Generative Artificial Intelligence-based Clinical Decision Support in Screening, Prevention, and Treatment Choices in Medical Care.

• Author: Henley, Susan

1. Introduction

   ChatGPT can improve task precision and efficiency (Krizanova et al, 2019; Lazaroiu, 2018; Peters et al., 2023; Popescu et al., 2017a), further patient education and consultation, and assist healthcare professionals in administrative duties. The purpose of my systematic review is to examine the recently published literature on generative artificial intelligence-based clinical decision support and integrate the insights it configures on screening, prevention, and treatment choices in medical care. By analyzing the most recent (2023) and significant (Web of Science, Scopus, and ProQuest) sources, my paper has attempted to prove that ChatGPT can contribute to a quite accurate diagnosis, handle clinical trial data and patient care adequately, and diversify screening, prevention, and treatment choices in medical care. The actuality and novelty of this study are articulated by addressing how deep neural network-based generative artificial intelligence algorithms (Andronie et al, 2023; Lewkowich, 2022; Rowland, 2022) can optimize patient engagement by automated clinical history configuration and preexisting medical knowledge integration, that is an emerging topic involving much interest. My research problem is whether ChatGPT complements clinical knowledge and experience in terms of relevant patient information and healthcare education quality assessment, effectiveness, usability, and accuracy.

   In this review, prior findings have been cumulated indicating that ChatGPT can optimize surgical efficiency and safety, extracting and assessing heterogeneous parameters from medical images, building user trust and engagement. The identified gaps advance how deep learning technique-based (Dabija et al., 2023; Nica, 2018; Popescu, 2018; Valaskova et al, 2022) ChatGPT provides clinical practice support through coherent and realistic medical content, compose medical notes concerning ongoing treatments, and configure respiratory and hemodynamic parameters. My main objective is to indicate that generative artificial intelligence tools and big data analytics (Andronie et al, 2021; Glogovetan et al, 2022; Nica et al, 2023) further disease prevention, prognosis, and treatment, detecting intricate patterns and relationships (Balica and Cutitoi, 2022; Nica, 2017; Popescu et al, 2017b; Vinerean et al, 2022) by inspecting massive quantities of medical data.

2. Theoretical Overview of the Main Concepts

   Generative artificial intelligence tools and big data analytics further hospital workflows by task precision and efficiency optimization, life-saving procedure initiation, and prehospital emergency care. Generative artificial intelligence technology can assist clinical workflow as regards low-complexity tasks enabling data flow, operating in unstructured environments making increasing administrative and clinical workflow more efficient. Fact-checking ChatGPT-based output with unclear and unselective information sourcing can improve queries formulated by physicians, furthering clinical practice guidelines and decision support tools and enhancing patient care. ChatGPT can assist in patient prioritization in terms of medical urgency, produce clinical data-based patient medical records and knowledge, and predict treatment outcomes. The manuscript is organized as following: theoretical overview (section 2), methodology (section 3), ChatGPT can be leveraged in patient data analysis (section 4), generative artificial intelligence tools further disease prevention, prognosis, and treatment (section 5), ChatGPT creates effective and individualized medical experiences (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 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 (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, MMAT, ROBIS, and SRDR (Figures 1-6).

4. ChatGPT Can Be Leveraged in Patient Data Analysis

   Generative artificial intelligence-based coherent and factually correct medical knowledge can produce analytical depth and context (Abd-alrazaq et al., 2023; Gabrielson et al., 2023; Nov et al., 2023), articulating comprehensive and accurate guidance, competence upskilling, and clinical reasoning skills. Generative artificial intelligence technology can assist clinical workflow as regards low-complexity tasks enabling data flow, operating in unstructured environments making increasing administrative and clinical workflow more efficient.

   ChatGPT can be leveraged in patient data analysis (e.g., vital signs and imaging findings) and in making real time medical decisions, advising healthcare professionals as regards the exemplary course of action (Fatani, 2023; Hassan et al., 2023; Mondal et al., 2023; Wagner and Ertl-Wagner, 2023), leading to streamlined procedure and reduced complications by use of predictive analytics so as to provide relevant patient care recommendations. ChatGPT can optimize surgical efficiency and safety, extracting and assessing heterogeneous parameters from medical images, building user trust and engagement.

   Analyzing vital signs and laboratory results, generative artificial intelligence can detect clinical deterioration rapidly, and proactive intervention, through patient-specific data analysis (e.g., treatment history, genetic information, and biomarkers) (Cadamuro et al., 2023; De Angelis et al., 2023; Liu et al., 2023; Zhou et al., 2023), can produce tailored medical care recommendations and specify individual therapeutic responses. ChatGPT can assist in patient prioritization in terms of medical urgency, produce clinical data-based patient medical records and knowledge, and predict treatment outcomes. (Table 3)

5. Generative Artificial Intelligence Tools Further Disease Prevention, Prognosis, and Treatment

   Deep neural network-based generative artificial intelligence algorithms can optimize patient engagement by automated clinical history configuration and preexisting medical knowledge integration (Balel, 2023; Elkassem and Smith, 2023; Rawashdeh et al., 2023) while providing pertinent answers with the aim of patient harm risk decrease. ChatGPT complements clinical knowledge and experience in terms of relevant patient information and healthcare education quality assessment, effectiveness, usability, and accuracy.

   ChatGPT and machine learning techniques can adequately and accurately manage massive quantities of medical data (Ge and Lai, 2023; Meo et al., 2023; Parray et al., 2023; Sanmarchi et al., 2023), enhancing patient outcomes, enabling disease risk factor identification, and advancing efficient...