Generative Artificial Intelligence-based Diagnostic Algorithms in Patient Data Processing, in Medical Image Analysis Systems, and in Healthcare Risk Assessment.

• Author: Stevens, Ann

1. Introduction

   ChatGPT can assist in personalized healthcare planning and outcomes, in medical condition diagnosis, and in patient data processing. The purpose of my systematic review is to examine the recently published literature on generative artificial intelligence-based diagnostic algorithms and integrate the insights it configures on patient data processing, on medical image analysis systems, and on healthcare risk assessment. By analyzing the most recent (2023) and significant (Web of Science, Scopus, and ProQuest) sources, my paper has attempted to prove that, based on robust patient medical history, ChatGPT can provide complex answers in relation to medical scenarios based on real-time knowledge development (Andronie et al., 2023a; Kliestik et al., 2020; Pop et al., 2023) and evidence-based clinical support resources. The actuality and novelty of this study are articulated by addressing how ChatGPT integrates therapy-accompanying applications, disease-specific symptoms, clinical evaluation, decisions and diagnosis, healthcare risk assessment, and evidence-based medical knowledge and history, that is an emerging topic involving much interest. My research problem is whether ChatGPT can improve patient engagement, generate accurate and coherent medical reports, diminish errors and cognitive load (Andronie et al., 2023b; Lazaroiu et al., 2020; Popescu et al., 2017a; Rowland, 2022), and configure abstract reasoning tasks and medical information contextualization.

   In this review, prior findings have been cumulated indicating that ChatGPT can assist in biomedical data support by use of image recognition technologies (Dabija et al., 2018; Nica, 2017; Peters et al., 2023; Popescu, 2018) and medical imaging data, determining the exemplary course of action. The identified gaps advance how generative artificial intelligence algorithms (Andronie et al., 2021; Dabija et al., 2023; Perkins, 2022) can assist in medical report and data analysis improvement. My main objective is to indicate that ChatGPT can formulate coherent discharge summaries, significant evidence-based content (Balcerzak et al., 2022; Lewkowich, 2022; Popescu et al., 2017b), and detailed operative notes, integrating particular medications, consultation time, and follow-up instructions.

2. Theoretical Overview of the Main Concepts

   ChatGPT assists healthcare systems in disease risk evaluation, treatment planning, diagnoses, and scheduling appointments, thus decreasing the workload. Generative artificial intelligence algorithms enhance personalized healthcare planning, real-time support and guidance, and healthcare task accuracy and efficiency. ChatGPT-based medical image analysis systems can ensure swift and accurate diagnostic predictions, configure personalized treatment options, and enhance healthcare quality and patient outcomes. ChatGPT-based medical image analysis systems can process large volumes of medical datasets swiftly, make clinical decisions, and assess patient medical histories. Generative artificial intelligence algorithms enhance treatment options and clinical workflows and practice, medical diagnostic decision support systems, and patient outcomes. The manuscript is organized as following: theoretical overview (section 2), methodology (section 3), ChatGPT assists health care systems in disease risk evaluation, treatment planning, and diagnoses (section 4), ChatGPT can assist in personalized healthcare planning and outcomes (section 5), generative artificial intelligence algorithms enhance treatment options and clinical workflows and practice (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, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including "generative artificial intelligence-based diagnostic algorithms" + "patient data processing," "medical image analysis systems," and "healthcare risk assessment." As research published in 2023 was inspected, only 169 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 28 mainly 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: AMSTAR, Dedoose, Distiller SR, and SRDR (Figures 1-6).

4. ChatGPT Assists Healthcare Systems in Disease Risk Evaluation, Treatment Planning, and Diagnoses

   ChatGPT can provide access to quality-controlled medical information (i.e., peer-reviewed published data) to both health care professionals and patients, enabling up-to-date knowledge sharing and guidance (Fatani, 2023; Richu et al., 2023; Walker et al., 2023), but rating- or score-based critical assessment and source transparency are required. ChatGPT can assist in biomedical data support by use of image recognition technologies and medical imaging data, determining the exemplary course of action.

   ChatGPT integrates therapy-accompanying applications, disease-specific symptoms, clinical evaluation, decisions and diagnosis, healthcare risk assessment, and evidence-based medical knowledge and history, articulating consistent accuracy of responses (Baumgartner, 2023; Sedaghat, 2023; Wen and Wang, 2023), but critical professional assessment and validation are needed. ChatGPT-based medical image analysis systems can ensure swift and accurate diagnostic predictions, configure personalized treatment options, and enhance healthcare quality and patient outcomes.

   ChatGPT can be deployed to articulate individual need-based personalized learning materials and experiences (Abd-alrazaq et al., 2023; Alhaidry et al., 2023; Putra et al., 2023), tailored study plans, real-time feedback and guidance, virtual patient simulations, diagnostic decisions, and treatment plans. ChatGPT assists healthcare systems in disease risk evaluation, treatment planning, diagnoses, and scheduling appointments, thus decreasing the workload. (Table 3)

5. ChatGPT Can Assist in Personalized Healthcare Planning and Outcomes

   ChatGPT can provide accessible medical advice to real-time inquiries from remote or underserved regions with scarce healthcare resources, and users can acquire preliminary information and prompt guidance concerning possible complications and recovery (Altamimi et al., 2023; Elkassem and Smith, 2023; Zhou et al., 2023), reducing human error risks. ChatGPT can improve patient engagement, generate accurate and coherent medical reports, diminish errors and cognitive load, and configure abstract reasoning tasks and medical information contextualization.

   ChatGPT assists in medical documentation by producing efficient and accurate patient clinical letters, discharge summaries, and medical reports and notes in healthcare system integration (Deiana et al., 2023; Liu et al...