Evaluating the Feasibility of ChatGPT in Healthcare: An Analysis of Multiple Clinical and Research Scenarios
By M. Cascella, J. Montomoli, Valentina Bellini, E. Bignami. Journal of medical systems, 2023
1,076 citations recorded by the source corpus.
This paper aims to highlight the potential applications and limits of a large language model (LLM) in healthcare. ChatGPT is a recently developed LLM that was trained on a massive dataset of text for dialogue with users. Although AI-based language models like ChatGPT have demonstrated impressive capabilities, it is uncertain how well they will perform in real-world scenarios, particularly in fields such as medicine where high-level and complex thinking is necessary. Furthermore, while the use of ChatGPT in writing scientific articles and other scientific outputs may have potential benefits, important ethical concerns must also be addressed. Consequently, we investigated the feasibility of ChatGPT in clinical and research scenarios: (1) support of the clinical practice, (2) scientific production, (3) misuse in medicine and research, and (4) reasoning about public health topics. Results indicated that it is important to recognize and promote education on the appropriate use and potential pitfalls of AI-based LLMs in medicine.
Subfields: General Health Professions, Health Informatics, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/6c7ba2af4b3e472bd8a5717367b88dcdd4abbd31
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In General Health Professions
In Health Informatics
In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics (2023, neutral): “215000 instances Link Alpaca-EN-AN [168] Inst. 52K instructions Link Alpaca-CH-AN [168] Inst.”
- The application of large language models in medicine: A scoping review (2024, neutral): “47,48 This advancement aids in improving the training efficiency of medical personnel, thereby enhancing the skills and capabilities of future healthcare professionals.”
- ChatGPT in Healthcare: A Taxonomy and Systematic Review (2023, background): “[20] medical research brief report comments on using ChatGPT in healthcare (e.”
- Utility of ChatGPT in Clinical Practice (2023, neutral): “While not all of the proposed fraudulent uses are unique to ChatGPT, what is impressive is the effective acceleration of ChatGPT in creating false evidence and material with a high degree of plausibility [16].”
- A study of generative large language model for medical research and healthcare (2023, neutral): “…facilitate documentation of patient reports (e.g., a progress report) 3,4 , improving diagnostic accuracy 5 , and assisting in various clinical care 6,7 , while at the same time concerning the hallucinations and fabrications 7,8 , bias and stereotype 9 , and risks of patient privacy and ethics 10 .”
- A Review on Large Language Models: Architectures, Applications, Taxonomies, Open Issues and Challenges (2024, background): “Revolutionizing Healthcare: LLMs find applications in medical record analysis [129], diagnosis assistance, and drug discovery.”
- ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns (2023, background): “This comes in light of the ability of ChatGPT to generate incorrect content that appears plausible from a scientific point of view [81].”
- A Survey on Evaluation of Large Language Models (2023, background): “In the field of medical assistance, LLMs demonstrate potential applications, including research on identifying gastrointestinal diseases [92], dementia diagnosis [205], accelerating the evaluation of COVID-19 literature [86], and their overall potential in healthcare [15].”
- Investigating the Impact of User Trust on the Adoption and Use of ChatGPT: Survey Analysis (2023, neutral): “Our findings align with and extend the current understanding of other studies by identifying key factors influencing user adoption, such as trustworthiness, algorithm quality, privacy, transparency, and brand value [47-50].”