ABSTRACT. The purpose of this study is to examine how ChatGPT can assist physicians and patients by inspecting complex datasets and producing personalized treatment recommendations. We contribute to the literature on how ChatGPT produce factually precise and contextually significant structured discussion feedback to elaborate and emerging clinical questions, by showing that generative artificial intelligence algorithms can be deployed as regards laboratory diagnostics, public health outbreak management, clinical decision support tools and trial data, patient care efficiency, and drug discovery. Throughout April 2023, we performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “generative artificial intelligence-based treatment planning” + “clinical decision-making,” “precision medicine,” and “personalized healthcare.” As we inspected research published in 2023, only 188 articles satisfied the eligibility criteria. By removing controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, we decided upon 40, generally empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Dedoose, MMAT, and SRDR.

Keywords: ChatGPT; generative artificial intelligence; treatment planning; clinical decision-making; precision medicine; personalized healthcare

How to cite: Grupac, M., Zauskova, A., and Nica, E. (2023). “Generative Artificial Intelligence-based Treatment Planning in Clinical Decision-Making, in Precision Medicine, and in Personalized Healthcare,” Contemporary Readings in Law and Social Justice 15(1): 45–62. doi: 10.22381/CRLSJ15120233.

Received 22 April 2023 • Received in revised form 25 July 2023
Accepted 28 July 2023 • Available online 30 July 2023