Generative Artificial Intelligence-driven Healthcare Systems in Patient Record Analysis, in Disease Diagnosis and Monitoring, and in Customized Treatment Plans
Generative Artificial Intelligence-driven Healthcare Systems in Patient Record Analysis, in Disease Diagnosis and Monitoring, and in Customized Treatment Plans

Summary/Abstract: In this article, we cumulate previous research findings indicating that generative artificial intelligence tools further clinical documentation, formulate discharge summaries, generate procedure notes, answer specific questions, diagnose medical record-based conditions, and recommend treatment options and plans. We contribute to the literature on how clinical data configured by ChatGPT and machine learning algorithms can assist medical professionals in determining precise and informed diagnoses through clear and concise information by showing that generative artificial intelligence algorithms can improve treatment planning, provide clinical decision support efficiently, and summarize massive volumes of medical data. 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-driven healthcare system” + “patient record analysis,” “disease diagnosis and monitoring,” and “customized treatment plans.” As we inspected research published in 2023, only 191 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, we decided upon 49, 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, ROBIS, and SRDR.

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