The potential, limitations, and future of diagnostics enhanced by generative artificial intelligence
Author:
Hirosawa Takanobu1ORCID, Shimizu Taro1
Affiliation:
1. Department of Diagnostic and Generalist Medicine , 12756 Dokkyo Medical University , Tochigi , Japan
Abstract
Abstract
Objectives
This short communication explores the potential, limitations, and future directions of generative artificial intelligence (GAI) in enhancing diagnostics.
Methods
This commentary reviews current applications and advancements in GAI, particularly focusing on its integration into medical diagnostics. It examines the role of GAI in supporting medical interviews, assisting in differential diagnosis, and aiding clinical reasoning through the lens of dual-process theory. The discussion is supported by recent examples and theoretical frameworks to illustrate the practical and potential uses of GAI in medicine.
Results
GAI shows significant promise in enhancing diagnostic processes by supporting the translation of patient descriptions into visual formats, providing differential diagnoses, and facilitating complex clinical reasoning. However, limitations such as the potential for generating medical misinformation, known as hallucinations, exist. Furthermore, the commentary highlights the integration of GAI with both intuitive and analytical decision-making processes in clinical diagnostics, demonstrating potential improvements in both the speed and accuracy of diagnoses.
Conclusions
While GAI presents transformative potential for medical diagnostics, it also introduces risks that must be carefully managed. Future advancements should focus on refining GAI technologies to better align with human diagnostic reasoning, ensuring GAI enhances rather than replaces the medical professionals’ expertise.
Publisher
Walter de Gruyter GmbH
Reference21 articles.
1. Sai, S, Gaur, A, Sai, R, Chamola, V, Guizani, M, Rodrigues, JJPC. Generative AI for transformative healthcare: a comprehensive study of emerging models, applications, case studies, and limitations. IEEE Access 2024;12:31078–106. https://doi.org/10.1109/access.2024.3367715. 2. Liu, J, Wang, C, Liu, S. Utility of ChatGPT in clinical practice. J Med Internet Res 2023;25:e48568. https://doi.org/10.2196/48568. 3. Tu, T, Palepu, A, Schaekermann, M, Saab, K, Freyberg, J, Tanno, R, et al.. Towards conversational diagnostic ai. arXiv preprint arXiv:240105654. 2024. 4. Balas, M, Micieli, JA. Visual snow syndrome: use of text-to-image artificial intelligence models to improve the patient perspective. Can J Neurol Sci 2023;50:946–7. https://doi.org/10.1017/cjn.2022.317. 5. Han, T, Adams, LC, Bressem, KK, Busch, F, Nebelung, S, Truhn, D. Comparative analysis of multimodal large language model performance on clinical vignette questions. JAMA 2024;331:1320–1. https://doi.org/10.1001/jama.2023.27861.
|
|