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Wong M, Lim ZW, Pushpanathan K, Cheung CY, Wang YX, Chen D, Tham YC. Review of emerging trends and projection of future developments in large language models research in ophthalmology. Br J Ophthalmol 2024; 108:1362-1370. [PMID: 38164563 DOI: 10.1136/bjo-2023-324734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 11/14/2023] [Indexed: 01/03/2024]
Abstract
BACKGROUND Large language models (LLMs) are fast emerging as potent tools in healthcare, including ophthalmology. This systematic review offers a twofold contribution: it summarises current trends in ophthalmology-related LLM research and projects future directions for this burgeoning field. METHODS We systematically searched across various databases (PubMed, Europe PMC, Scopus and Web of Science) for articles related to LLM use in ophthalmology, published between 1 January 2022 and 31 July 2023. Selected articles were summarised, and categorised by type (editorial, commentary, original research, etc) and their research focus (eg, evaluating ChatGPT's performance in ophthalmology examinations or clinical tasks). FINDINGS We identified 32 articles meeting our criteria, published between January and July 2023, with a peak in June (n=12). Most were original research evaluating LLMs' proficiency in clinically related tasks (n=9). Studies demonstrated that ChatGPT-4.0 outperformed its predecessor, ChatGPT-3.5, in ophthalmology exams. Furthermore, ChatGPT excelled in constructing discharge notes (n=2), evaluating diagnoses (n=2) and answering general medical queries (n=6). However, it struggled with generating scientific articles or abstracts (n=3) and answering specific subdomain questions, especially those regarding specific treatment options (n=2). ChatGPT's performance relative to other LLMs (Google's Bard, Microsoft's Bing) varied by study design. Ethical concerns such as data hallucination (n=27), authorship (n=5) and data privacy (n=2) were frequently cited. INTERPRETATION While LLMs hold transformative potential for healthcare and ophthalmology, concerns over accountability, accuracy and data security remain. Future research should focus on application programming interface integration, comparative assessments of popular LLMs, their ability to interpret image-based data and the establishment of standardised evaluation frameworks.
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Affiliation(s)
| | - Zhi Wei Lim
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Krithi Pushpanathan
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Centre for Innovation and Precision Eye Health & Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Carol Y Cheung
- Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Ya Xing Wang
- Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital University of Medical Science, Beijing, China
| | - David Chen
- Centre for Innovation and Precision Eye Health & Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Ophthalmology, National University Hospital, Singapore
| | - Yih Chung Tham
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Centre for Innovation and Precision Eye Health & Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
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Jalal SM. Competency of Nurses on Electrocardiogram Monitoring and Interpretation in Selected Hospitals of Al-Ahsa, Saudi Arabia. ADVANCES IN MEDICAL EDUCATION AND PRACTICE 2024; 15:823-832. [PMID: 39280259 PMCID: PMC11402370 DOI: 10.2147/amep.s469116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2024] [Accepted: 09/09/2024] [Indexed: 09/18/2024]
Abstract
Purpose The ability of healthcare nurses to monitor and interpret electrocardiograms (ECGs) is essential for the identification of heart-related abnormalities and rapid treatment initiation. Lack of expertise of nurses in this competency may cause confusion and complications. The aims of this study were to assess the competency levels of nurses in monitoring and interpreting ECGs and to associate the knowledge with the demographic variables. Patients and Methods A descriptive cross-sectional study design was used. A total of 156 nurses were selected from five hospitals located in Al-Ahsa, Saudi Arabia by computer generated simple randomization. A structured self-administered tool for knowledge and observational checklist for skills regarding ECG monitoring and interpretation was used. Tool validity and reliability were tested. Descriptive and inferential statistics, including the mean, standard deviation, and chi-square test, were applied. Statistical significance was set at p < 0.05. Results Mean participant age was 32.59 ± 5.35 years, 30% of nurses had adequate knowledge, and the overall mean score was 17 ± 3.97. Seventy-two (46.2%) nurses correctly interpreted the ECG axis, and 76 (48.7%) could identify the Q-T interval on ECG strips. Significant associations of nurse knowledge level were detected with age (p < 0.0208), education (p < 0.0001), experience (p < 0.0001), nationality (p < 0.0002), and hospital type (p < 0.0018). Conclusion Most nurses had a low level of expertise in interpreting ECGs, and it will be crucial for them to improve their competence. Adequate training on ECG interpretation will enhance the proficiency of nurses and help provide appropriate care and life-saving measures to patients in emergency situations.
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Affiliation(s)
- Sahbanathul Missiriya Jalal
- Department of Nursing, College of Applied Medical Sciences, King Faisal University, Al-Ahsa, 31982, Saudi Arabia
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3
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Obimba DC, Esteva C, Nzouatcham Tsicheu EN, Wong R. Effectiveness of Artificial Intelligence Technologies in Cancer Treatment for Older Adults: A Systematic Review. J Clin Med 2024; 13:4979. [PMID: 39274201 PMCID: PMC11396550 DOI: 10.3390/jcm13174979] [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: 06/20/2024] [Revised: 07/29/2024] [Accepted: 08/21/2024] [Indexed: 09/16/2024] Open
Abstract
Background: Aging is a multifaceted process that may lead to an increased risk of developing cancer. Artificial intelligence (AI) applications in clinical cancer research may optimize cancer treatments, improve patient care, and minimize risks, prompting AI to receive high levels of attention in clinical medicine. This systematic review aims to synthesize current articles about the effectiveness of artificial intelligence in cancer treatments for older adults. Methods: We conducted a systematic review by searching CINAHL, PsycINFO, and MEDLINE via EBSCO. We also conducted forward and backward hand searching for a comprehensive search. Eligible studies included a study population of older adults (60 and older) with cancer, used AI technology to treat cancer, and were published in a peer-reviewed journal in English. This study was registered on PROSPERO (CRD42024529270). Results: This systematic review identified seven articles focusing on lung, breast, and gastrointestinal cancers. They were predominantly conducted in the USA (42.9%), with others from India, China, and Germany. The measures of overall and progression-free survival, local control, and treatment plan concordance suggested that AI interventions were equally or less effective than standard care in treating older adult cancer patients. Conclusions: Despite promising initial findings, the utility of AI technologies in cancer treatment for older adults remains in its early stages, as further developments are necessary to enhance accuracy, consistency, and reliability for broader clinical use.
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Affiliation(s)
- Doris C Obimba
- Department of Public Health and Preventive Medicine, Norton College of Medicine, SUNY Upstate Medical University, Syracuse, NY 13210, USA
| | - Charlene Esteva
- Department of Public Health and Preventive Medicine, Norton College of Medicine, SUNY Upstate Medical University, Syracuse, NY 13210, USA
| | - Eurika N Nzouatcham Tsicheu
- Department of Public Health and Preventive Medicine, Norton College of Medicine, SUNY Upstate Medical University, Syracuse, NY 13210, USA
| | - Roger Wong
- Department of Public Health and Preventive Medicine, Norton College of Medicine, SUNY Upstate Medical University, Syracuse, NY 13210, USA
- Department of Geriatrics, SUNY Upstate Medical University, Syracuse, NY 13210, USA
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Nechita LC, Nechita A, Voipan AE, Voipan D, Debita M, Fulga A, Fulga I, Musat CL. AI-Enhanced ECG Applications in Cardiology: Comprehensive Insights from the Current Literature with a Focus on COVID-19 and Multiple Cardiovascular Conditions. Diagnostics (Basel) 2024; 14:1839. [PMID: 39272624 PMCID: PMC11394310 DOI: 10.3390/diagnostics14171839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2024] [Revised: 08/17/2024] [Accepted: 08/22/2024] [Indexed: 09/15/2024] Open
Abstract
The application of artificial intelligence (AI) in electrocardiography is revolutionizing cardiology and providing essential insights into the consequences of the COVID-19 pandemic. This comprehensive review explores AI-enhanced ECG (AI-ECG) applications in risk prediction and diagnosis of heart diseases, with a dedicated chapter on COVID-19-related complications. Introductory concepts on AI and machine learning (ML) are explained to provide a foundational understanding for those seeking knowledge, supported by examples from the literature and current practices. We analyze AI and ML methods for arrhythmias, heart failure, pulmonary hypertension, mortality prediction, cardiomyopathy, mitral regurgitation, hypertension, pulmonary embolism, and myocardial infarction, comparing their effectiveness from both medical and AI perspectives. Special emphasis is placed on AI applications in COVID-19 and cardiology, including detailed comparisons of different methods, identifying the most suitable AI approaches for specific medical applications and analyzing their strengths, weaknesses, accuracy, clinical relevance, and key findings. Additionally, we explore AI's role in the emerging field of cardio-oncology, particularly in managing chemotherapy-induced cardiotoxicity and detecting cardiac masses. This comprehensive review serves as both an insightful guide and a call to action for further research and collaboration in the integration of AI in cardiology, aiming to enhance precision medicine and optimize clinical decision-making.
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Affiliation(s)
- Luiza Camelia Nechita
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 800008 Galati, Romania
| | - Aurel Nechita
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 800008 Galati, Romania
| | - Andreea Elena Voipan
- Faculty of Automation, Computers, Electrical Engineering and Electronics, Dunarea de Jos University of Galati, 800008 Galati, Romania
| | - Daniel Voipan
- Faculty of Automation, Computers, Electrical Engineering and Electronics, Dunarea de Jos University of Galati, 800008 Galati, Romania
| | - Mihaela Debita
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 800008 Galati, Romania
| | - Ana Fulga
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 800008 Galati, Romania
| | - Iuliu Fulga
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 800008 Galati, Romania
| | - Carmina Liana Musat
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 800008 Galati, Romania
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Ose B, Sattar Z, Gupta A, Toquica C, Harvey C, Noheria A. Artificial Intelligence Interpretation of the Electrocardiogram: A State-of-the-Art Review. Curr Cardiol Rep 2024; 26:561-580. [PMID: 38753291 DOI: 10.1007/s11886-024-02062-1] [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] [Accepted: 04/17/2024] [Indexed: 06/26/2024]
Abstract
PURPOSE OF REVIEW Artificial intelligence (AI) is transforming electrocardiography (ECG) interpretation. AI diagnostics can reach beyond human capabilities, facilitate automated access to nuanced ECG interpretation, and expand the scope of cardiovascular screening in the population. AI can be applied to the standard 12-lead resting ECG and single-lead ECGs in external monitors, implantable devices, and direct-to-consumer smart devices. We summarize the current state of the literature on AI-ECG. RECENT FINDINGS Rhythm classification was the first application of AI-ECG. Subsequently, AI-ECG models have been developed for screening structural heart disease including hypertrophic cardiomyopathy, cardiac amyloidosis, aortic stenosis, pulmonary hypertension, and left ventricular systolic dysfunction. Further, AI models can predict future events like development of systolic heart failure and atrial fibrillation. AI-ECG exhibits potential in acute cardiac events and non-cardiac applications, including acute pulmonary embolism, electrolyte abnormalities, monitoring drugs therapy, sleep apnea, and predicting all-cause mortality. Many AI models in the domain of cardiac monitors and smart watches have received Food and Drug Administration (FDA) clearance for rhythm classification, while others for identification of cardiac amyloidosis, pulmonary hypertension and left ventricular dysfunction have received breakthrough device designation. As AI-ECG models continue to be developed, in addition to regulatory oversight and monetization challenges, thoughtful clinical implementation to streamline workflows, avoiding information overload and overwhelming of healthcare systems with false positive results is necessary. Research to demonstrate and validate improvement in healthcare efficiency and improved patient outcomes would be required before widespread adoption of any AI-ECG model.
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Affiliation(s)
- Benjamin Ose
- The University of Kansas School of Medicine, Kansas City, KS, USA
| | - Zeeshan Sattar
- Division of General and Hospital Medicine, The University of Kansas Medical Center, Kansas City, KS, USA
| | - Amulya Gupta
- Department of Cardiovascular Medicine, The University of Kansas Medical Center, Kansas City, KS, USA
- Program for AI & Research in Cardiovascular Medicine (PARC), The University of Kansas Medical Center, Kansas City, KS, USA
| | | | - Chris Harvey
- Department of Cardiovascular Medicine, The University of Kansas Medical Center, Kansas City, KS, USA
- Program for AI & Research in Cardiovascular Medicine (PARC), The University of Kansas Medical Center, Kansas City, KS, USA
| | - Amit Noheria
- Department of Cardiovascular Medicine, The University of Kansas Medical Center, Kansas City, KS, USA.
- Program for AI & Research in Cardiovascular Medicine (PARC), The University of Kansas Medical Center, Kansas City, KS, USA.
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Stamate E, Piraianu AI, Ciobotaru OR, Crassas R, Duca O, Fulga A, Grigore I, Vintila V, Fulga I, Ciobotaru OC. Revolutionizing Cardiology through Artificial Intelligence-Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment-A Comprehensive Review of the Past 5 Years. Diagnostics (Basel) 2024; 14:1103. [PMID: 38893630 PMCID: PMC11172021 DOI: 10.3390/diagnostics14111103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 05/12/2024] [Accepted: 05/23/2024] [Indexed: 06/21/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, this fact being supported by the exponential increase in the number of publications in which the algorithms play an important role in data analysis, pattern discovery, identification of anomalies, and therapeutic decision making. Furthermore, with technological development, there have appeared new models of machine learning (ML) and deep learning (DP) that are capable of exploring various applications of AI in cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional cardiology, and many others. In this sense, the present article aims to provide a general vision of the current state of AI use in cardiology. RESULTS We identified and included a subset of 200 papers directly relevant to the current research covering a wide range of applications. Thus, this paper presents AI applications in cardiovascular imaging, arithmology, clinical or emergency cardiology, cardiovascular prevention, and interventional procedures in a summarized manner. Recent studies from the highly scientific literature demonstrate the feasibility and advantages of using AI in different branches of cardiology. CONCLUSIONS The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice. From predicting the risk of sudden death or the ability to respond to cardiac resynchronization therapy to the diagnosis of pulmonary embolism or the early detection of valvular diseases, AI algorithms have shown their potential to mitigate human error and provide feasible solutions. At the same time, limits imposed by the small samples studied are highlighted alongside the challenges presented by ethical implementation; these relate to legal implications regarding responsibility and decision making processes, ensuring patient confidentiality and data security. All these constitute future research directions that will allow the integration of AI in the progress of cardiology.
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Affiliation(s)
- Elena Stamate
- Department of Cardiology, Emergency University Hospital of Bucharest, 050098 Bucharest, Romania; (E.S.); (V.V.)
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
| | - Alin-Ionut Piraianu
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
| | - Oana Roxana Ciobotaru
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Railway Hospital Galati, 800223 Galati, Romania
| | - Rodica Crassas
- Emergency County Hospital Braila, 810325 Braila, Romania;
| | - Oana Duca
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Emergency County Hospital Braila, 810325 Braila, Romania;
| | - Ana Fulga
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei Street, 800578 Galati, Romania
| | - Ionica Grigore
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Emergency County Hospital Braila, 810325 Braila, Romania;
| | - Vlad Vintila
- Department of Cardiology, Emergency University Hospital of Bucharest, 050098 Bucharest, Romania; (E.S.); (V.V.)
- Clinical Department of Cardio-Thoracic Pathology, University of Medicine and Pharmacy “Carol Davila” Bucharest, 37 Dionisie Lupu Street, 4192910 Bucharest, Romania
| | - Iuliu Fulga
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei Street, 800578 Galati, Romania
| | - Octavian Catalin Ciobotaru
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Railway Hospital Galati, 800223 Galati, Romania
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Smaranda AM, Drăgoiu TS, Caramoci A, Afetelor AA, Ionescu AM, Bădărău IA. Artificial Intelligence in Sports Medicine: Reshaping Electrocardiogram Analysis for Athlete Safety-A Narrative Review. Sports (Basel) 2024; 12:144. [PMID: 38921838 PMCID: PMC11209071 DOI: 10.3390/sports12060144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2024] [Revised: 05/22/2024] [Accepted: 05/23/2024] [Indexed: 06/27/2024] Open
Abstract
Artificial Intelligence (AI) is redefining electrocardiogram (ECG) analysis in pre-participation examination (PPE) of athletes, enhancing the detection and monitoring of cardiovascular health. Cardiovascular concerns, including sudden cardiac death, pose significant risks during sports activities. Traditional ECG, essential yet limited, often fails to distinguish between benign cardiac adaptations and serious conditions. This narrative review investigates the application of machine learning (ML) and deep learning (DL) in ECG interpretation, aiming to improve the detection of arrhythmias, channelopathies, and hypertrophic cardiomyopathies. A literature review over the past decade, sourcing from PubMed and Google Scholar, highlights the growing adoption of AI in sports medicine for its precision and predictive capabilities. AI algorithms excel at identifying complex cardiac patterns, potentially overlooked by traditional methods, and are increasingly integrated into wearable technologies for continuous monitoring. Overall, by offering a comprehensive overview of current innovations and outlining future advancements, this review supports sports medicine professionals in merging traditional screening methods with state-of-the-art AI technologies. This approach aims to enhance diagnostic accuracy and efficiency in athlete care, promoting early detection and more effective monitoring through AI-enhanced ECG analysis within athlete PPEs.
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Affiliation(s)
- Alina Maria Smaranda
- Discipline of Sports Medicine, Carol Davila University of Medicine and Pharmacy, 050474 Bucharest, Romania; (A.C.); (A.M.I.)
- Sports Medicine Resident Doctor, Carol Davila University of Medicine and Pharmacy, 050474 Bucharest, Romania;
| | - Teodora Simina Drăgoiu
- Sports Medicine Resident Doctor, Carol Davila University of Medicine and Pharmacy, 050474 Bucharest, Romania;
| | - Adela Caramoci
- Discipline of Sports Medicine, Carol Davila University of Medicine and Pharmacy, 050474 Bucharest, Romania; (A.C.); (A.M.I.)
- National Institute of Sports Medicine, 022103 Bucharest, Romania
| | - Adelina Ana Afetelor
- Department of Thoracic Surgery, “Marius Nasta” National Institute of Pneumology, 050159 Bucharest, Romania;
| | - Anca Mirela Ionescu
- Discipline of Sports Medicine, Carol Davila University of Medicine and Pharmacy, 050474 Bucharest, Romania; (A.C.); (A.M.I.)
- National Institute of Sports Medicine, 022103 Bucharest, Romania
| | - Ioana Anca Bădărău
- Department of Physiology, Carol Davila University of Medicine and Pharmacy, 050474 Bucharest, Romania;
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Kenyon CR, Pietri MP, Rosenthal JL, Arsanjani R, Ayoub C. Artificial Intelligence Assists in the Early Identification of Cardiac Amyloidosis. J Pers Med 2024; 14:559. [PMID: 38929780 PMCID: PMC11205191 DOI: 10.3390/jpm14060559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Revised: 05/18/2024] [Accepted: 05/21/2024] [Indexed: 06/28/2024] Open
Abstract
A 69-year-old female presented with symptomatic atrial fibrillation. Cardiac amyloidosis was suspected due to an artificial intelligence clinical tool applied to the presenting electrocardiogram predicting a high probability for amyloidosis, and the subsequent unexpected finding of left atrial appendage thrombus reinforced this clinical suspicion. This facilitated an early diagnosis by the biopsy of AL cardiac amyloidosis and the prompt initiation of targeted therapy. This case highlights the utilization of an AI clinical tool and its impact on clinical care, particularly for the early detection of a rare and difficult to diagnose condition where early therapy is critical.
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Affiliation(s)
| | | | | | | | - Chadi Ayoub
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USA; (C.R.K.); (M.P.P.); (J.L.R.); (R.A.)
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Thießen N, Schnabel R. [Diagnosis of acute coronary syndrome]. Dtsch Med Wochenschr 2024; 149:488-495. [PMID: 38621682 DOI: 10.1055/a-2163-2586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/17/2024]
Abstract
Acute coronary syndrome is one of the most important differential diagnostic considerations in emergency medicine. It describes the constellation of newly occurring clinical symptoms, often accompanied by typical 12-lead ECG changes and the release of cardiac troponins. The spectrum includes unstable angina pectoris, non-ST-segment elevation myocardial infarction (NSTEMI), and ST-segment elevation myocardial infarction (STEMI). It is important to consistently carry out the diagnostic steps that are crucial for further therapeutic procedures to avoid delaying life-saving invasive coronary diagnostics, without losing sight of the diverse, sometimes time-critical differential diagnoses. Anamnesis and clinical examination form the basis of the further procedure. Further developments of biomarker assays with personalized limit values, new imaging modalities with ever higher resolution and faster imaging methods as well as advances in automated ECG analysis with integration of all findings through artificial intelligence will continue to offer many optimization options in the future diagnosis of acute coronary syndrome.
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Yavuz YE, Kahraman F. Evaluation of the prediagnosis and management of ChatGPT-4.0 in clinical cases in cardiology. Future Cardiol 2024; 20:197-207. [PMID: 39049771 DOI: 10.1080/14796678.2024.2348898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 04/25/2024] [Indexed: 07/27/2024] Open
Abstract
Aim: Evaluation of the performance of ChatGPT-4.0 in providing prediagnosis and treatment plans for cardiac clinical cases by expert cardiologists. Methods: 20 cardiology clinical cases developed by experienced cardiologists were divided into two groups according to preparation methods. Cases were reviewed and analyzed by the ChatGPT-4.0 program, and analyses of ChatGPT were then sent to cardiologists. Eighteen expert cardiologists evaluated the quality of ChatGPT-4.0 responses using Likert and Global quality scales. Results: Physicians rated case difficulty (median 2.00), revealing high ChatGPT-4.0 agreement to differential diagnoses (median 5.00). Management plans received a median score of 4, indicating good quality. Regardless of the difficulty of the cases, ChatGPT-4.0 showed similar performance in differential diagnosis (p: 0.256) and treatment plans (p: 0.951). Conclusion: ChatGPT-4.0 excels at delivering accurate management and demonstrates its potential as a valuable clinical decision support tool in cardiology.
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Affiliation(s)
- Yunus Emre Yavuz
- Department of Cardiology, Siirt Training & Research Hospital, Siirt, 56100, Turkey
| | - Fatih Kahraman
- Department of Cardiology, Kütahya Evliya Çelebi Training & Research Hospital, Kütahya, 43000, Turkey
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Neri L, Gallelli I, Dall'Olio M, Lago J, Borghi C, Diemberger I, Corazza I. Validation of a New and Straightforward Algorithm to Evaluate Signal Quality during ECG Monitoring with Wearable Devices Used in a Clinical Setting. Bioengineering (Basel) 2024; 11:222. [PMID: 38534496 DOI: 10.3390/bioengineering11030222] [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: 01/12/2024] [Revised: 02/03/2024] [Accepted: 02/23/2024] [Indexed: 03/28/2024] Open
Abstract
BACKGROUND Wearable devices represent a new approach for monitoring key clinical parameters, such as ECG signals, for research and health purposes. These devices could outcompete medical devices in terms of affordability and use in out-clinic settings, allowing remote monitoring. The major limitation, especially when compared to implantable devices, is the presence of artifacts. Several authors reported a relevant percentage of recording time with poor/unusable traces for ECG, potentially hampering the use of these devices for this purpose. For this reason, it is of the utmost importance to develop a simple and inexpensive system enabling the user of the wearable devices to have immediate feedback on the quality of the acquired signal, allowing for real-time correction. METHODS A simple algorithm that can work in real time to verify the quality of the ECG signal (acceptable and unacceptable) was validated. Based on simple statistical parameters, the algorithm was blindly tested by comparison with ECG tracings previously classified by two expert cardiologists. RESULTS The classifications of 7200 10s-signal samples acquired on 20 patients with a commercial wearable ECG monitor were compared. The algorithm has an overall efficiency of approximately 95%, with a sensitivity of 94.7% and a specificity of 95.3%. CONCLUSIONS The results demonstrate that even a simple algorithm can be used to classify signal coarseness, and this could allow real-time intervention by the subject or the technician.
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Affiliation(s)
- Luca Neri
- Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Medical and Surgical Sciences, University of Bologna, 40126 Bologna, Italy
| | | | | | - Jessica Lago
- Department of Medical and Surgical Sciences, University of Bologna, 40126 Bologna, Italy
| | - Claudio Borghi
- Department of Medical and Surgical Sciences, University of Bologna, 40126 Bologna, Italy
- IRCCS AOU, Policlinico di S. Orsola, 40138 Bologna, Italy
| | - Igor Diemberger
- Department of Medical and Surgical Sciences, University of Bologna, 40126 Bologna, Italy
- IRCCS AOU, Policlinico di S. Orsola, 40138 Bologna, Italy
| | - Ivan Corazza
- Department of Medical and Surgical Sciences, University of Bologna, 40126 Bologna, Italy
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Appel JM. Artificial intelligence in medicine and the negative outcome penalty paradox. JOURNAL OF MEDICAL ETHICS 2024:jme-2023-109848. [PMID: 38290853 DOI: 10.1136/jme-2023-109848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Accepted: 01/18/2024] [Indexed: 02/01/2024]
Abstract
Artificial intelligence (AI) holds considerable promise for transforming clinical diagnostics. While much has been written both about public attitudes toward the use of AI tools in medicine and about uncertainty regarding legal liability that may be delaying its adoption, the interface of these two issues has so far drawn less attention. However, understanding this interface is essential to determining how jury behaviour is likely to influence adoption of AI by physicians. One distinctive concern identified in this paper is a 'negative outcome penalty paradox' (NOPP) in which physicians risk being penalised by juries in cases with negative outcomes, whether they overrule AI determinations or accept them. The paper notes three reasons why AI in medicine is uniquely susceptible to the NOPP and urges serious further consideration of this complex dilemma.
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Affiliation(s)
- Jacob M Appel
- Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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Chlorogiannis DD, Apostolos A, Chlorogiannis A, Palaiodimos L, Giannakoulas G, Pargaonkar S, Xesfingi S, Kokkinidis DG. The Role of ChatGPT in the Advancement of Diagnosis, Management, and Prognosis of Cardiovascular and Cerebrovascular Disease. Healthcare (Basel) 2023; 11:2906. [PMID: 37958050 PMCID: PMC10648908 DOI: 10.3390/healthcare11212906] [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: 10/03/2023] [Revised: 10/24/2023] [Accepted: 11/04/2023] [Indexed: 11/15/2023] Open
Abstract
Cardiovascular and cerebrovascular disease incidence has risen mainly due to poor control of preventable risk factors and still constitutes a significant financial and health burden worldwide. ChatGPT is an artificial intelligence language-based model developed by OpenAI. Due to the model's unique cognitive capabilities beyond data processing and the production of high-quality text, there has been a surge of research interest concerning its role in the scientific community and contemporary clinical practice. To fully exploit ChatGPT's potential benefits and reduce its possible misuse, extreme caution must be taken to ensure its implications ethically and equitably. In this narrative review, we explore the language model's possible applications and limitations while emphasizing its potential value for diagnosing, managing, and prognosis of cardiovascular and cerebrovascular disease.
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Affiliation(s)
| | - Anastasios Apostolos
- First Department of Cardiology, School of Medicine, National Kapodistrian University of Athens, Hippokrateion General Hospital of Athens, 115 27 Athens, Greece;
| | - Anargyros Chlorogiannis
- Department of Health Economics, Policy and Management, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Leonidas Palaiodimos
- Division of Hospital Medicine, Jacobi Medical Center, NYC H+H, Albert Einstein College of Medicine, New York, NY 10461, USA; (L.P.); (S.P.)
| | - George Giannakoulas
- Department of Cardiology, AHEPA University Hospital, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece;
| | - Sumant Pargaonkar
- Division of Hospital Medicine, Jacobi Medical Center, NYC H+H, Albert Einstein College of Medicine, New York, NY 10461, USA; (L.P.); (S.P.)
| | - Sofia Xesfingi
- Department of Economics, University of Piraeus, 185 34 Piraeus, Greece
| | - Damianos G. Kokkinidis
- Section of Cardiovascular Medicine, Yale University School of Medicine, New Haven, CT 06510, USA
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Garg RK, Urs VL, Agarwal AA, Chaudhary SK, Paliwal V, Kar SK. Exploring the role of ChatGPT in patient care (diagnosis and treatment) and medical research: A systematic review. Health Promot Perspect 2023; 13:183-191. [PMID: 37808939 PMCID: PMC10558973 DOI: 10.34172/hpp.2023.22] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 07/06/2023] [Indexed: 10/10/2023] Open
Abstract
Background ChatGPT is an artificial intelligence based tool developed by OpenAI (California, USA). This systematic review examines the potential of ChatGPT in patient care and its role in medical research. Methods The systematic review was done according to the PRISMA guidelines. Embase, Scopus, PubMed and Google Scholar data bases were searched. We also searched preprint data bases. Our search was aimed to identify all kinds of publications, without any restrictions, on ChatGPT and its application in medical research, medical publishing and patient care. We used search term "ChatGPT". We reviewed all kinds of publications including original articles, reviews, editorial/ commentaries, and even letter to the editor. Each selected records were analysed using ChatGPT and responses generated were compiled in a table. The word table was transformed in to a PDF and was further analysed using ChatPDF. Results We reviewed full texts of 118 articles. ChatGPT can assist with patient enquiries, note writing, decision-making, trial enrolment, data management, decision support, research support, and patient education. But the solutions it offers are usually insufficient and contradictory, raising questions about their originality, privacy, correctness, bias, and legality. Due to its lack of human-like qualities, ChatGPT's legitimacy as an author is questioned when used for academic writing. ChatGPT generated contents have concerns with bias and possible plagiarism. Conclusion Although it can help with patient treatment and research, there are issues with accuracy, authorship, and bias. ChatGPT can serve as a "clinical assistant" and be a help in research and scholarly writing.
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Affiliation(s)
| | - Vijeth L Urs
- Department of Neurology, King George’s Medical University, Lucknow, India
| | | | | | - Vimal Paliwal
- Department of Neurology, Sanjay Gandhi Institute of Medical Sciences, Lucknow, India
| | - Sujita Kumar Kar
- Department of Psychiatry, King George’s Medical University, Lucknow, India
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Chi CY, Moghadas-Dastjerdi H, Winkler A, Ao S, Chen YP, Wang LW, Su PI, Lin WS, Tsai MS, Huang CH. Clinical Validation of Explainable Deep Learning Model for Predicting the Mortality of In-Hospital Cardiac Arrest Using Diagnosis Codes of Electronic Health Records. Rev Cardiovasc Med 2023; 24:265. [PMID: 39076399 PMCID: PMC11270098 DOI: 10.31083/j.rcm2409265] [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: 03/20/2023] [Revised: 06/12/2023] [Accepted: 06/26/2023] [Indexed: 07/31/2024] Open
Abstract
Background Using deep learning for disease outcome prediction is an approach that has made large advances in recent years. Notwithstanding its excellent performance, clinicians are also interested in learning how input affects prediction. Clinical validation of explainable deep learning models is also as yet unexplored. This study aims to evaluate the performance of Deep SHapley Additive exPlanations (D-SHAP) model in accurately identifying the diagnosis code associated with the highest mortality risk. Methods Incidences of at least one in-hospital cardiac arrest (IHCA) for 168,693 patients as well as 1,569,478 clinical records were extracted from Taiwan's National Health Insurance Research Database. We propose a D-SHAP model to provide insights into deep learning model predictions. We trained a deep learning model to predict the 30-day mortality likelihoods of IHCA patients and used D-SHAP to see how the diagnosis codes affected the model's predictions. Physicians were asked to annotate a cardiac arrest dataset and provide expert opinions, which we used to validate our proposed method. A 1-to-4-point annotation of each record (current decision) along with four previous records (historical decision) was used to validate the current and historical D-SHAP values. Results A subset consisting of 402 patients with at least one cardiac arrest record was randomly selected from the IHCA cohort. The median age was 72 years, with mean and standard deviation of 69 ± 17 years. Results indicated that D-SHAP can identify the cause of mortality based on the diagnosis codes. The top five most important diagnosis codes, namely respiratory failure, sepsis, pneumonia, shock, and acute kidney injury were consistent with the physician's opinion. Some diagnoses, such as urinary tract infection, showed a discrepancy between D-SHAP and clinical judgment due to the lower frequency of the disease and its occurrence in combination with other comorbidities. Conclusions The D-SHAP framework was found to be an effective tool to explain deep neural networks and identify most of the important diagnoses for predicting patients' 30-day mortality. However, physicians should always carefully consider the structure of the original database and underlying pathophysiology.
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Affiliation(s)
- Chien-Yu Chi
- Department of Emergency Medicine, National Taiwan University Hospital Yunlin Branch, 640 Yunlin, Taiwan
| | | | - Adrian Winkler
- Knowtions Research Inc., Toronto, Ontario M5J 2S1, Canada
| | - Shuang Ao
- Knowtions Research Inc., Toronto, Ontario M5J 2S1, Canada
| | - Yen-Pin Chen
- Department of Emergency Medicine, National Taiwan University, 100 Taipei, Taiwan
| | - Liang-Wei Wang
- Department of Emergency Medicine, National Taiwan University, 100 Taipei, Taiwan
| | - Pei-I Su
- Department of Emergency Medicine, National Taiwan University, 100 Taipei, Taiwan
| | - Wei-Shu Lin
- Department of Emergency Medicine, National Taiwan University, 100 Taipei, Taiwan
| | - Min-Shan Tsai
- Department of Emergency Medicine, National Taiwan University, 100 Taipei, Taiwan
| | - Chien-Hua Huang
- Department of Emergency Medicine, National Taiwan University, 100 Taipei, Taiwan
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