AI), which has become an integral part of our daily lives. Lately, AI has also been assimilated in the field of medicine to upgrade overall healthcare system and encourage personalized treatment. Theranostics literally meaning combination of diagnosis and therapeutics, is a targeted pharmacotherapy, based on specific targeted diagnostic tests. Numerous theranostic agents/biomarkers are available which can identify the most beneficial treatment, correct dose or predict response to a medicine, thus, maximizing drug efficacy, minimizing toxicity and providing informed treatment choice. For instance, a statistics based Cluster-FLIM technology provides precise data on drug-receptor binding behavior in biological tissues using fluorescence real experimental imaging. Automated Idylla™ qPCR System is another approach in oncology to determine the EGFR mutations at initial stage as well as during the treatment and also assists the oncologist in designing the treatment protocol. Recent incorporation of automation and AI in theranostics has brought a drastic change in early detection and treatment protocols for various diseases such as cancer and diabetes. Also, it leads to quick analysis of number of diverse experimental datum with accuracy. The approach mainly uses computer algorithms to unveil relevant and significant information from clinical data, thereby assisting in making accurate, logical and pertinent decisions. This review highlights the emerging uses/role of automation and AI in theranostics, technical difficulties and focuses on its future prospects to facilitate a patient specific, reliable and efficient pharmacotherapy.
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49
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Mazur H, Erbrich L, Quodbach J. Investigations into the use of machine learning to predict drug dosage form design to obtain desired release profiles for 3D printed oral medicines. Pharm Dev Technol 2023; 28:219-231. [PMID: 36715438 DOI: 10.1080/10837450.2023.2173778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Three-dimensional (3D) printing, digitalization, and artificial intelligence (AI) are gaining increasing interest in modern medicine. All three aspects are combined in personalized medicine where 3D-printed dosage forms are advantageous because of their variable geometry design. The geometry design can be used to determine the surface area to volume (SA/V) ratio, which affects drug release from the dosage forms. This study investigated artificial neural networks (ANN) to predict suitable geometries for the desired dose and release profile. Filaments with 5% API load and polyvinyl alcohol were 3D printed using Fused Deposition Modeling to provide a wide variety of geometries with different dosages and SA/V ratios. These were dissolved in vitro, and the API release profiles were described mathematically. Using these data, ANN architectures were designed with the goal of predicting a suitable dosage form geometry. Poor accuracies of 68.5% in the training and 44.4% in the test settings were achieved with a classification architecture. However, the SA/V ratio could be predicted accurately with a mean squared error loss of only 0.05. This study shows that the prediction of the SA/V ratio using AI works, but not of the exact geometry. For this purpose, a global database could be built with a range of geometries to simplify the prescription process.
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Affiliation(s)
- Hellen Mazur
- Institute of Pharmaceutics and Biopharmaceutics, Heinrich Heine University, Düsseldorf, Germany
| | - Leon Erbrich
- Institute of Pharmaceutics and Biopharmaceutics, Heinrich Heine University, Düsseldorf, Germany
| | - Julian Quodbach
- Institute of Pharmaceutics and Biopharmaceutics, Heinrich Heine University, Düsseldorf, Germany.,Department of Pharmaceutics, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands
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Wang X, He X, Wei J, Liu J, Li Y, Liu X. Application of artificial intelligence to the public health education. Front Public Health 2023; 10:1087174. [PMID: 36703852 PMCID: PMC9872201 DOI: 10.3389/fpubh.2022.1087174] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 12/15/2022] [Indexed: 01/11/2023] Open
Abstract
With the global outbreak of coronavirus disease 2019 (COVID-19), public health has received unprecedented attention. The cultivation of emergency and compound professionals is the general trend through public health education. However, current public health education is limited to traditional teaching models that struggle to balance theory and practice. Fortunately, the development of artificial intelligence (AI) has entered the stage of intelligent cognition. The introduction of AI in education has opened a new era of computer-assisted education, which brought new possibilities for teaching and learning in public health education. AI-based on big data not only provides abundant resources for public health research and management but also brings convenience for students to obtain public health data and information, which is conducive to the construction of introductory professional courses for students. In this review, we elaborated on the current status and limitations of public health education, summarized the application of AI in public health practice, and further proposed a framework for how to integrate AI into public health education curriculum. With the rapid technological advancements, we believe that AI will revolutionize the education paradigm of public health and help respond to public health emergencies.
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Affiliation(s)
- Xueyan Wang
- Laboratory of Integrative Medicine, Clinical Research Center for Breast, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xiujing He
- Laboratory of Integrative Medicine, Clinical Research Center for Breast, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Jiawei Wei
- Research Center for Nano-Biomaterials, Analytical and Testing Center, Sichuan University, Chengdu, Sichuan, China
| | - Jianping Liu
- The First People's Hospital of Yibin, Yibin, Sichuan, China
| | - Yuanxi Li
- Laboratory of Integrative Medicine, Clinical Research Center for Breast, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xiaowei Liu
- Laboratory of Integrative Medicine, Clinical Research Center for Breast, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China
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