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

• Author: Kovacova, Maria

1. Introduction

   Generative artificial intelligence technologies (Andronie et al., 2021; Kliestik et al., 2020; Pop et al., 2023) assist healthcare professionals in swiftly performing clinical reasoning, considering medical conditions and recovery capabilities, and diagnostic and judgment errors. The purpose of our systematic review is to examine the recently published literature on generative artificial intelligence-driven healthcare systems and integrate the insights it configures on patient record analysis, on disease diagnosis and monitoring, and on customized treatment plans. By analyzing the most recent (2023) and significant (Web of Science, Scopus, and ProQuest) sources, our paper has attempted to prove that ChatGPT can generate clinical decision support suggestions and guidelines in learning health system development, improving clinical workflow by integrating deep reinforcement learning algorithms. The actuality and novelty of this study are articulated by addressing how clinical data configured by ChatGPT and machine learning algorithms (Andronie et al., 2023; Kovacova et al., 2022; Pop et al, 2021) can assist medical professionals in determining precise and informed diagnoses through clear and concise information, that is an emerging topic involving much interest. Our research problem is whether generative artificial intelligence tools (Balcerzak et al, 2022; Lazaroiu et al, 2020; Popescu et al, 2017a) further clinical documentation, formulate discharge summaries, generate procedure notes, answer specific questions, diagnose medical record-based conditions, and recommend treatment options and plans.

   In this review, prior findings have been cumulated indicating that ChatGPT can recommend appropriate evidence-based treatments and predict abnormal events, being pivotal in disease diagnosis and monitoring. The identified gaps advance how ChatGPT can improve artificial intelligence-driven medical education practices and learning experiences with respect to professional knowledge, skills, and competence by use of computer vision algorithms (Blake, 2022; Nagy et al, 2023; Peters et al, 2023; Popescu et al, 2017b) and natural language processing. Our main objective is to indicate that generative artificial intelligence algorithms (Jaramillo-Aristizabal, 2022; Nica, 2017; Popescu, 2018; Vatamanescu et al., 2020) can improve treatment planning, provide clinical decision support efficiently, and summarize massive volumes of medical data.

2. Theoretical Overview of the Main Concepts

   ChatGPT can articulate customized treatment plans, handle large volumes of medical data, and improve patient outcomes. Generative artificial intelligence technologies assist healthcare professionals as regards patient treatment outcomes in emergency situations by use of visual computing and object recognition algorithms. Generative artificial intelligence can solve clinical cases, compose reliable medical texts, and provide healthcare solutions, integrating coherent outcomes, limitations, and implications through continuous improvement of deep learning algorithms. ChatGPT-based diagnostic and medical management answers pertaining to clinical decision support should be systematically inspected for potential factually incorrect outputs. The manuscript is organized as following: theoretical overview (section 2), methodology (section 3), ChatGPT can articulate customized treatment plans (section 4), generative artificial intelligence algorithms can streamline patient data gathering and inspection (section 5), generative artificial intelligence technologies assist healthcare professionals in swiftly performing clinical reasoning (section 6), discussion (section 7), synopsis of the main research outcomes (section 8), conclusions (section 9), limitations, implications, and further directions of research (section 10).

3. Methodology

   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 (Tables 1 and 2). 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 (Figures 1-6).

4. ChatGPT Can Articulate Customized Treatment Plans

   ChatGPT can improve artificial intelligence-driven medical education practices and learning experiences with respect to professional knowledge, skills, and competence by use of computer vision algorithms and natural language processing (Abd-alrazaq et al., 2023; Cai et al., 2023; Deiana et al., 2023; Sallam et al., 2023; Vaishya et al., 2023; Zhou et al., 2023), generating clinical case studies, serving as virtual patients, and providing personalized assistance. ChatGPT can recommend appropriate evidence-based treatments and predict abnormal events, being pivotal in disease diagnosis and monitoring.

   By configuring clinical guidelines based on patient data, ChatGPT can assist health care professionals in formulating accurate diagnoses and deciding on treatment plans, radiology reports, medical notes, and personalized decision support (Cifarelli and Sheehan, 2023; Liu et al., 2023a; Liu et al., 2023b; Putra et al., 2023; Sallam, 2023) while predicting patient outcomes. ChatGPT can generate clinical decision support suggestions and guidelines in learning health system development, improving clinical workflow by integrating deep reinforcement learning algorithms.

   ChatGPT can clarify medical procedure outcomes, misunderstandings, risks, and benefits by integrating patient history and health status and assisting consultation processes in making informed care decisions (Cadamuro et al., 2023; Giannos and Delardas, 2023; Mallio et al., 2023; Mondal et al., 2023; Sanmarchi et al., 2023; Xie et al., 2023), while simulating healthcare professional--patient consultations by providing coherent answers to patients seeking information in digital clinical guidance in health-specific contexts. ChatGPT can articulate customized treatment plans, handle large volumes of medical data, and improve patient outcomes. (Table 3)

5. Generative Artificial Intelligence Algorithms Can Streamline Patient Data Gathering and Inspection

   ChatGPT's real-time response capability, consistent information delivery, and comprehensive advice can enhance medical guidance and education, particularly when healthcare resources are insufficient or the areas are distant or underserved (Altamimi et al., 2023; Johnson et al., 2023; Levin et al., 2023; Morath et al., 2023; Rawashdeh et al., 2023; Wagner and Ertl-Wagner, 2023), harmonizing with professional medical consultation in the matter of triage support, personalized treatment, and specific clinical guidelines. Generative artificial intelligence algorithms can streamline patient data gathering and inspection, enabling precise diagnosis.

   Clinical data configured by ChatGPT and machine learning algorithms can assist medical professionals in determining precise and informed diagnoses through clear and concise information (Biswas, 2023; Darkhabani et al., 2023; Juhi et al., 2023; Kim et al., 2023; Rahsepar et al., 2023; Wen and Wang, 2023), while patient queries can be addressed effectively in terms of procedure purposes and possible risks. Generative artificial intelligence can solve clinical cases, compose reliable medical texts, and provide healthcare solutions, integrating coherent outcomes, limitations, and implications.

   Generative artificial intelligence technologies can develop clinical reasoning skills, practice, and documentation, decrease clerical work, improve patient care and medical education, and further evidence-based medicine (Grunebaum et al., 2023; Mohammed et al., 2023; Ravi et al., 2023; Thurzo et al., 2023), managing diagnostic uncertainty throughout safe and simulated settings. Generative artificial intelligence algorithms can improve treatment planning, provide clinical decision support efficiently, and summarize massive volumes of medical data. (Table 4)

6. Generative Artificial Intelligence Technologies Assist Healthcare Professionals in Swiftly Performing Clinical Reasoning

   ChatGPT-based diagnostic and medical management answers pertaining to clinical decision support should be systematically inspected for potential factually incorrect outputs, as generative artificial intelligence algorithms source information nontransparently...