Artificial Intelligence Medical Compendium

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 

Structure-guided mutation design requires relating a proposed substitution to its molecular environment to anticipate its effects. Existing evaluations of large language models (LLMs) in protein engineering emphasize sequence-based prediction or tool... read more 

Abstract. Base editors install single nucleotide substitutions without a double strand break or donor template, but editing at multiple susceptible positions can generate bystander alleles. Predicting the frequency of an intended outcome therefore re... read more 

Programmable RNA sensors enable sequence-specific detection of RNA inputs for diagnostics, genetic circuit control, and multiplexed molecular readouts. Although readily programmable, their performance varies substantially with the target site selecte... read more 

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