ABSTRACT. This article reviews and advances existing literature concerning how deep neural network-based generative artificial intelligence algorithms can optimize patient engagement by automated clinical history configuration and preexisting medical knowledge integration. In this research, previous findings were cumulated showing that ChatGPT complements clinical knowledge and experience in terms of relevant patient information and healthcare education quality assessment, effectiveness, usability, and accuracy, and I contribute to the literature by indicating that generative artificial intelligence tools and big data analytics further disease prevention, prognosis, and treatment, detecting intricate patterns and relationships by inspecting massive quantities of medical data. Throughout March 2023, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “generative artificial intelligence-based clinical decision support” + “screening in medical care,” “prevention in medical care,” and “treatment choices in medical care.” As research published in 2023 was inspected, only 176 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, I selected 31 mainly empirical sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, MMAT, ROBIS, and SRDR.

Keywords: ChatGPT; generative artificial intelligence; clinical decision support; screening; prevention; treatment; medical care

How to cite: Henley, S. (2023). “Generative Artificial Intelligence-based Clinical Decision Support in Screening, Prevention, and Treatment Choices in Medical Care,” Contemporary Readings in Law and Social Justice 15(1): 27–44. doi: 10.22381/CRLSJ15120232.

Received 23 March 2023 • Received in revised form 24 July 2023
Accepted 27 July 2023 • Available online 30 July 2023

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