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Boucher A, Peters M, Jones GB. How Digital Solutions Might Provide a World of New Opportunities for Holistic and Empathic Support of Patients with Hidradenitis Suppurativa. Dermatol Ther (Heidelb) 2024; 14:1975-1981. [PMID: 39042318 PMCID: PMC11333405 DOI: 10.1007/s13555-024-01234-9] [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: 04/18/2024] [Accepted: 07/08/2024] [Indexed: 07/24/2024] Open
Abstract
Hidradenitis suppurativa (HS) is a complex chronic relapsing inflammatory condition anchored in the hair follicle wherein painful abscesses, nodules, and tunnels form under the skin with the potential for intermittent pus drainage and tissue scarring. Current estimates of incidence are 1-4% globally with the disease three times more prevalent in women and higher rates among Black populations. Patients with HS are also more likely to suffer from depression, anxiety, and loneliness underscoring the need for carefully approached strategies on disease awareness and interventions. Delays in formal diagnosis, which have been estimated at 7-10 years on average, impede timely provision of optimal care. Despite best intent, when patients present at a physician's office, stigmas relating to physical appearance can be exacerbated by negative interactions experienced by patients. In addition to long wait times and the dearth of available HS expert dermatology professionals, patients perceive heightened physician focus on two of the HS flare risk factors (smoking and body mass index [BMI]) as negatively impacting their care. Given the need for continual, personal, and sensitive patient support, herein we advocate for re-examination of approach to care and the leveraging of highly personalized digital support solutions. New medications which can directly or indirectly control elements of the disease and its comorbidities are also entering the marketplace. Collectively, we posit that these new developments provide opportunity for a holistic approach for patients with HS, leading to long-term engagement and improved outcomes.
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Affiliation(s)
- Annie Boucher
- Novartis Pharma AG, Lichtstrasse 35, 4056, Basel, Switzerland
| | - Martin Peters
- Novartis Pharma AG, Lichtstrasse 35, 4056, Basel, Switzerland
| | - Graham B Jones
- Novartis Pharmaceuticals, 250 Massachusetts Avenue, Cambridge, MA, 02139, USA.
- Clinical and Translational Science Institute, Tufts University Medical Center, 800 Washington Street, Boston, MA, 02111, USA.
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Takefuji Y. Generative AI for diabetologists: a concise tutorial on dataset analysis. J Diabetes Metab Disord 2024; 23:1419-1423. [PMID: 38932811 PMCID: PMC11196448 DOI: 10.1007/s40200-023-01377-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 12/18/2023] [Indexed: 06/28/2024]
Abstract
Objectives This paper aims to provide a tutorial for diabetologists and endocrinologists on using generative AI to analyze datasets. It is designed to be accessible to those new to generative AI or without programming experience. Methods The paper presents three examples using a real diabetes dataset. The examples demonstrate binary classification with the 'Group' variable, cross-validation analysis, and NT-proBNP regression. Results The binary classification achieved a prediction accuracy of nearly 0.9. However, the NT-proBNP regression was not successful with this dataset. The calculated R-squared values indicate a poor fit between the predicted model and the raw data. Conclusions The unsuccessful NT-proBNP regression may be due to insufficient training data or the need for additional determinants. The dataset may be too small or new metrics may be required to accurately predict NT-proBNP regression values. It is crucial for users to verify the generated codes to ensure that they can achieve their desired objectives.
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Affiliation(s)
- Yoshiyasu Takefuji
- Faculty of Data Science, Musashino University, 3-3-3 Ariake Koto-Ku, Tokyo, 135-8181 Japan
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3
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Meşe İ, Altıntaş Taşlıçay C, Kuzan BN, Kuzan TY, Sivrioğlu AK. Educating the next generation of radiologists: a comparative report of ChatGPT and e-learning resources. Diagn Interv Radiol 2024; 30:163-174. [PMID: 38145370 PMCID: PMC11095068 DOI: 10.4274/dir.2023.232496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 11/29/2023] [Indexed: 12/26/2023]
Abstract
Rapid technological advances have transformed medical education, particularly in radiology, which depends on advanced imaging and visual data. Traditional electronic learning (e-learning) platforms have long served as a cornerstone in radiology education, offering rich visual content, interactive sessions, and peer-reviewed materials. They excel in teaching intricate concepts and techniques that necessitate visual aids, such as image interpretation and procedural demonstrations. However, Chat Generative Pre-Trained Transformer (ChatGPT), an artificial intelligence (AI)-powered language model, has made its mark in radiology education. It can generate learning assessments, create lesson plans, act as a round-the-clock virtual tutor, enhance critical thinking, translate materials for broader accessibility, summarize vast amounts of information, and provide real-time feedback for any subject, including radiology. Concerns have arisen regarding ChatGPT's data accuracy, currency, and potential biases, especially in specialized fields such as radiology. However, the quality, accessibility, and currency of e-learning content can also be imperfect. To enhance the educational journey for radiology residents, the integration of ChatGPT with expert-curated e-learning resources is imperative for ensuring accuracy and reliability and addressing ethical concerns. While AI is unlikely to entirely supplant traditional radiology study methods, the synergistic combination of AI with traditional e-learning can create a holistic educational experience.
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Affiliation(s)
- İsmail Meşe
- University of Health Sciences Türkiye, Erenköy Mental Health and Neurology Training and Research Hospital, Clinic of Radiology, İstanbul, Türkiye
| | | | - Beyza Nur Kuzan
- Kartal Dr. Lütfi Kırdar City Hospital, Clinic of Radiology, İstanbul, Türkiye
| | - Taha Yusuf Kuzan
- Sancaktepe Şehit Prof. Dr. İlhan Varank Training and Research Hospital, Clinic of Radiology, İstanbul, Türkiye
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Koh MCY, Ngiam JN, Yong J, Tambyah PA, Archuleta S. The role of an artificial intelligence model in antiretroviral therapy counselling and advice for people living with HIV. HIV Med 2024; 25:504-508. [PMID: 38169077 DOI: 10.1111/hiv.13604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 12/14/2023] [Indexed: 01/05/2024]
Abstract
OBJECTIVES People living with HIV may find personalized access to accurate information on antiretroviral therapy (ART) challenging given the stigma and costs potentially associated with attending physical consultations. Artificial intelligence (AI) chatbots such as ChatGPT may help to lower barriers to accessing information addressing concerns around ART initiation. However, the safety and accuracy of the information provided remains to be studied. METHODS We instructed ChatGPT to answer questions that people living with HIV frequently ask about ART, covering i) knowledge of and access to ART; ii) ART initiation, side effects, and adherence, and iii) general sexual health practices while receiving ART. We checked the accuracy of the advice against international HIV clinical practice guidelines. RESULTS ChatGPT answered all questions accurately and comprehensively. It recognized potentially life-threatening scenarios such as abacavir hypersensitivity reaction and gave appropriate advice. However, in certain contexts, such as specific geographic locations or for pregnant individuals, the advice lacked specificity to an individual's unique circumstances and may be inadequate. Nevertheless, ChatGPT consistently re-directed the individual to seek help from a healthcare professional to obtain targeted advice. CONCLUSIONS ChatGPT may act as a useful adjunct in the process of ART counselling for people living with HIV. Improving access to information on and knowledge about ART may improve access and adherence to ART and outcomes for people living with HIV overall.
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Affiliation(s)
- Matthew Chung Yi Koh
- Division of Infectious Diseases, Department of Medicine, National University Hospital, National University Health System, Singapore, Singapore
| | - Jinghao Nicholas Ngiam
- Division of Infectious Diseases, Department of Medicine, National University Hospital, National University Health System, Singapore, Singapore
| | - Joy Yong
- Department of Pharmacy, National University Health System, Singapore, Singapore
| | - Paul Anantharajah Tambyah
- Division of Infectious Diseases, Department of Medicine, National University Hospital, National University Health System, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Infectious Diseases Translational Research Programme, Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Sophia Archuleta
- Division of Infectious Diseases, Department of Medicine, National University Hospital, National University Health System, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
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5
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Spotnitz M, Idnay B, Gordon ER, Shyu R, Zhang G, Liu C, Cimino JJ, Weng C. A Survey of Clinicians' Views of the Utility of Large Language Models. Appl Clin Inform 2024; 15:306-312. [PMID: 38442909 PMCID: PMC11023712 DOI: 10.1055/a-2281-7092] [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: 12/01/2023] [Accepted: 02/15/2024] [Indexed: 03/07/2024] Open
Abstract
OBJECTIVES Large language models (LLMs) like Generative pre-trained transformer (ChatGPT) are powerful algorithms that have been shown to produce human-like text from input data. Several potential clinical applications of this technology have been proposed and evaluated by biomedical informatics experts. However, few have surveyed health care providers for their opinions about whether the technology is fit for use. METHODS We distributed a validated mixed-methods survey to gauge practicing clinicians' comfort with LLMs for a breadth of tasks in clinical practice, research, and education, which were selected from the literature. RESULTS A total of 30 clinicians fully completed the survey. Of the 23 tasks, 16 were rated positively by more than 50% of the respondents. Based on our qualitative analysis, health care providers considered LLMs to have excellent synthesis skills and efficiency. However, our respondents had concerns that LLMs could generate false information and propagate training data bias.Our survey respondents were most comfortable with scenarios that allow LLMs to function in an assistive role, like a physician extender or trainee. CONCLUSION In a mixed-methods survey of clinicians about LLM use, health care providers were encouraging of having LLMs in health care for many tasks, and especially in assistive roles. There is a need for continued human-centered development of both LLMs and artificial intelligence in general.
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Affiliation(s)
- Matthew Spotnitz
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, United States
| | - Betina Idnay
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, United States
| | - Emily R. Gordon
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, United States
- Department of Dermatology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, New York, United States
| | - Rebecca Shyu
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, United States
| | - Gongbo Zhang
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, United States
| | - Cong Liu
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, United States
| | - James J. Cimino
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, United States
- Department of Biomedical Informatics and Data Science, Informatics Institute, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, United States
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, United States
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Lee GU, Hong DY, Kim SY, Kim JW, Lee YH, Park SO, Lee KR. Comparison of the problem-solving performance of ChatGPT-3.5, ChatGPT-4, Bing Chat, and Bard for the Korean emergency medicine board examination question bank. Medicine (Baltimore) 2024; 103:e37325. [PMID: 38428889 PMCID: PMC10906566 DOI: 10.1097/md.0000000000037325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Accepted: 01/31/2024] [Indexed: 03/03/2024] Open
Abstract
Large language models (LLMs) have been deployed in diverse fields, and the potential for their application in medicine has been explored through numerous studies. This study aimed to evaluate and compare the performance of ChatGPT-3.5, ChatGPT-4, Bing Chat, and Bard for the Emergency Medicine Board Examination question bank in the Korean language. Of the 2353 questions in the question bank, 150 questions were randomly selected, and 27 containing figures were excluded. Questions that required abilities such as analysis, creative thinking, evaluation, and synthesis were classified as higher-order questions, and those that required only recall, memory, and factual information in response were classified as lower-order questions. The answers and explanations obtained by inputting the 123 questions into the LLMs were analyzed and compared. ChatGPT-4 (75.6%) and Bing Chat (70.7%) showed higher correct response rates than ChatGPT-3.5 (56.9%) and Bard (51.2%). ChatGPT-4 showed the highest correct response rate for the higher-order questions at 76.5%, and Bard and Bing Chat showed the highest rate for the lower-order questions at 71.4%. The appropriateness of the explanation for the answer was significantly higher for ChatGPT-4 and Bing Chat than for ChatGPT-3.5 and Bard (75.6%, 68.3%, 52.8%, and 50.4%, respectively). ChatGPT-4 and Bing Chat outperformed ChatGPT-3.5 and Bard in answering a random selection of Emergency Medicine Board Examination questions in the Korean language.
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Affiliation(s)
- Go Un Lee
- Department of Emergency Medicine, Konkuk University Medical Center, Seoul, Republic of Korea
| | - Dae Young Hong
- Department of Emergency Medicine, Konkuk University School of Medicine, Seoul, Republic of Korea
| | - Sin Young Kim
- Department of Emergency Medicine, Konkuk University Medical Center, Seoul, Republic of Korea
| | - Jong Won Kim
- Department of Emergency Medicine, Konkuk University School of Medicine, Seoul, Republic of Korea
| | - Young Hwan Lee
- Department of Emergency Medicine, Konkuk University School of Medicine, Seoul, Republic of Korea
| | - Sang O Park
- Department of Emergency Medicine, Konkuk University School of Medicine, Seoul, Republic of Korea
| | - Kyeong Ryong Lee
- Department of Emergency Medicine, Konkuk University School of Medicine, Seoul, Republic of Korea
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Tangadulrat P, Sono S, Tangtrakulwanich B. Using ChatGPT for Clinical Practice and Medical Education: Cross-Sectional Survey of Medical Students' and Physicians' Perceptions. JMIR MEDICAL EDUCATION 2023; 9:e50658. [PMID: 38133908 PMCID: PMC10770783 DOI: 10.2196/50658] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Revised: 10/17/2023] [Accepted: 12/11/2023] [Indexed: 12/23/2023]
Abstract
BACKGROUND ChatGPT is a well-known large language model-based chatbot. It could be used in the medical field in many aspects. However, some physicians are still unfamiliar with ChatGPT and are concerned about its benefits and risks. OBJECTIVE We aim to evaluate the perception of physicians and medical students toward using ChatGPT in the medical field. METHODS A web-based questionnaire was sent to medical students, interns, residents, and attending staff with questions regarding their perception toward using ChatGPT in clinical practice and medical education. Participants were also asked to rate their perception of ChatGPT's generated response about knee osteoarthritis. RESULTS Participants included 124 medical students, 46 interns, 37 residents, and 32 attending staff. After reading ChatGPT's response, 132 of the 239 (55.2%) participants had a positive rating about using ChatGPT for clinical practice. The proportion of positive answers was significantly lower in graduated physicians (48/115, 42%) compared with medical students (84/124, 68%; P<.001). Participants listed a lack of a patient-specific treatment plan, updated evidence, and a language barrier as ChatGPT's pitfalls. Regarding using ChatGPT for medical education, the proportion of positive responses was also significantly lower in graduate physicians (71/115, 62%) compared to medical students (103/124, 83.1%; P<.001). Participants were concerned that ChatGPT's response was too superficial, might lack scientific evidence, and might need expert verification. CONCLUSIONS Medical students generally had a positive perception of using ChatGPT for guiding treatment and medical education, whereas graduated doctors were more cautious in this regard. Nonetheless, both medical students and graduated doctors positively perceived using ChatGPT for creating patient educational materials.
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Affiliation(s)
- Pasin Tangadulrat
- Department of Orthopedics, Faculty of Medicine, Prince of Songkla University, Hatyai, Thailand
| | - Supinya Sono
- Division of Family and Preventive Medicine, Faculty of Medicine, Prince of Songkla University, Hatyai, Thailand
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Pushpanathan K, Lim ZW, Er Yew SM, Chen DZ, Hui'En Lin HA, Lin Goh JH, Wong WM, Wang X, Jin Tan MC, Chang Koh VT, Tham YC. Popular large language model chatbots' accuracy, comprehensiveness, and self-awareness in answering ocular symptom queries. iScience 2023; 26:108163. [PMID: 37915603 PMCID: PMC10616302 DOI: 10.1016/j.isci.2023.108163] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 09/19/2023] [Accepted: 10/05/2023] [Indexed: 11/03/2023] Open
Abstract
In light of growing interest in using emerging large language models (LLMs) for self-diagnosis, we systematically assessed the performance of ChatGPT-3.5, ChatGPT-4.0, and Google Bard in delivering proficient responses to 37 common inquiries regarding ocular symptoms. Responses were masked, randomly shuffled, and then graded by three consultant-level ophthalmologists for accuracy (poor, borderline, good) and comprehensiveness. Additionally, we evaluated the self-awareness capabilities (ability to self-check and self-correct) of the LLM-Chatbots. 89.2% of ChatGPT-4.0 responses were 'good'-rated, outperforming ChatGPT-3.5 (59.5%) and Google Bard (40.5%) significantly (all p < 0.001). All three LLM-Chatbots showed optimal mean comprehensiveness scores as well (ranging from 4.6 to 4.7 out of 5). However, they exhibited subpar to moderate self-awareness capabilities. Our study underscores the potential of ChatGPT-4.0 in delivering accurate and comprehensive responses to ocular symptom inquiries. Future rigorous validation of their performance is crucial to ensure their reliability and appropriateness for actual clinical use.
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Affiliation(s)
- Krithi Pushpanathan
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Centre for Innovation and Precision Eye Health & Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Zhi Wei Lim
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Samantha Min Er Yew
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Centre for Innovation and Precision Eye Health & Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - David Ziyou Chen
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Centre for Innovation and Precision Eye Health & Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Ophthalmology, National University Hospital, Singapore, Singapore
| | - Hazel Anne Hui'En Lin
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Centre for Innovation and Precision Eye Health & Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Ophthalmology, National University Hospital, Singapore, Singapore
| | - Jocelyn Hui Lin Goh
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Wendy Meihua Wong
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Centre for Innovation and Precision Eye Health & Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Ophthalmology, National University Hospital, Singapore, Singapore
| | - Xiaofei Wang
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing, China
- Advanced Innovation Centre for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Marcus Chun Jin Tan
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Centre for Innovation and Precision Eye Health & Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Ophthalmology, National University Hospital, Singapore, Singapore
| | - Victor Teck Chang Koh
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Centre for Innovation and Precision Eye Health & Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Ophthalmology, National University Hospital, Singapore, Singapore
| | - Yih-Chung Tham
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Centre for Innovation and Precision Eye Health & Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Programme (Eye ACP), Duke NUS Medical School, Singapore, Singapore
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Tian S, Jin Q, Yeganova L, Lai PT, Zhu Q, Chen X, Yang Y, Chen Q, Kim W, Comeau DC, Islamaj R, Kapoor A, Gao X, Lu Z. Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Health. ARXIV 2023:arXiv:2306.10070v2. [PMID: 37904734 PMCID: PMC10614979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/01/2023]
Abstract
ChatGPT has drawn considerable attention from both the general public and domain experts with its remarkable text generation capabilities. This has subsequently led to the emergence of diverse applications in the field of biomedicine and health. In this work, we examine the diverse applications of large language models (LLMs), such as ChatGPT, in biomedicine and health. Specifically we explore the areas of biomedical information retrieval, question answering, medical text summarization, information extraction, and medical education, and investigate whether LLMs possess the transformative power to revolutionize these tasks or whether the distinct complexities of biomedical domain presents unique challenges. Following an extensive literature survey, we find that significant advances have been made in the field of text generation tasks, surpassing the previous state-of-the-art methods. For other applications, the advances have been modest. Overall, LLMs have not yet revolutionized biomedicine, but recent rapid progress indicates that such methods hold great potential to provide valuable means for accelerating discovery and improving health. We also find that the use of LLMs, like ChatGPT, in the fields of biomedicine and health entails various risks and challenges, including fabricated information in its generated responses, as well as legal and privacy concerns associated with sensitive patient data. We believe this survey can provide a comprehensive and timely overview to biomedical researchers and healthcare practitioners on the opportunities and challenges associated with using ChatGPT and other LLMs for transforming biomedicine and health.
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Affiliation(s)
- Shubo Tian
- National Library of Medicine, National Institutes of Health
| | - Qiao Jin
- National Library of Medicine, National Institutes of Health
| | - Lana Yeganova
- National Library of Medicine, National Institutes of Health
| | - Po-Ting Lai
- National Library of Medicine, National Institutes of Health
| | - Qingqing Zhu
- National Library of Medicine, National Institutes of Health
| | - Xiuying Chen
- King Abdullah University of Science and Technology
| | - Yifan Yang
- National Library of Medicine, National Institutes of Health
| | - Qingyu Chen
- National Library of Medicine, National Institutes of Health
| | - Won Kim
- National Library of Medicine, National Institutes of Health
| | | | | | - Aadit Kapoor
- National Library of Medicine, National Institutes of Health
| | - Xin Gao
- King Abdullah University of Science and Technology
| | - Zhiyong Lu
- National Library of Medicine, National Institutes of Health
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Köroğlu EY, Fakı S, Beştepe N, Tam AA, Çuhacı Seyrek N, Topaloglu O, Ersoy R, Cakir B. A Novel Approach: Evaluating ChatGPT's Utility for the Management of Thyroid Nodules. Cureus 2023; 15:e47576. [PMID: 38021609 PMCID: PMC10666652 DOI: 10.7759/cureus.47576] [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: 10/24/2023] [Indexed: 12/01/2023] Open
Abstract
Background and objective Artificial intelligence (AI) applications such as Chat Generative Pre-Trained Transformer (ChatGPT) created by OpenAI, which represent the revolutionary aspects of today's technology, have benefitted professionals in many fields and society at large. In this study, we aimed to assess how effective is ChatGPT in helping both the patient and the physician manage thyroid nodules, a very common pathology. Methods Fifty-five questions frequently asked by patients were identified and asked to ChatGPT. Subsequently, three cases of thyroid nodules were progressively presented to ChatGPT. The answers to patient questions were scored for correctness and reliability by two endocrinologists. As for the cases, diagnostic and therapeutic approaches provided by ChatGPT were analyzed and scored by two endocrinologists for correctness, safety, and usability. The responses were evaluated by using 7-point Likert-type scales designed by us. Results The answers to patient questions were found to be mostly correct and reliable by both raters (Rater #1: 6.47 ± 0.50 and 6.27 ± 0.52; Rater #2: 6.18 ± 0.92 and 6.09 ± 0.96). Regarding the management of cases, ChatGPT's approach was found to be largely correct, safe, and usable by Rater #1, while Rater #2 evaluated the approaches as partially or mostly correct, safe, and usable. Conclusion Based on our findings, ChatGPT can be used as an informative and reliable resource for managing patients with thyroid nodules. While it is not suitable to be used as a primary resource for physicians, it has the potential to be a helpful and supportive tool.
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Affiliation(s)
- Ekin Y Köroğlu
- Endocrinology and Metabolism, Ankara City Hospital, Ankara, TUR
| | - Sevgül Fakı
- Endocrinology and Metabolism, Ankara City Hospital, Ankara, TUR
| | - Nagihan Beştepe
- Endocrinology and Metabolism, Ankara City Hospital, Ankara, TUR
| | - Abbas A Tam
- Endocrinology and Metabolism, Ankara Yıldırım Beyazıt University School of Medicine, Ankara, TUR
| | - Neslihan Çuhacı Seyrek
- Endocrinology and Metabolism, Ankara Yıldırım Beyazıt University School of Medicine, Ankara, TUR
| | - Oya Topaloglu
- Endocrinology and Metabolism, Ankara Yıldırım Beyazıt University School of Medicine, Ankara, TUR
| | - Reyhan Ersoy
- Endocrinology and Metabolism, Ankara Yıldırım Beyazıt University School of Medicine, Ankara, TUR
| | - Bekir Cakir
- Endocrinology and Metabolism, Ankara Yıldırım Beyazıt University School of Medicine, Ankara, TUR
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