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Pulido M. [The challenge of artificial intelligence in medical writing and editing]. Med Clin (Barc) 2024; 163:186-188. [PMID: 38679496 DOI: 10.1016/j.medcli.2024.01.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 01/25/2024] [Accepted: 01/26/2024] [Indexed: 05/01/2024]
Affiliation(s)
- Marta Pulido
- Editora médica independiente, Barcelona, España; Acreditada en Editing/Writing por la American Medical Writers Association; Profesora de Comunicación Científica, Máster de Biomedicina, Universitat de Barcelona, Barcelona, España.
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Gravina AG, Pellegrino R, Palladino G, Imperio G, Ventura A, Federico A. Charting new AI education in gastroenterology: Cross-sectional evaluation of ChatGPT and perplexity AI in medical residency exam. Dig Liver Dis 2024; 56:1304-1311. [PMID: 38503659 DOI: 10.1016/j.dld.2024.02.019] [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: 01/10/2024] [Revised: 02/08/2024] [Accepted: 02/28/2024] [Indexed: 03/21/2024]
Abstract
BACKGROUND Conversational chatbots, fueled by large language models, spark debate over their potential in education and medical career exams. There is debate in the literature about the scientific integrity of the outputs produced by these chatbots. AIMS This study evaluates ChatGPT 3.5 and Perplexity AI's cross-sectional performance in responding to questions from the 2023 Italian national residency admission exam (SSM23), comparing results and chatbots' concordance with previous years SSMs. METHODS Gastroenterology-related SSM23 questions were input into ChatGPT 3.5 and Perplexity AI, evaluating their performance in correct responses and total scores. This process was repeated with questions from the three preceding years. Additionally, chatbot concordance was assessed using Cohen's method. RESULTS In SSM23, ChatGPT 3.5 outperforms Perplexity AI with 94.11% correct responses, demonstrating consistency across years. Concordance weakened in 2023 (κ=0.203, P = 0.148), but ChatGPT consistently maintains a high standard compared to Perplexity AI. CONCLUSION ChatGPT 3.5 and Perplexity AI exhibit promise in addressing gastroenterological queries, emphasizing potential educational roles. However, their variable performance mandates cautious use as supplementary tools alongside conventional study methods. Clear guidelines are crucial for educators to balance traditional approaches and innovative systems, enhancing educational standards.
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Affiliation(s)
- Antonietta Gerarda Gravina
- Hepatogastroenterology Division, Department of Precision Medicine, University of Campania Luigi Vanvitelli, Via Luigi de Crecchio, 80138, Naples, Italy
| | - Raffaele Pellegrino
- Hepatogastroenterology Division, Department of Precision Medicine, University of Campania Luigi Vanvitelli, Via Luigi de Crecchio, 80138, Naples, Italy.
| | - Giovanna Palladino
- Hepatogastroenterology Division, Department of Precision Medicine, University of Campania Luigi Vanvitelli, Via Luigi de Crecchio, 80138, Naples, Italy
| | - Giuseppe Imperio
- Hepatogastroenterology Division, Department of Precision Medicine, University of Campania Luigi Vanvitelli, Via Luigi de Crecchio, 80138, Naples, Italy
| | - Andrea Ventura
- Hepatogastroenterology Division, Department of Precision Medicine, University of Campania Luigi Vanvitelli, Via Luigi de Crecchio, 80138, Naples, Italy
| | - Alessandro Federico
- Hepatogastroenterology Division, Department of Precision Medicine, University of Campania Luigi Vanvitelli, Via Luigi de Crecchio, 80138, Naples, Italy
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Moulaei K, Yadegari A, Baharestani M, Farzanbakhsh S, Sabet B, Reza Afrash M. Generative artificial intelligence in healthcare: A scoping review on benefits, challenges and applications. Int J Med Inform 2024; 188:105474. [PMID: 38733640 DOI: 10.1016/j.ijmedinf.2024.105474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Revised: 05/03/2024] [Accepted: 05/04/2024] [Indexed: 05/13/2024]
Abstract
BACKGROUND Generative artificial intelligence (GAI) is revolutionizing healthcare with solutions for complex challenges, enhancing diagnosis, treatment, and care through new data and insights. However, its integration raises questions about applications, benefits, and challenges. Our study explores these aspects, offering an overview of GAI's applications and future prospects in healthcare. METHODS This scoping review searched Web of Science, PubMed, and Scopus . The selection of studies involved screening titles, reviewing abstracts, and examining full texts, adhering to the PRISMA-ScR guidelines throughout the process. RESULTS From 1406 articles across three databases, 109 met inclusion criteria after screening and deduplication. Nine GAI models were utilized in healthcare, with ChatGPT (n = 102, 74 %), Google Bard (Gemini) (n = 16, 11 %), and Microsoft Bing AI (n = 10, 7 %) being the most frequently employed. A total of 24 different applications of GAI in healthcare were identified, with the most common being "offering insights and information on health conditions through answering questions" (n = 41) and "diagnosis and prediction of diseases" (n = 17). In total, 606 benefits and challenges were identified, which were condensed to 48 benefits and 61 challenges after consolidation. The predominant benefits included "Providing rapid access to information and valuable insights" and "Improving prediction and diagnosis accuracy", while the primary challenges comprised "generating inaccurate or fictional content", "unknown source of information and fake references for texts", and "lower accuracy in answering questions". CONCLUSION This scoping review identified the applications, benefits, and challenges of GAI in healthcare. This synthesis offers a crucial overview of GAI's potential to revolutionize healthcare, emphasizing the imperative to address its limitations.
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Affiliation(s)
- Khadijeh Moulaei
- Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
| | - Atiye Yadegari
- Department of Pediatric Dentistry, School of Dentistry, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Mahdi Baharestani
- Network of Interdisciplinarity in Neonates and Infants (NINI), Universal Scientific Education and Research Network (USERN), Tehran, Iran
| | - Shayan Farzanbakhsh
- Network of Interdisciplinarity in Neonates and Infants (NINI), Universal Scientific Education and Research Network (USERN), Tehran, Iran
| | - Babak Sabet
- Department of Surgery, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammad Reza Afrash
- Department of Artificial Intelligence, Smart University of Medical Sciences, Tehran, Iran.
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Liu M, Okuhara T, Chang X, Shirabe R, Nishiie Y, Okada H, Kiuchi T. Performance of ChatGPT Across Different Versions in Medical Licensing Examinations Worldwide: Systematic Review and Meta-Analysis. J Med Internet Res 2024; 26:e60807. [PMID: 39052324 PMCID: PMC11310649 DOI: 10.2196/60807] [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: 05/22/2024] [Revised: 06/11/2024] [Accepted: 06/15/2024] [Indexed: 07/27/2024] Open
Abstract
BACKGROUND Over the past 2 years, researchers have used various medical licensing examinations to test whether ChatGPT (OpenAI) possesses accurate medical knowledge. The performance of each version of ChatGPT on the medical licensing examination in multiple environments showed remarkable differences. At this stage, there is still a lack of a comprehensive understanding of the variability in ChatGPT's performance on different medical licensing examinations. OBJECTIVE In this study, we reviewed all studies on ChatGPT performance in medical licensing examinations up to March 2024. This review aims to contribute to the evolving discourse on artificial intelligence (AI) in medical education by providing a comprehensive analysis of the performance of ChatGPT in various environments. The insights gained from this systematic review will guide educators, policymakers, and technical experts to effectively and judiciously use AI in medical education. METHODS We searched the literature published between January 1, 2022, and March 29, 2024, by searching query strings in Web of Science, PubMed, and Scopus. Two authors screened the literature according to the inclusion and exclusion criteria, extracted data, and independently assessed the quality of the literature concerning Quality Assessment of Diagnostic Accuracy Studies-2. We conducted both qualitative and quantitative analyses. RESULTS A total of 45 studies on the performance of different versions of ChatGPT in medical licensing examinations were included in this study. GPT-4 achieved an overall accuracy rate of 81% (95% CI 78-84; P<.01), significantly surpassing the 58% (95% CI 53-63; P<.01) accuracy rate of GPT-3.5. GPT-4 passed the medical examinations in 26 of 29 cases, outperforming the average scores of medical students in 13 of 17 cases. Translating the examination questions into English improved GPT-3.5's performance but did not affect GPT-4. GPT-3.5 showed no difference in performance between examinations from English-speaking and non-English-speaking countries (P=.72), but GPT-4 performed better on examinations from English-speaking countries significantly (P=.02). Any type of prompt could significantly improve GPT-3.5's (P=.03) and GPT-4's (P<.01) performance. GPT-3.5 performed better on short-text questions than on long-text questions. The difficulty of the questions affected the performance of GPT-3.5 and GPT-4. In image-based multiple-choice questions (MCQs), ChatGPT's accuracy rate ranges from 13.1% to 100%. ChatGPT performed significantly worse on open-ended questions than on MCQs. CONCLUSIONS GPT-4 demonstrates considerable potential for future use in medical education. However, due to its insufficient accuracy, inconsistent performance, and the challenges posed by differing medical policies and knowledge across countries, GPT-4 is not yet suitable for use in medical education. TRIAL REGISTRATION PROSPERO CRD42024506687; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=506687.
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Affiliation(s)
- Mingxin Liu
- Department of Health Communication, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Tsuyoshi Okuhara
- Department of Health Communication, School of Public Health, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - XinYi Chang
- Department of Industrial Engineering and Economics, School of Engineering, Tokyo Institute of Technology, Tokyo, Japan
| | - Ritsuko Shirabe
- Department of Health Communication, School of Public Health, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Yuriko Nishiie
- Department of Health Communication, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Hiroko Okada
- Department of Health Communication, School of Public Health, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Takahiro Kiuchi
- Department of Health Communication, School of Public Health, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
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Hu M, Qian J, Pan S, Li Y, Qiu RLJ, Yang X. Advancing medical imaging with language models: featuring a spotlight on ChatGPT. Phys Med Biol 2024; 69:10TR01. [PMID: 38537293 DOI: 10.1088/1361-6560/ad387d] [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: 06/16/2023] [Accepted: 03/27/2024] [Indexed: 05/04/2024]
Abstract
This review paper aims to serve as a comprehensive guide and instructional resource for researchers seeking to effectively implement language models in medical imaging research. First, we presented the fundamental principles and evolution of language models, dedicating particular attention to large language models. We then reviewed the current literature on how language models are being used to improve medical imaging, emphasizing a range of applications such as image captioning, report generation, report classification, findings extraction, visual question response systems, interpretable diagnosis and so on. Notably, the capabilities of ChatGPT were spotlighted for researchers to explore its further applications. Furthermore, we covered the advantageous impacts of accurate and efficient language models in medical imaging analysis, such as the enhancement of clinical workflow efficiency, reduction of diagnostic errors, and assistance of clinicians in providing timely and accurate diagnoses. Overall, our goal is to have better integration of language models with medical imaging, thereby inspiring new ideas and innovations. It is our aspiration that this review can serve as a useful resource for researchers in this field, stimulating continued investigative and innovative pursuits of the application of language models in medical imaging.
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Affiliation(s)
- Mingzhe Hu
- Department of Computer Science and Informatics, Emory University, Atlanta, GA, United States of America
| | - Joshua Qian
- Department of Radiation Oncology, Winship Cancer Institute, School of Medicine, Emory University, Atlanta, GA, United States of America
| | - Shaoyan Pan
- Department of Computer Science and Informatics, Emory University, Atlanta, GA, United States of America
| | - Yuheng Li
- Department of Biomedical Engineering, Emory University, Atlanta, GA, United States of America
| | - Richard L J Qiu
- Department of Radiation Oncology, Winship Cancer Institute, School of Medicine, Emory University, Atlanta, GA, United States of America
| | - Xiaofeng Yang
- Department of Computer Science and Informatics, Emory University, Atlanta, GA, United States of America
- Department of Radiation Oncology, Winship Cancer Institute, School of Medicine, Emory University, Atlanta, GA, United States of America
- Department of Biomedical Engineering, Emory University, Atlanta, GA, United States of America
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King RC, Samaan JS, Yeo YH, Peng Y, Kunkel DC, Habib AA, Ghashghaei R. A Multidisciplinary Assessment of ChatGPT's Knowledge of Amyloidosis: Observational Study. JMIR Cardio 2024; 8:e53421. [PMID: 38640472 PMCID: PMC11069089 DOI: 10.2196/53421] [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: 10/10/2023] [Revised: 01/31/2024] [Accepted: 02/22/2024] [Indexed: 04/21/2024] Open
Abstract
BACKGROUND Amyloidosis, a rare multisystem condition, often requires complex, multidisciplinary care. Its low prevalence underscores the importance of efforts to ensure the availability of high-quality patient education materials for better outcomes. ChatGPT (OpenAI) is a large language model powered by artificial intelligence that offers a potential avenue for disseminating accurate, reliable, and accessible educational resources for both patients and providers. Its user-friendly interface, engaging conversational responses, and the capability for users to ask follow-up questions make it a promising future tool in delivering accurate and tailored information to patients. OBJECTIVE We performed a multidisciplinary assessment of the accuracy, reproducibility, and readability of ChatGPT in answering questions related to amyloidosis. METHODS In total, 98 amyloidosis questions related to cardiology, gastroenterology, and neurology were curated from medical societies, institutions, and amyloidosis Facebook support groups and inputted into ChatGPT-3.5 and ChatGPT-4. Cardiology- and gastroenterology-related responses were independently graded by a board-certified cardiologist and gastroenterologist, respectively, who specialize in amyloidosis. These 2 reviewers (RG and DCK) also graded general questions for which disagreements were resolved with discussion. Neurology-related responses were graded by a board-certified neurologist (AAH) who specializes in amyloidosis. Reviewers used the following grading scale: (1) comprehensive, (2) correct but inadequate, (3) some correct and some incorrect, and (4) completely incorrect. Questions were stratified by categories for further analysis. Reproducibility was assessed by inputting each question twice into each model. The readability of ChatGPT-4 responses was also evaluated using the Textstat library in Python (Python Software Foundation) and the Textstat readability package in R software (R Foundation for Statistical Computing). RESULTS ChatGPT-4 (n=98) provided 93 (95%) responses with accurate information, and 82 (84%) were comprehensive. ChatGPT-3.5 (n=83) provided 74 (89%) responses with accurate information, and 66 (79%) were comprehensive. When examined by question category, ChatGTP-4 and ChatGPT-3.5 provided 53 (95%) and 48 (86%) comprehensive responses, respectively, to "general questions" (n=56). When examined by subject, ChatGPT-4 and ChatGPT-3.5 performed best in response to cardiology questions (n=12) with both models producing 10 (83%) comprehensive responses. For gastroenterology (n=15), ChatGPT-4 received comprehensive grades for 9 (60%) responses, and ChatGPT-3.5 provided 8 (53%) responses. Overall, 96 of 98 (98%) responses for ChatGPT-4 and 73 of 83 (88%) for ChatGPT-3.5 were reproducible. The readability of ChatGPT-4's responses ranged from 10th to beyond graduate US grade levels with an average of 15.5 (SD 1.9). CONCLUSIONS Large language models are a promising tool for accurate and reliable health information for patients living with amyloidosis. However, ChatGPT's responses exceeded the American Medical Association's recommended fifth- to sixth-grade reading level. Future studies focusing on improving response accuracy and readability are warranted. Prior to widespread implementation, the technology's limitations and ethical implications must be further explored to ensure patient safety and equitable implementation.
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Affiliation(s)
- Ryan C King
- Division of Cardiology, Department of Medicine, University of California, Irvine Medical Center, Orange, CA, United States
| | - Jamil S Samaan
- Karsh Division of Gastroenterology and Hepatology, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Yee Hui Yeo
- Karsh Division of Gastroenterology and Hepatology, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Yuxin Peng
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China
| | - David C Kunkel
- GI Motility and Physiology Program, Division of Gastroenterology, University of California, San Diego, La Jolla, CA, United States
| | - Ali A Habib
- Division of Neurology, University of California, Irvine Medical Center, Orange, CA, United States
| | - Roxana Ghashghaei
- Division of Cardiology, Department of Medicine, University of California, Irvine Medical Center, Orange, CA, United States
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Frosolini A, Catarzi L, Benedetti S, Latini L, Chisci G, Franz L, Gennaro P, Gabriele G. The Role of Large Language Models (LLMs) in Providing Triage for Maxillofacial Trauma Cases: A Preliminary Study. Diagnostics (Basel) 2024; 14:839. [PMID: 38667484 PMCID: PMC11048758 DOI: 10.3390/diagnostics14080839] [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: 03/29/2024] [Revised: 04/10/2024] [Accepted: 04/16/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND In the evolving field of maxillofacial surgery, integrating advanced technologies like Large Language Models (LLMs) into medical practices, especially for trauma triage, presents a promising yet largely unexplored potential. This study aimed to evaluate the feasibility of using LLMs for triaging complex maxillofacial trauma cases by comparing their performance against the expertise of a tertiary referral center. METHODS Utilizing a comprehensive review of patient records in a tertiary referral center over a year-long period, standardized prompts detailing patient demographics, injury characteristics, and medical histories were created. These prompts were used to assess the triage suggestions of ChatGPT 4.0 and Google GEMINI against the center's recommendations, supplemented by evaluating the AI's performance using the QAMAI and AIPI questionnaires. RESULTS The results in 10 cases of major maxillofacial trauma indicated moderate agreement rates between LLM recommendations and the referral center, with some variances in the suggestion of appropriate examinations (70% ChatGPT and 50% GEMINI) and treatment plans (60% ChatGPT and 45% GEMINI). Notably, the study found no statistically significant differences in several areas of the questionnaires, except in the diagnosis accuracy (GEMINI: 3.30, ChatGPT: 2.30; p = 0.032) and relevance of the recommendations (GEMINI: 2.90, ChatGPT: 3.50; p = 0.021). A Spearman correlation analysis highlighted significant correlations within the two questionnaires, specifically between the QAMAI total score and AIPI treatment scores (rho = 0.767, p = 0.010). CONCLUSIONS This exploratory investigation underscores the potential of LLMs in enhancing clinical decision making for maxillofacial trauma cases, indicating a need for further research to refine their application in healthcare settings.
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Affiliation(s)
- Andrea Frosolini
- Maxillofacial Surgery Unit, Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy; (L.C.); (S.B.); (L.L.); (G.C.); (P.G.); (G.G.)
| | - Lisa Catarzi
- Maxillofacial Surgery Unit, Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy; (L.C.); (S.B.); (L.L.); (G.C.); (P.G.); (G.G.)
| | - Simone Benedetti
- Maxillofacial Surgery Unit, Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy; (L.C.); (S.B.); (L.L.); (G.C.); (P.G.); (G.G.)
| | - Linda Latini
- Maxillofacial Surgery Unit, Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy; (L.C.); (S.B.); (L.L.); (G.C.); (P.G.); (G.G.)
| | - Glauco Chisci
- Maxillofacial Surgery Unit, Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy; (L.C.); (S.B.); (L.L.); (G.C.); (P.G.); (G.G.)
| | - Leonardo Franz
- Phoniatris and Audiology Unit, Department of Neuroscience DNS, University of Padova, 35122 Treviso, Italy;
- Artificial Intelligence in Medicine and Innovation in Clinical Research and Methodology (PhD Program), Department of Clinical and Experimental Sciences, University of Brescia, 25121 Brescia, Italy
| | - Paolo Gennaro
- Maxillofacial Surgery Unit, Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy; (L.C.); (S.B.); (L.L.); (G.C.); (P.G.); (G.G.)
| | - Guido Gabriele
- Maxillofacial Surgery Unit, Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy; (L.C.); (S.B.); (L.L.); (G.C.); (P.G.); (G.G.)
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Alshehri S, Alahmari KA, Alasiry A. A Comprehensive Evaluation of AI-Assisted Diagnostic Tools in ENT Medicine: Insights and Perspectives from Healthcare Professionals. J Pers Med 2024; 14:354. [PMID: 38672981 PMCID: PMC11051468 DOI: 10.3390/jpm14040354] [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: 02/27/2024] [Revised: 03/18/2024] [Accepted: 03/22/2024] [Indexed: 04/28/2024] Open
Abstract
The integration of Artificial Intelligence (AI) into healthcare has the potential to revolutionize medical diagnostics, particularly in specialized fields such as Ear, Nose, and Throat (ENT) medicine. However, the successful adoption of AI-assisted diagnostic tools in ENT practice depends on the understanding of various factors; these include influences on their effectiveness and acceptance among healthcare professionals. This cross-sectional study aimed to assess the usability and integration of AI tools in ENT practice, determine the clinical impact and accuracy of AI-assisted diagnostics in ENT, measure the trust and confidence of ENT professionals in AI tools, gauge the overall satisfaction and outlook on the future of AI in ENT diagnostics, and identify challenges, limitations, and areas for improvement in AI-assisted ENT diagnostics. A structured online questionnaire was distributed to 600 certified ENT professionals with at least one year of experience in the field. The questionnaire assessed participants' familiarity with AI tools, usability, clinical impact, trust, satisfaction, and identified challenges. A total of 458 respondents completed the questionnaire, resulting in a response rate of 91.7%. The majority of respondents reported familiarity with AI tools (60.7%) and perceived them as generally usable and clinically impactful. However, challenges such as integration with existing systems, user-friendliness, accuracy, and cost were identified. Trust and satisfaction levels varied among participants, with concerns regarding data privacy and support. Geographic and practice setting differences influenced perceptions and experiences. The study highlights the diverse perceptions and experiences of ENT professionals regarding AI-assisted diagnostics. While there is general enthusiasm for these tools, challenges related to integration, usability, trust, and cost need to be addressed for their widespread adoption. These findings provide valuable insights for developers, policymakers, and healthcare providers aiming to enhance the role of AI in ENT practice.
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Affiliation(s)
- Sarah Alshehri
- Otology and Neurotology, Department of Surgery, College of Medicine, King Khalid University, Abha 61423, Saudi Arabia
| | - Khalid A. Alahmari
- Medical Rehabilitation Sciences, College of Applied Medical Sciences, King Khalid University, Abha 61423, Saudi Arabia;
| | - Areej Alasiry
- Department of Informatics and Computer Systems, College of Computer Science, King Khalid University, Abha 61423, Saudi Arabia;
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Brandão M, Mendes F, Martins M, Cardoso P, Macedo G, Mascarenhas T, Mascarenhas Saraiva M. Revolutionizing Women's Health: A Comprehensive Review of Artificial Intelligence Advancements in Gynecology. J Clin Med 2024; 13:1061. [PMID: 38398374 PMCID: PMC10889757 DOI: 10.3390/jcm13041061] [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: 12/31/2023] [Revised: 02/04/2024] [Accepted: 02/05/2024] [Indexed: 02/25/2024] Open
Abstract
Artificial intelligence has yielded remarkably promising results in several medical fields, namely those with a strong imaging component. Gynecology relies heavily on imaging since it offers useful visual data on the female reproductive system, leading to a deeper understanding of pathophysiological concepts. The applicability of artificial intelligence technologies has not been as noticeable in gynecologic imaging as in other medical fields so far. However, due to growing interest in this area, some studies have been performed with exciting results. From urogynecology to oncology, artificial intelligence algorithms, particularly machine learning and deep learning, have shown huge potential to revolutionize the overall healthcare experience for women's reproductive health. In this review, we aim to establish the current status of AI in gynecology, the upcoming developments in this area, and discuss the challenges facing its clinical implementation, namely the technological and ethical concerns for technology development, implementation, and accountability.
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Affiliation(s)
- Marta Brandão
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
| | - Francisco Mendes
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Miguel Martins
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Pedro Cardoso
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Guilherme Macedo
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Teresa Mascarenhas
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
- Department of Obstetrics and Gynecology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Miguel Mascarenhas Saraiva
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
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Soto-Chávez MJ, Bustos MM, Fernández-Ávila DG, Muñoz OM. Evaluation of information provided to patients by ChatGPT about chronic diseases in Spanish language. Digit Health 2024; 10:20552076231224603. [PMID: 38188865 PMCID: PMC10768597 DOI: 10.1177/20552076231224603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 12/18/2023] [Indexed: 01/09/2024] Open
Abstract
Introduction Artificial intelligence has presented exponential growth in medicine. The ChatGPT language model has been highlighted as a possible source of patient information. This study evaluates the reliability and readability of ChatGPT-generated patient information on chronic diseases in Spanish. Methods Questions frequently asked by patients on the internet about diabetes mellitus, heart failure, rheumatoid arthritis (RA), chronic kidney disease (CKD), and systemic lupus erythematosus (SLE) were submitted to ChatGPT. Reliability was assessed by rating responses as (1) comprehensive, (2) correct but inadequate, (3) some correct and some incorrect, (4) completely incorrect, and divided between "good" (1 and 2) and "bad" (3 and 4). Readability was evaluated with the adapted Flesch and Szigriszt formulas. Results And 71.67% of the answers were "good," with none qualified as "completely incorrect." Better reliability was observed in questions on diabetes and RA versus heart failure (p = 0.02). In readability, responses were "moderately difficult" (54.73, interquartile range (IQR) 51.59-58.58), with better results for CKD (median 56.1, IQR 53.5-59.1) and RA (56.4, IQR 53.7-60.7), than for heart failure responses (median 50.6, IQR 46.3-53.8). Conclusion Our study suggests that the ChatGPT tool can be a reliable source of information in spanish for patients with chronic diseases with different reliability for some of them, however, it needs to improve the readability of its answers to be recommended as a useful tool for patients.
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Affiliation(s)
| | - Marlon Mauricio Bustos
- Department of Internal Medicine, Pontificia Universidad Javeriana, Bogotá, Colombia
- Department of Internal Medicine, Hospital Universitario San Ignacio, Bogotá, Colombia
| | - Daniel G. Fernández-Ávila
- Department of Internal Medicine, Pontificia Universidad Javeriana, Bogotá, Colombia
- Rheumatology Unit, Hospital Universitario San Ignacio, Bogotá, Colombia
| | - Oscar Mauricio Muñoz
- Department of Internal Medicine, Pontificia Universidad Javeriana, Bogotá, Colombia
- Department of Internal Medicine, Hospital Universitario San Ignacio, Bogotá, Colombia
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