Cheungpasitporn, Wisit

ABSTRACTBackgroundMetabolic dysfunction‐associated steatotic liver disease (MASLD) affects over one third of the global population and is closely linked to gastrointestinal, oncological, and cardiovascular diseases. As the most common cause of steatotic liver disease (SLD), MASLD is often referred to as “fatty liver” in general health information searches. With the Internet becoming a primary source of healthcare information, this study aimed to evaluate global and regional public interest in steatotic liver disease using Google Trends data from January 2004 to December 2024.MethodsA comprehensive analysis was performed using Google Trends with the search term “fatty liver disease.” Interest levels were quantified using Google Trends indices, and temporal patterns were evaluated using average annual percent change (AAPC). Regional variations were assessed to identify geographic hotspots of public interest.ResultsGlobal interest in MASLD demonstrated significant growth (AAPC: 6.77%, 95% Confidence interval [CI]: 5.33–8.23), with a 264% increase from 2004 to 2024. Peak interest was observed in August 2024 (index: 100) compared to the lowest point in June 2007. Iran showed the highest country‐specific search volume (index: 100), while within the United States, West Virginia demonstrated the greatest relative interest (index: 100). Key topics associated with MASLD included “liver,” “symptoms,” “disease,” and “therapy.”ConclusionsSubstantial growth in global public interest in MASLD, with distinct temporal and geographical patterns, provides insights to develop targeted awareness campaigns and educational interventions, particularly in regions with low search volumes. Future research should examine the relationship between online search patterns and clinical outcomes in MASLD.

Abstract Generative artificial intelligence (AI) holds immense potential for medical applications, but the lack of a comprehensive evaluation framework and methodological deficiencies in existing studies hinder its effective implementation. Standardized assessment guidelines are crucial for ensuring reliable and consistent evaluation of generative AI in healthcare. Our objective is to develop robust, standardized guidelines tailored for evaluating generative AI performance in medical contexts. Through a rigorous literature review utilizing the Web of Sciences, Cochrane Library, PubMed, and Google Scholar, we focused on research testing generative AI capabilities in medicine. Our multidisciplinary team of experts conducted discussion sessions to develop a comprehensive 32‐item checklist. This checklist encompasses critical evaluation aspects of generative AI in medical applications, addressing key dimensions such as question collection, querying methodologies, and assessment techniques. The checklist and its broader assessment framework provide a holistic evaluation of AI systems, delineating a clear pathway from question gathering to result assessment. It guides researchers through potential challenges and pitfalls, enhancing research quality and reporting and aiding the evolution of generative AI in medicine and life sciences. Our framework furnishes a standardized, systematic approach for testing generative AI's applicability in medicine. For a concise checklist, please refer to Table S or visit GenAIMed.org .