Emerging Technologies in Education: A Bibliometric Analysis of Artificial Intelligence and its Applications in Health Sciences.

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Abstract

Artificial Intelligence brings a new paradigm in health sciences related to using technologies capable of processing a large amount of patient information to strengthen prediction, prevention and clinical care. This research aimed to perform a bibliometric analysis of Artificial Intelligence and its applications in Health Sciences, particularly on Emerging Technologies in Education. To this end, a search for articles related to "Artificial Intelligence and its Applications in Health Sciences" was conducted at the international level in the Scopus database with search parameters based on titles, abstracts and keywords. The results revealed that the network of the 100 most essential terms was grouped into four clusters, namely: the first cluster identified with red color is related to artificial Intelligence; the second cluster identified with green color is related to the controlled study; the third cluster identified with yellow color is related to algorithm and, the fourth cluster identified with yellow color is related to education. It was concluded that artificial Intelligence has experienced advances that are having an impact on health sciences education. Academics and researchers have tools that allow them to obtain information to deepen the diagnosis of diseases and present students with robust case studies that strengthen the teaching-learning process

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Evaluating the Performance of ChatGPT in Ophthalmology : An Analysis of Its Successes and Shortcomings

Fares Antaki, Samir Touma, Daniel Milad … (2023)

Purpose Foundation models are a novel type of artificial intelligence algorithms, in which models are pretrained at scale on unannotated data and fine-tuned for a myriad of downstream tasks, such as generating text. This study assessed the accuracy of ChatGPT, a large language model (LLM), in the ophthalmology question-answering space. Design Evaluation of diagnostic test or technology. Participants ChatGPT is a publicly available LLM. Methods We tested 2 versions of ChatGPT (January 9 “legacy” and ChatGPT Plus) on 2 popular multiple choice question banks commonly used to prepare for the high-stakes Ophthalmic Knowledge Assessment Program (OKAP) examination. We generated two 260-question simulated exams from the Basic and Clinical Science Course (BCSC) Self-Assessment Program and the OphthoQuestions online question bank. We carried out logistic regression to determine the effect of the examination section, cognitive level, and difficulty index on answer accuracy. We also performed a post hoc analysis using Tukey’s test to decide if there were meaningful differences between the tested subspecialties. Main Outcome Measures We reported the accuracy of ChatGPT for each examination section in percentage correct by comparing ChatGPT’s outputs with the answer key provided by the question banks. We presented logistic regression results with a likelihood ratio (LR) chi-square. We considered differences between examination sections statistically significant at a P value of < 0.05. Results The legacy model achieved 55.8% accuracy on the BCSC set and 42.7% on the OphthoQuestions set. With ChatGPT Plus, accuracy increased to 59.4% ± 0.6% and 49.2% ± 1.0%, respectively. Accuracy improved with easier questions when controlling for the examination section and cognitive level. Logistic regression analysis of the legacy model showed that the examination section (LR, 27.57; P = 0.006) followed by question difficulty (LR, 24.05; P < 0.001) were most predictive of ChatGPT’s answer accuracy. Although the legacy model performed best in general medicine and worst in neuro-ophthalmology ( P < 0.001) and ocular pathology ( P = 0.029), similar post hoc findings were not seen with ChatGPT Plus, suggesting more consistent results across examination sections. Conclusion ChatGPT has encouraging performance on a simulated OKAP examination. Specializing LLMs through domain-specific pretraining may be necessary to improve their performance in ophthalmic subspecialties. Financial Disclosure(s) Proprietary or commercial disclosure may be found after the references.

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Large AI Models in Health Informatics: Applications, Challenges, and the Future.

Jianing Qiu, Lin Li, Jiankai Sun … (2023)

Large AI models, or foundation models, are models recently emerging with massive scales both parameter-wise and data-wise, the magnitudes of which can reach beyond billions. Once pretrained, large AI models demonstrate impressive performance in various downstream tasks. A prime example is ChatGPT, whose capability has compelled people's imagination about the far-reaching influence that large AI models can have and their potential to transform different domains of our lives. In health informatics, the advent of large AI models has brought new paradigms for the design of methodologies. The scale of multi-modal data in the biomedical and health domain has been ever-expanding especially since the community embraced the era of deep learning, which provides the ground to develop, validate, and advance large AI models for breakthroughs in health-related areas. This article presents a comprehensive review of large AI models, from background to their applications. We identify seven key sectors in which large AI models are applicable and might have substantial influence, including: 1) bioinformatics; 2) medical diagnosis; 3) medical imaging; 4) medical informatics; 5) medical education; 6) public health; and 7) medical robotics. We examine their challenges, followed by a critical discussion about potential future directions and pitfalls of large AI models in transforming the field of health informatics.

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Generative Artificial Intelligence Through ChatGPT and Other Large Language Models in Ophthalmology

Ting Fang Tan, Arun James Thirunavukarasu, J Peter Campbell … (2023)

The rapid progress of large language models (LLMs) driving generative artificial intelligence applications heralds the potential of opportunities in health care. We conducted a review up to April 2023 on Google Scholar, Embase, MEDLINE, and Scopus using the following terms: “large language models,” “generative artificial intelligence,” “ophthalmology,” “ChatGPT,” and “eye,” based on relevance to this review. From a clinical viewpoint specific to ophthalmologists, we explore from the different stakeholders’ perspectives—including patients, physicians, and policymakers—the potential LLM applications in education, research, and clinical domains specific to ophthalmology. We also highlight the foreseeable challenges of LLM implementation into clinical practice, including the concerns of accuracy, interpretability, perpetuating bias, and data security. As LLMs continue to mature, it is essential for stakeholders to jointly establish standards for best practices to safeguard patient safety. Financial Disclosure(s) Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

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Journal

Title: Seminars in Medical Writing and Education

Abbreviated Title: Seminars in Medical Writing and Education

Publisher: AG Editor (Argentina)

ISSN (Print): 3008-8127

Publication date Created: January 01 2024

Publication date (Electronic): February 21 2024

Volume: 3

Page: 49

Article

DOI: 10.56294/mw202449

SO-VID: 187cce2b-c650-4b61-89c3-540a663fdcc1

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https://creativecommons.org/licenses/by/4.0

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Emerging Technologies in Education: A Bibliometric Analysis of Artificial Intelligence and its Applications in Health Sciences.

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Most cited references 26

Evaluating the Performance of ChatGPT in Ophthalmology : An Analysis of Its Successes and Shortcomings

Large AI Models in Health Informatics: Applications, Challenges, and the Future.

Generative Artificial Intelligence Through ChatGPT and Other Large Language Models in Ophthalmology

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