The medical field is constantly evolving, integrating the latest technologies to enhance patient care and treatment efficacy. While various methodologies are available to evaluate the quality of research studies, checklists are often favored for their efficiency and ease of use. In this study, we contribute to this area of research by 1) analyzing the components of the most widely used checklists, and 2) proposing a more comprehensive checklist, CLARITY AI, which synthesizes the strengths of existing tools. This study analyzed several established checklists—CLAIM, CONSORT, DECIDE, FUTURE, IJMEDI, PRISMA, SPIRIT, STARD, STARE-HI, and TRIPOD—with the goal of developing a comprehensive checklist for evaluating research studies. Each item in these checklists was carefully cataloged, labeled, and assessed. The analysis aimed to identify the most critical items for inclusion in a definitive checklist for research study evaluation. The final version of the checklist is a coherent integration of structural elements—such as Title, Abstract, and Introduction—and essential parameters like Study Identification and Data Handling. This synthesis results in a comprehensive tool for thorough study and research evaluation. By integrating the strengths of multiple established checklists, CLARITY offers a robust, systematic, and user-friendly framework for assessing research quality. This tool not only elevates research standards but also enhances transparency, reproducibility, and overall credibility in the field of medical AI studies. Its application has the potential to produce more reliable and effective healthcare solutions, ultimately improving patient outcomes and advancing medical research.