ABSTRACT The integration of large language models, particularly ChatGPT, into healthcare has attracted increasing attention due to their potential to support medical education, clinical decision‐making, administrative workflows, patient communication and clinical documentation. However, their adoption in healthcare also raises important concerns related to accuracy, reliability, privacy, professional supervision and validation in real clinical settings. This paper presents a systematic literature review (SLR) on the applications of ChatGPT in the health sector. The review follows established SLR guidelines and analyzes 25 primary studies selected from ACM Digital Library, IEEE Xplore and PubMed. The selected studies were examined according to four research questions focused on application areas, reported benefits, limitations and challenges and validation methods. The results show that ChatGPT has been mainly explored in medical education, diagnostic support, administrative task automation, oral health and medical research. Reported benefits include faster access to information, support for documentation, personalization, cost reduction and assistance in decision‐making. Nevertheless, the review also identifies relevant limitations, including hallucinations, inconsistent accuracy, context loss, ethical and legal risks, privacy concerns and dependence on prompt engineering. The analysis of validation methods shows that most studies rely on quantitative metrics, expert‐based assessment, qualitative evaluation or hybrid approaches, although real‐world clinical validation remains limited. This review provides a structured overview of current evidence and identifies research gaps that should be addressed to support the safe, reliable and effective use of ChatGPT and related LLM‐based tools in healthcare.

Over the last decade, clinical practice guidelines (CPGs) have become an important asset for daily life in healthcare organizations. Efficient CPG management and digitization can improve the quality of patient care and healthcare by reducing variability. CPG digitization, however, is a difficult, complex task because such guidelines are usually expressed as text, and this often results in the development of partial software solutions. There are currently many CPG suites (CPGS) for managing the CPG lifecycle, but they do not all provide full support for this lifecycle, making it more difficult to choose the one which will best meet the specific needs and requirements of a healthcare organization. This paper proposes a quality model which makes it possible to compare CPGs by highlighting each phase of the lifecycle. The research was conducted using a methodology that combined a systematic literature review with quality models. The paper also discusses how the proposed model was instantiated to evaluate and compare several current CPG-based execution systems.