Artificial Intelligence Medical Compendium

Catalytic metal-binding sites are established drug targets for many human diseases, yet systematic study of how metal function relates to disease is limited by the lack of a resource combining structural localization of human metal-binding sites with... read more 

Existing genomic foundation models (GFMs) are adept at capturing local, sequential dependencies in DNA. However, their architectures often struggle to efficiently model gene regulation features, such as sparse motifs and distal regulatory elements th... read more 

Biological foundation models encode rich information about protein structure, function, and cellular state in high-dimensional embeddings, but accessing specific biological properties typically relies on additional, task-specific prediction models. H... read more 

Understanding tissue organization requires accurate molecular quantification and faithful spatial localization. Spatial omics, particularly spatial transcriptomics (ST), face measurement trade-offs that limit simultaneous achievement of molecular and... read more 

Decision models like Jev read text and return probabilities, categories, or ordinal levels, with confidence scores for the latter two, at a fraction of large language models' (LLMs) cost. We explored how Jev could be integrated into clinical workflow... read more 

Background: Patients increasingly consult free artificial intelligence (AI) chatbots about their symptoms, but these models may favour agreement with the patient over accuracy. In obstructive sleep apnoea (OSA), where most cases in the general popula... read more 

Background Patients with normal perfusion, normal myocardial flow reserve (MFR) and no coronary artery calcium (CAC) on myocardial perfusion imaging are considered low risk. Yet, adverse events still occur and no routinely reported imaging marker str... read more 

Objective Large language models (LLMs) are increasingly used to generate, summarize, and evaluate clinical documentation, yet standardized methods for evaluating nuanced, context-dependent clinical language remain limited. Existing approaches rely pr... read more 

Significance: There is potential for generative artificial intelligence (AI) to perform "full stack" academic publishing, including comprehensive manuscript fabrication, "peer"-review, and editorial decision making. This study evaluated the benefits ... read more 

Background: Relapsing-remitting multiple sclerosis (RRMS) often progresses to secondary-progressive MS (SPMS), but diagnosis is typically retrospective, delaying potential interventions. Existing rule-based and machine learning approaches often lack ... read more