ABSTRACT. The aim of this systematic review is to synthesize and analyze generative artificial intelligence algorithms articulating specific treatment recommendations, clinical decision-making, correct diagnoses, patient outcomes, medical practices, and healthcare equity. In this research, prior findings were cumulated indicating that generative artificial intelligence tools automatically identify and segment various structures in medical images and enhance diagnostic accuracy and surgical planning. We carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout April 2023, with search terms including “generative artificial intelligence-based diagnostic algorithms” + “disease risk detection,” “personalized and targeted healthcare procedures,” and “patient care safety and quality.” As we analyzed research published in 2023, only 186 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, we decided on 32, chiefly empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Distiller SR, ROBIS, and SRDR.

Keywords: ChatGPT; generative artificial intelligence; diagnostic algorithms; disease risk detection; personalized and targeted healthcare procedures; patient care safety and quality

How to cite: Bugaj, M., Kliestik, T., and Lăzăroiu, G. (2023). “Generative Artificial Intelligence-based Diagnostic Algorithms in Disease Risk Detection, in Personalized and Targeted Healthcare Procedures, and in Patient Care Safety and Quality,” Contemporary Readings in Law and Social Justice 15(1): 9–26. doi: 10.22381/CRLSJ15120231.

Received 22 April 2023 • Received in revised form 26 July 2023
Accepted 29 July 2023 • Available online 30 July 2023