ABSTRACT. This paper provides a systematic literature review of studies investigating how ChatGPT can be deployed in clinical decision support and practice, producing precise differential diagnosis lists and providing reliable disease and medical query-related information with regard to real-time monitoring and personalized treatment. The analysis highlights that generative artificial intelligence tools enable medical decision-making and analyze patient data, resulting in better patient outcomes and in enhanced access to care. Throughout March 2023, we performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “healthcare generative artificial intelligence tools” + “medical diagnosis,” “medical treatment,” and “medical prognosis.” As we inspected research published in 2023, only 174 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, we decided upon 38, generally empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Distiller SR, MMAT, and ROBIS.

Keywords: ChatGPT; healthcare generative artificial intelligence tool; medical diagnosis; treatment; prognosis

How to cite: Horak, J., Novak, A., and Voumik, L. C. (2023). “Healthcare Generative Artificial Intelligence Tools in Medical Diagnosis, Treatment, and Prognosis,” Contemporary Readings in Law and Social Justice 15(1): 81–98. doi: 10.22381/CRLSJ15120235.

Received 26 March 2023 • Received in revised form 27 July 2023
Accepted 29 July 2023 • Available online 30 July 2023