Artificial Intelligence Medical Compendium

Models trained on protein sequences can predict mutational effects, but their outputs represent a convolution of different phenotypes. Isolating stability-independent activity effects is essential for rational enzyme design, allosteric drug design an... read more 

Multi-agent language-model systems increasingly assign roles to agents and let them deliberate. A role label, however, does not specify which evidence an agent can bring or which standard it applies before accepting a conclusion. Agents that read one... read more 

Deep learning (DL) applied to haematoxylin and eosin (H&E)-stained pathology images can predict microsatellite instability (MSI) in gastric cancer (GC). We evaluated its diagnostic accuracy and discriminative performance. PubMed, Embase, Cochrane Lib... read more 

High attrition rates across clinical drug development impose immense clinical and financial burdens, yet prospectively forecasting whether an individual trial will achieve its primary endpoint remains challenging. Here we present a machine-learning s... read more 

Cervical cytology screening relies on the visual assessment of nuclear and cellular morphology across thousands of cells per slide, a labor-intensive task that could benefit from quantitative computational support. Here we present CytoLens, a modular... read more 

Background: Hospital readmission is a common and costly outcome in patients with chronic obstructive pulmonary disease (COPD). Multimodal deep learning models combining structured electronic health record (EHR) data with physiological, imaging, and t... read more 

Purpose To evaluate the external transportability of source-derived probability calibration for chest radiograph deep learning classifiers under distribution shift and determine its dependence on sample size and disease prevalence. Materials and Meth... read more 

Background Differentiating bipolar disorder (BD) from major depressive disorder (MDD) during major depressive episodes remains a significant challenge. EDIT-B is an in vitro diagnostic test based on machine learning (ML) method integrating clinical m... read more 

Background: Large language models (LLMs) configured as virtual patients may expand structured practice of clinical reasoning in health professions education. Before testing effectiveness, it must be established whether students engage with them and w... read more 

PURPOSE: To demonstrate an end-to-end research workflow combining a large electronic health record (EHR) resource and a multi-agent artificial intelligence (AI) system governed by Human-Guided Orchestration (HGO), using sequential sacituzumab govitec... read more