Use of artificial intelligence in medical education: A strength or an infirmity

Sarfaraz, Shaur; Khurshid, Zohaib; Zafar, Muhammad Sohail

doi:10.1016/j.jtumed.2023.06.008

Cited by 3 publications

(3 citation statements)

References 10 publications

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“…and its legal and ethical issues into medical education curricula [17]. It is essential to cultivate problemsolving skills pertinent to the AI era in medical students, which encourages a proactive approach to addressing the complex and evolving landscape of healthcare augmented by AI.…”

Section: Ethical and Educational Imperatives In Ai Integration For En...mentioning

confidence: 99%

Learner-Centered Experience-Based Medical Education in an AI-Driven Society: A Literature Review

Komasawa,

Yokohira

2023

Cureus

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This review proposes and explores the significance of "experience-based medical education" (EXPBME) in the context of an artificial intelligence (AI)-driven society. The rapid advancements in AI, particularly driven by deep learning, have revolutionized medical practices by replicating human cognitive functions, such as image analysis and data interpretation, significantly enhancing efficiency and precision across medical settings. The integration of AI into healthcare presents substantial potential, ranging from precise diagnostics to streamlined data management. However, non-technical skills, such as situational awareness on recognizing AI's fallibility or inherent risks, are critical for future healthcare professionals. EXPBME in a clinical or simulation environment plays a vital role, allowing medical practitioners to navigate AI failures through sufficient reflections. As AI continues to evolve, aligning educational frameworks to nurture these fundamental non-technical skills is paramount to adequately prepare healthcare professionals. Learnercentered EXPBME, combined with the AI literacy acquirement, stands as a key pillar in shaping the future of medical education.

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Section: Ethical and Educational Imperatives In Ai Integration For En...mentioning

confidence: 99%

Learner-Centered Experience-Based Medical Education in an AI-Driven Society: A Literature Review

Komasawa,

Yokohira

2023

Cureus

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“…Deep learning from neural networks has implications for improving surgical and clinical precision and accuracy, patient education, data interpretation, information management, and many other potential applications within subspecialty care. 1 , 2 , 3 , 4 , 5 , 6 Although these recent developments have made substantial contributions, their use requires considerable time, effort, and data specific to that area of interest. These types of AI can generally considered to be domain specific.…”

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confidence: 99%

“…AI has become an increasingly important tool for medical education as well as providing fast access to many years of data and includes computer‐based models, virtual reality simulations, and personalized learning platforms. 5 , 7 , 8 As the capabilities of AI continue their rapid advance, it is becoming increasingly important to regularly evaluate the competency of AI‐powered tools. This evaluation is crucial to maintain high standards and prevent potential errors or biases, especially when addressing generative AI models that may demonstrate flawed reasoning or deliver misinformation that could harm patients or spread inaccurate information.…”

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confidence: 99%

Comparative Performance of ChatGPT 3.5 and GPT4 on Rhinology Standardized Board Examination Questions

Patel,

Fleischer,

Filip

et al. 2024

OTO Open

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ObjectiveAdvances in deep learning and artificial intelligence (AI) have led to the emergence of large language models (LLM) like ChatGPT from OpenAI. The study aimed to evaluate the performance of ChatGPT 3.5 and GPT4 on Otolaryngology (Rhinology) Standardized Board Examination questions in comparison to Otolaryngology residents.MethodsThis study selected all 127 rhinology standardized questions from www.boardvitals.com, a commonly used study tool by otolaryngology residents preparing for board exams. Ninety‐three text‐based questions were administered to ChatGPT 3.5 and GPT4, and their answers were compared with the average results of the question bank (used primarily by otolaryngology residents). Thirty‐four image‐based questions were provided to GPT4 and underwent the same analysis. Based on the findings of an earlier study, a pass‐fail cutoff was set at the 10th percentile.ResultsOn text‐based questions, ChatGPT 3.5 answered correctly 45.2% of the time (8th percentile) (P = .0001), while GPT4 achieved 86.0% (66th percentile) (P = .001). GPT4 answered image‐based questions correctly 64.7% of the time. Projections suggest that ChatGPT 3.5 might not pass the American Board of Otolaryngology Written Question Exam (ABOto WQE), whereas GPT4 stands a strong chance of passing.DiscussionThe older LLM, ChatGPT 3.5, is unlikely to pass the ABOto WQE. However, the advanced GPT4 model exhibits a much higher likelihood of success. This rapid progression in AI indicates its potential future role in otolaryngology education.Implications for PracticeAs AI technology rapidly advances, it may be that AI‐assisted medical education, diagnosis, and treatment planning become commonplace in the medical and surgical landscape.Level of EvidenceLevel 5.

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

Zapata,

Guerrero,

Montilla

2024

Seminars in Medical Writing and Education

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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 colour is related to artificial Intelligence; the second cluster identified with green colour is related to the controlled study; the third cluster identified with yellow colour is related to algorithm and, the fourth cluster identified with yellow colour 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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Use of artificial intelligence in medical education: A strength or an infirmity

Cited by 3 publications

References 10 publications

Learner-Centered Experience-Based Medical Education in an AI-Driven Society: A Literature Review

Learner-Centered Experience-Based Medical Education in an AI-Driven Society: A Literature Review

Comparative Performance of ChatGPT 3.5 and GPT4 on Rhinology Standardized Board Examination Questions

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

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