healthcare

A systematic review of diffusion-based methods for medical image inpainting, covering architectures, applications, datasets, and evaluation strategies, with a proposed taxonomy and identification of challenges such as lack of standardized benchmarks.

Microsoft Research and the Broad Institute, with support from Dana-Farber Cancer Institute, are collaborating on Project Ex Vivo to use AI for better understanding cancer cell states and advancing precision oncology.

Forbes article explains split learning, a technique that enables AI models to train on sensitive data without exposing raw data, improving privacy compliance and reducing communication costs.

This paper evaluates GPT-4.1's ability to simulate personas for opinion prediction, achieving accurate election forecasts in eight out of nine U.S. states and high accuracy in predicting beliefs about childhood vaccines, though simulated dialogues lacked natural human flow.

A novel decision-aware machine learning framework was deployed nationwide in Sierra Leone to allocate essential medicines, achieving a 19% increase in consumption and covering 2 million women and children under five.

Proposes TRACER, a framework that integrates severity-grounded knowledge graphs and retrieval-augmented generation for trajectory-aware clinical risk prediction, achieving large gains in mortality and readmission prediction on MIMIC-III and MIMIC-IV datasets.

LLM4EHR proposes a clinical foundation model that temporally aligns Electronic Health Record time series with medical event sequences using a domain-adapted large language model and a regularized contrastive objective, improving downstream prediction tasks.

This study analyzes patterns of financial vulnerability before and after the COVID-19 pandemic using MEPS data and machine learning methods, finding that income level, insurance status, and prescription drug spending are key predictors, with persistent inequities.

Cura 1T is a healthcare-specialized LLM trained via a human-gated self-evolution loop that iteratively improves on patient consultation, clinical reasoning, and agentic healthcare tasks, achieving top performance on medical benchmarks while maintaining general reasoning ability.

AI is being piloted by the Trump administration for insurance prior authorization decisions, but physicians worry it may increase wrongful denials of necessary treatments.

Bunkerhill raised $55M from prominent investors to build AI agents for healthcare, citing life-saving outcomes and major efficiency gains in pilot health systems.

This paper presents the design and implementation of a safety-constrained LLM system for maternal and child health resource navigation, using a multi-layered architecture with domain-restricted RAG and boundary enforcement to ensure safety and reliability.

This essay explores the 'conversion trap' where AI delivers intelligence but fails to produce outcomes due to weak infrastructure and delivery systems, using COVID vaccine distribution as an example.

A developer shares their experience using AI for medical clinic content, emphasizing that human review is essential for accuracy and trust in healthcare, even when AI writes well.

The paper introduces ThReadMed-QA, a multi-turn medical dialogue dataset, and evaluates five LLMs on correcting patient misconceptions, finding substantial degradation over subsequent turns.

ChikitAI is an agentic AI product designed for healthcare triage and care automation, aiming to streamline patient intake and clinical workflows.

Muse Spark 1.1 outperforms GPT-5.6 Sol and Gemini 3.1 on Radiology's Last Exam 2.0, a new visual reasoning benchmark for autonomous AI diagnosis in healthcare, though it still lags behind Fable and human radiologists.

Queue's fully autonomous robotic pharmacy kiosk dispenses prescriptions in 60 seconds, filling 600 pills per minute at a Palo Alto pilot. The startup raised $18.6M and plans broader rollout by early 2027.

This paper evaluates retrieval-augmented generation (RAG) versus long-context prompting for clinical reasoning tasks over electronic health records, finding RAG to be token-efficient and competitive, especially for imaging extraction and antibiotic timeline reconstruction.

This paper introduces Feature Sufficiency Analysis (FSA), a framework to determine whether a subset of clinical features is sufficient for AI model predictions, with case studies in postoperative ventilation and mortality prediction.