Foundation models in ophthalmology: opportunities and challenges DOI

Mertcan Sevgi,

Eden Ruffell,

Fares Antaki

и другие.

Current Opinion in Ophthalmology, Год журнала: 2024, Номер unknown

Опубликована: Сен. 26, 2024

Purpose of review Last year marked the development first foundation model in ophthalmology, RETFound, setting stage for generalizable medical artificial intelligence (GMAI) that can adapt to novel tasks. Additionally, rapid advancements large language (LLM) technology, including models such as GPT-4 and Gemini, have been tailored specialization evaluated on clinical scenarios with promising results. This explores opportunities challenges further these technologies. Recent findings RETFound outperforms traditional deep learning specific tasks, even when only fine-tuned small datasets. LMMs like Med-Gemini Medprompt perform better than out-of-the-box ophthalmology However, there is still a significant deficiency ophthalmology-specific multimodal models. gap primarily due substantial computational resources required train limitations high-quality Summary Overall, present but face challenges, particularly need high-quality, standardized datasets training specialization. Although has focused vision models, greatest lie advancing which more closely mimic capabilities clinicians.

Язык: Английский

Evaluation of large language models for providing educational information in orthokeratology care DOI
Yangyi Huang, Runhan Shi, Can Chen

и другие.

Contact Lens and Anterior Eye, Год журнала: 2025, Номер unknown, С. 102384 - 102384

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0
Developing a decision support system using different classification algorithms for polyclinic selection DOI
Müberra Terzi Kumandaş, Naci Murat

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 127042 - 127042

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0
The performance of ChatGPT and ERNIE Bot in surgical resident examinations DOI

Siyin Guo,

Genpeng Li, Wei Du

и другие.

International Journal of Medical Informatics, Год журнала: 2025, Номер unknown, С. 105906 - 105906

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
Comparative performance analysis of global and chinese-domain large language models for myopia DOI
Zehua Jiang,

Yueyuan Xu,

Zhi Wei Lim

и другие.

Eye, Год журнала: 2025, Номер unknown

Опубликована: Апрель 13, 2025

Язык: Английский

Процитировано

0
Evaluation of error detection and treatment recommendations in nucleic acid test reports using ChatGPT models DOI
Wenzheng Han, Chao Wan, Rui Shan

и другие.

Clinical Chemistry and Laboratory Medicine (CCLM), Год журнала: 2025, Номер unknown

Опубликована: Апрель 18, 2025

Abstract Objectives Accurate medical laboratory reports are essential for delivering high-quality healthcare. Recently, advanced artificial intelligence models, such as those in the ChatGPT series, have shown considerable promise this domain. This study assessed performance of specific GPT models-namely, 4o, o1, and o1 mini-in identifying errors within providing treatment recommendations. Methods In retrospective study, 86 Nucleic acid test report seven upper respiratory tract pathogens were compiled. There 285 from four common error categories intentionally randomly introduced into generated incorrected reports. models tasked with detecting these errors, using three senior scientists (SMLS) interns (MLI) control groups. Additionally, generating accurate reliable recommendations following positive outcomes based on corrected χ2 tests, Kruskal-Wallis Wilcoxon tests used statistical analysis where appropriate. Results comparison SMLS or MLI, accurately detected types, average detection rates 88.9 %(omission), 91.6 % (time sequence), 91.7 (the same individual acted both inspector reviewer). However, rate result input format by was only 51.9 %, indicating a relatively poor aspect. exhibited substantial to almost perfect agreement total (kappa [min, max]: 0.778, 0.837). between MLI moderately lower 0.632, 0.696). When it comes reading all reports, showed obviously reduced time compared (all p<0.001). Notably, our also found GPT-o1 mini model had better consistency identification than model, which that GPT-4o model. The pairwise comparisons model’s outputs across repeated runs 0.912, 0.996). GPT-o1(all significantly outperformed p<0.0001). Conclusions capability some accuracy reliability competent, especially, potentially reducing work hours enhancing clinical decision-making.

Язык: Английский

Процитировано

0
Vision of the future: large language models in ophthalmology DOI
Prashant D. Tailor, Haley S. D’Souza, Hanzhou Li

и другие.

Current Opinion in Ophthalmology, Год журнала: 2024, Номер 35(5), С. 391 - 402

Опубликована: Май 30, 2024

Large language models (LLMs) are rapidly entering the landscape of medicine in areas from patient interaction to clinical decision-making. This review discusses evolving role LLMs ophthalmology, focusing on their current applications and future potential enhancing ophthalmic care.

Язык: Английский

Процитировано

3
AI’s pivotal impact on redefining stakeholder roles and their interactions in medical education and health care DOI Creative Commons
Jayne S. Reuben,

Hila Meiri,

Hadar Arien‐Zakay

и другие.

Frontiers in Digital Health, Год журнала: 2024, Номер 6

Опубликована: Ноя. 5, 2024

Artificial Intelligence (AI) has the potential to revolutionize medical training, diagnostics, treatment planning, and healthcare delivery while also bringing challenges such as data privacy, risk of technological overreliance, preservation critical thinking. This manuscript explores impact AI Machine Learning (ML) on interactions, focusing faculty, students, clinicians, patients. ML's early inclusion in curriculum will support student-centered learning; however, all stakeholders require specialized training bridge gap between practice innovation. underscores importance education ethical responsible use emphasizing collaboration maximize its benefits. calls for a re-evaluation interpersonal relationships within improve overall quality care safeguard welfare by leveraging AI's strengths managing risks.

Язык: Английский

Процитировано

3
Assessment of Large Language Models in Cataract Care Information Provision: A Quantitative Comparison DOI Creative Commons
Zichang Su, Kai Jin,

Hongkang Wu

и другие.

Ophthalmology and Therapy, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 8, 2024

Cataracts are a significant cause of blindness. While individuals frequently turn to the Internet for medical advice, distinguishing reliable information can be challenging. Large language models (LLMs) have attracted attention generating accurate, human-like responses that may used consultation. However, comprehensive assessment LLMs' accuracy within specific domains is still lacking.

Язык: Английский

Процитировано

3
Qualitative metrics from the biomedical literature for evaluating large language models in clinical decision-making: a narrative review DOI Creative Commons

Cindy Ho,

Tiffany Tian,

Alessandra T. Ayers

и другие.

BMC Medical Informatics and Decision Making, Год журнала: 2024, Номер 24(1)

Опубликована: Ноя. 26, 2024

The large language models (LLMs), most notably ChatGPT, released since November 30, 2022, have prompted shifting attention to their use in medicine, particularly for supporting clinical decision-making. However, there is little consensus the medical community on how LLM performance contexts should be evaluated. We performed a literature review of PubMed identify publications between December 1, and April 2024, that discussed assessments LLM-generated diagnoses or treatment plans. selected 108 relevant articles from analysis. frequently used LLMs were GPT-3.5, GPT-4, Bard, LLaMa/Alpaca-based models, Bing Chat. five criteria scoring outputs "accuracy", "completeness", "appropriateness", "insight", "consistency". defining high-quality been consistently by researchers over past 1.5 years. identified high degree variation studies reported findings assessed performance. Standardized reporting qualitative evaluation metrics assess quality can developed facilitate research healthcare.

Язык: Английский

Процитировано

3
The Performance of OpenAI ChatGPT-4 and Google Gemini in Virology Multiple-Choice Questions: A Comparative Analysis of English and Arabic Responses DOI Creative Commons
Malik Sallam,

Kholoud Al-Mahzoum,

Rawan Ahmad Almutawaa

и другие.

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Апрель 12, 2024

Abstract Background: The integration of artificial intelligence (AI) in healthcare education is inevitable. Understanding the proficiency generative AI different languages to answer complex questions crucial for educational purposes. Objective: To compare performance ChatGPT-4 and Gemini answering Virology multiple-choice (MCQs) English Arabic, while assessing quality generated content. Methods: Both models’ responses 40 MCQs were assessed correctness based on CLEAR tool designed evaluation AI-generated classified into lower higher cognitive categories revised Bloom’s taxonomy. study design considered METRICS checklist reporting AI-based studies healthcare. Results: performed better compared with consistently surpassing scores. led 80% vs. 62.5% 65% 55% Arabic. For both models, superior domains was reported. Conclusion: Both exhibited potential applications; nevertheless, their varied across highlighting importance continued development ensure effective globally.

Язык: Английский

Процитировано

2