Healthcare Generative Artificial Intelligence Tools in Synthetic Patient Cases, in Medical Image Interpretation, and in Diagnosis and Treatment Plans.

• Author: Barker, Michael

1. Introduction

   Generative artificial intelligence algorithms (Balcerzak et al., 2022; Lazaroiu et al., 2020; Pelau et al., 2021) can provide personalized medical information and optimize healthcare delivery quality. The purpose of my systematic review is to examine the recently published literature on healthcare generative artificial intelligence tools and integrate the insights it configures on synthetic patient cases, on medical image interpretation, and on diagnosis and treatment plans. By analyzing the most recent (2023) and significant (Web of Science, Scopus, and ProQuest) sources, my paper has attempted to prove that ChatGPT can assist in disease progression and outcome precise prediction, and in managing large quantities of patient data. The actuality and novelty of this study are articulated by addressing how artificial intelligence-based medical and digital health technologies can be deployed in disease and condition prevention, diagnosis, treatment, and monitoring, that is an emerging topic involving much interest. My research problem is whether ChatGPT can assist in diagnosing medical conditions and in articulating physiological parameter monitoring and personalized healthcare services.

   In this review, prior findings have been cumulated indicating that based on spatial computing techniques (Barbu et al., 2021; Nagy and Lazaroiu, 2022; Pop et al., 2021) and pathology data, generative artificial intelligence algorithms (Blake, 2022; Nagy et al., 2023; Peters et al., 2023; Popescu et al., 2017a) can interpret medical images and handle patient data and symptoms. The identified gaps advance how ChatGPT harnesses deep learning techniques (Andronie et al., 2023; Kliestik et al., 2020; Novak et al., 2022; Popescu, 2018) to aid patients in appropriate personal healthcare management, assisting medical professionals in clinical decision support, patient monitoring, and laboratory diagnosis. My main objective is to indicate that developed on deep learning algorithms and big data analytics (Jaramillo-Aristizabal, 2022; Nica et al., 2023; Popescu et al., 2017b), ChatGPT can be instrumental in patient medical history gathering and analysis and in operative procedure safety and compliance.

2. Theoretical Overview of the Main Concepts

   ChatGPT can be instrumental in imaging modality-based diagnosis and in medical image investigation by use of artificial intelligence-based clinical decision support systems. Producing synthetic patient cases, generative artificial intelligence technologies are pivotal in clinic visit summaries and reasoning, in medication counseling practice, in diagnosis and treatment plans, and in vulnerable patient-related health equity, formulating plausible-sounding answers. Incorporating ChatGPT into enhanced capabilities of medical education assists low-resource and disadvantaged users in making accurate and reliable informed decisions through unique and personalized answers across engaging and realistic interactions. ChatGPT can enhance patient communication while being pivotal in pre-procedure checklist creation, routine task automation, protocol selection, administrative task burden decrease, and report generation. The manuscript is organized as following: theoretical overview (section 2), methodology (section 3), ChatGPT can assist in diagnosing medical conditions (section 4), generative artificial intelligence algorithms can interpret medical images and handle patient data and symptoms (section 5), ChatGPT can be instrumental in patient medical history gathering and analysis (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 "healthcare generative artificial intelligence tools" + "synthetic patient cases," "medical image interpretation," and "diagnosis and treatment plans." As research published in 2023 was inspected, only 171 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 26 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 Assist in Diagnosing Medical Conditions

   A generative artificial intelligence-driven personalized medical education system can assist health care professionals coherently, optimizing knowledge retention and study time, providing accurate feedback and effective learning materials (Abd-alrazaq et al., 2023; Fatani, 2023; Vaishya et al., 2023), integrating formative and summative assessment methods, and configuring reliable grading rubrics. Generative artificial intelligence algorithms can provide personalized medical information and optimize healthcare delivery quality.

   ChatGPT harnesses deep learning techniques to aid patients in appropriate personal healthcare management, assisting medical professionals in clinical decision support, patient monitoring, and laboratory diagnosis (Ali, 2023; Karabacak et al., 2023; Tirth et al., 2023) by producing contextually relevant responses as regards warning signs and symptoms. Incorporating ChatGPT into enhanced capabilities of medical education assists low-resource and disadvantaged users in making accurate and reliable informed decisions through unique and personalized answers across engaging and realistic interactions.

   Users may harness generative artificial intelligence technologies for self-diagnosis without considering possible risks and thus articulating performance expectancy, decision-making process perceptions, and risk--reward evaluation (De Angelis et al., 2023; Putra et al., 2023; Shahsavar and Choudhury, 2023) in relation to safe and streamlined integration of ChatGPT in healthcare findings. ChatGPT can assist in diagnosing medical conditions and in articulating physiological parameter monitoring and personalized healthcare services. (Table 3)

5. Generative Artificial Intelligence Algorithms Can Interpret Medical Images and Handle Patient Data and Symptoms

   Decreasing deficiencies in medical records and being pivotal in applying medical knowledge, ChatGPT assists in clinical decision-making by inspecting patient data (e.g., medical history), providing recommendations on a personalised basis, and in patient medical record transcription precisely and coherently (Bhattacharya et al., 2023; Rahsepar et al., 2023; Srijan Chatterjee et al., 2023), and thus healthcare professionals can provide patient care in the remaining time. ChatGPT can assist in disease progression and outcome precise prediction, and in managing large quantities of patient data.

   ChatGPT can produce accurate, consistent, and unambiguous clinical letters to patients, leading to healthcare system...