Generative Artificial Intelligence-driven Healthcare Systems in Medical Imaging Analysis, in Clinical Decision Support, and in Patient Engagement and Monitoring.

• Author: Hayward, Ray

1. Introduction

   ChatGPT can articulate possible diagnoses and treatment options through patient symptom, medical history, and laboratory outcome processing. The purpose of my systematic review is to examine the recently published literature on generative artificial intelligence-driven healthcare systems and integrate the insights it configures on medical imaging analysis, on clinical decision support, and on patient engagement and monitoring. By analyzing the most recent (2023) and significant (Web of Science, Scopus, and ProQuest) sources, my paper has attempted to prove that ChatGPT is of high interest for users aiming prompt answers, diagnostic suggestions, and health concern reassurance by integrating massive volumes of knowledgeable and reliable (Andronie et al., 2023a; Lewkowich, 2022; Pop et al, 2023; Vatamanescu et al., 2022) medical data. The actuality and novelty of this study are articulated by addressing how generative artificial intelligence (Andronie et al, 2023b; Nagy et al., 2023; Popescu et al., 2017a) can enhance treatment accuracy and efficiency, patient communication and outcomes, clinical decision support, computer-aided detection and diagnosis processes, and medical imaging analysis, that is an emerging topic involving much interest. My research problem is whether, improving clinical reasoning, medical training, and guideline-recommended healthcare management, ChatGPT can adequately handle medical information and provide suitable responses to elaborate questions, without presently being a proxy for critical thinking.

   In this review, prior findings have been cumulated indicating that ChatGPT can increase patient engagement and monitoring across diagnosis support systems, and optimize image processing (Barbu et al, 2021; Nica et al, 2023; Peters et al., 2023; Popescu, 2018) and analysis (Balcerzak et al., 2022; Nica et al., 2022; Popescu et al., 2017b) in specialized care. The identified gaps advance how ChatGPT assists in treatment, diagnosis, and medication provision, integrating health-related reliable and accurate information and sources and shaping medical knowledge intrinsic configurations. My main objective is to indicate that ChatGPT and machine-learning-based expertise and judgment (Andronie et al., 2021; Kliestik et al., 2020; Novak et al., 2022; Popescu et al., 2020) in personalized care can provide adequate treatment recommendations in conformity with the patient clinical presentation, disease severity, and co-diagnoses.

2. Theoretical Overview of the Main Concepts

   Generative artificial intelligence tools can enhance diagnostic precision and clinical practice, provide swift access to precise medical information and care, and reduce healthcare workload. Generative artificial intelligence algorithms can be leveraged in relation to clinical imaging analysis, human infection management and diagnosis, public health surveillance, clinical infection practice and decision support systems, and microbiome-based therapies. Generative artificial intelligence tools can enhance customized healthcare plans and preventive and therapeutic approaches and further medical image analysis, enabling personalized patient care. The manuscript is organized as following: theoretical overview (section 2), methodology (section 3), ChatGPT can increase patient engagement and monitoring across diagnosis support systems (section 4), generative artificial intelligence tools can enhance diagnostic precision and clinical practice (section 5), ChatGPT can adequately handle medical information and provide suitable responses to elaborate questions (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-driven healthcare systems" + "medical imaging analysis," "clinical decision support," and "patient engagement and monitoring." 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 33 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 Can Increase Patient Engagement and Monitoring across Diagnosis Support Systems

   Deep learning-based and data enhancement techniques can optimize medical image generation task performance through massive medical imaging dataset analysis (Gong et al., 2023; Nov et al., 2023; Theodosiou and Read, 2023) by integrating generative artificial intelligence algorithms in cognitive and affective computing processes. Generative artificial intelligence algorithms can be leveraged in relation to clinical imaging analysis, human infection management and diagnosis, public health surveillance, clinical infection practice and decision support systems, and microbiome-based therapies.

   Generative artificial intelligence tools can optimize oral exams and presentations, multimodal activity assignments, hands-on activities, peer evaluations, and group projects (Abd-alrazaq et al., 2023; Lee, 2023; Putra et al., 2023; Zhou et al., 2023), enabling analytical and critical reasoning, teamwork creativity, and argument soundness and precision in medical education. ChatGPT can increase patient engagement and monitoring across diagnosis support systems, and optimize image processing and analysis in specialized care.

   ChatGPT can enable virtual patient--physician interactions, assisting healthcare professionals in optimizing clinical workflow, in prioritizing patients, and in providing preliminary evaluations and remote home care guidance (Liu et al., 2023; Richu et al., 2023; Sallam, 2023; Venerito et al., 2023), while supporting evidence-based practice and configuring clinical practice effectiveness through information accuracy across health care workflows. By dynamically assisting healthcare professionals, ChatGPT can be instrumental in medical knowledge, conditions, and diagnoses, and in treatment options, while providing appropriate care. (Table 3)

5. Generative Artificial Intelligence Tools Can Enhance Diagnostic Precision and Clinical Practice

   Integrating generative artificial intelligence tools and machine learning technologies in medical consultations can enhance patient education and outcomes in acute healthcare conditions in terms of clinical assessment and symptom progression (Altamimi et al., 2023; Mohammed et al., 2023; Sedaghat, 2023), guiding treatment decisions together with professional medical care. Generative artificial intelligence tools can enhance customized healthcare plans and preventive and therapeutic approaches and further medical image analysis, enabling personalized patient care.

   ChatCPT can expedite and enhance medical report finding summarization and image interpretation workflow, decreasing omissions and cognitive load, while improving context (Ali, 2023; Cadamuro et al., 2023; Elkassem and Smith, 2023; Srijan Chatterjee et al., 2023) by integrating prior report information and thus articulating a medical condition understanding and a tailored care plan design in conformity with user needs and preferences. Generative artificial intelligence tools can enhance diagnostic precision and clinical practice, provide...