AI in healthcare
74 notes, newest first. Includes the vault tags healthcare, ai in healthcare, medical ai, medicine, medical imaging, drug discovery.
MedZERO: Self-Evolving Agents for Open-Ended Medical Reasoning Through Controlled Knowledge Accumulation
The preprint presents MedZERO, a self-evolving framework for open-ended medical reasoning. An Examiner generates medical question-option pairs, while a Reasoner solves them with evidence-grounded multi-turn reasoning…
Synthetic Hospital: An Open, Verifiable, Physician-Validated Longitudinal EHR Benchmark
The preprint describes a fully synthetic, fact-grounded longitudinal EHR benchmark built from public medical education material, with no PHI. The abstract reports 1,268 patients and 5,602 encounters, ICD-10-CM/SNOMED…
A knowledge-guided agentic framework for mitigating patient-context ambiguity in health queries
The paper introduces a framework to improve the performance of healthcare chatbots by reducing patient-context ambiguity in queries. The framework uses a knowledge graph to interpret queries, create plausible…
Adaptive Memory and Reflection Multi-Agent System for Medical Question Answering
Researchers introduce a new multi-agent system called Adaptive Memory and Reflection (AMR) for medical question answering. This system aims to address the limitations of existing systems by using specialized agents…
DentAgent: Evidence-Centric Multi-Agent Coordination for Multimodal Dental Reasoning
Researchers have developed DentAgent, an evidence-centric multi-agent framework to address the limitations of existing dental AI systems. This system integrates various modalities like domain knowledge, radiographs…
PGFS++: Molecular Property Improvement under Synthesis and Diversity Constraints
Researchers present PGFS++, an advancement of a previous method called PGFS for improving molecular properties like drug-likeness or binding affinity. The new method addresses limitations in PGFS by using trainable…
Browse articles
The article titled 'MultiMed-RAG: leveraging multi-source knowledge and agent collaboration for medical tasks' discusses a new system called CliniSense, which uses artificial intelligence to evaluate clinical skills…
CliniSense AI for automated clinical skills assessment with real-time feedback
Two articles discuss the development of AI systems for clinical skills assessment and a virtual trial framework for generating clinical evidence in orthopedics.
Part of the npj Series
An article titled 'MultiMed-RAG: Leveraging Multi-Source Knowledge and Agent Collaboration for Medical Tasks' published in the npj Series, was written by Yuxing Lu, Sin Yee Goi, and Jinzhuo Wang in 2026. The article…
Unsupervised Anomaly Detection for Image Dataset Quality Assurance in Multi-Center Breast MRI
Researchers propose an automated dataset quality assurance mechanism using unsupervised anomaly detection and out-of-distribution detection for multi-center dynamic contrast-enhanced breast MRI. The study compares four…
MIRA: Medical Image Reflection for Agentic Diagnosis
Researchers present MIRA, a medical image diagnosis framework that allows an autonomous agent to search for relevant evidence, verify the relevance and consistency of the acquired evidence, and adapt its tool-use…
Towards Expert-level Medical AI for Real-time Video Consultations
A new AI system called AMIE (Articulate Medical Intelligence Explorer) in a video configuration was tested for real-time clinical video consultations. The system performed on par or better than human primary care…
Towards Expert-level Medical AI for Real-time Video Consultations
Researchers have developed AMIE (Articulate Medical Intelligence Explorer) in a video configuration for real-time clinical video consultations, which performs at expert level compared to human primary care physicians…
Towards Expert-level Medical AI for Real-time Video Consultations
A new AI system called AMIE (Articulate Medical Intelligence Explorer) in a video configuration has been demonstrated to perform at expert-level for real-time clinical video consultations. This system, which integrates…
Does FLAIR super-resolution erase or hallucinate small white-matter lesions?
The study investigates whether using super-resolution (SR) techniques on Fluid-attenuated Inversion Recovery (FLAIR) scans affects the detection of small white-matter lesions. The research finds that SR tends to erase…
ECHO: A Locally-Deployable Agentic Health Assistant with Temporal Memory, Safety Guardrails, and Speech Assessment
The paper introduces ECHO, a locally-deployable health assistant designed for long-term chronic care management. It consists of three integrated modules: an agentic chatbot, a safety layer, and a speech assessment…
From Siloed Algorithms to Compliance-First Agentic Platforms: A Multi-Layered Architecture for Hospital AI Systems
The research proposes a multi-layered architecture for hospital AI systems aimed at addressing the challenges of isolated AI deployments in hospitals. The proposed architecture includes an Agent Orchestration Layer, a…
From Siloed Algorithms to Compliance-First Agentic Platforms: A Multi-Layered Architecture for Hospital AI Systems
The research proposes a multi-layered compliance-first Agentic AI architecture for hospital AI systems. This architecture aims to address the issues of duplicated effort, hidden risks, and unrealized value in current…
Improving the Realism of Synthetic Clinical Benchmarks Under Utility Constraints
Researchers propose a method to improve the realism of synthetic clinical benchmarks for AI agents in healthcare settings without compromising their downstream utility checks. They develop a framework that ensures…
Improving the Realism of Synthetic Clinical Benchmarks Under Utility Constraints
The authors propose a method to improve the realism of synthetic clinical benchmarks for enterprise AI agents while maintaining their utility. They focus on healthcare settings where operational data is hard to access…
Improving the Realism of Synthetic Clinical Benchmarks Under Utility Constraints
Researchers propose a method to improve the realism of synthetic clinical benchmarks for AI agents in privacy-sensitive healthcare settings without compromising their practical utility. They formulate this as a problem…
QuanTiMedAI: Quantum-Enhanced Time-Series Model guided by Agentic AI for Cardiac Arrest Mortality Prediction
Researchers introduce QuanTiMedAI, a framework for predicting cardiac arrest mortality using a combination of an agentic large language model (LLM) and a quantum recurrent network. The LLM is used for feature…
QuanTiMedAI: Quantum-Enhanced Time-Series Model guided by Agentic AI for Cardiac Arrest Mortality Prediction
The researchers present QuanTiMedAI, a quantum-agentic framework designed for predicting cardiac arrest mortality rates. This system combines an agentic large language model for clinically informed feature discovery…
QuanTiMedAI: Quantum-Enhanced Time-Series Model guided by Agentic AI for Cardiac Arrest Mortality Prediction
Researchers have developed a new framework called QuanTiMedAI for predicting mortality in cardiac arrest patients in intensive care units (ICUs). The framework uses agentic AI and quantum enhancement to analyze…
Tracing the Heart: An Evidence-Linked Pipeline for Heart-Failure Feature Engineering
Researchers developed an AI system called nMAS to automate the process of extracting structured and aggregated features from Electronic Health Records (EHR) for heart failure research. The system generates these…
Tracing the Heart: An Evidence-Linked Pipeline for Heart-Failure Feature Engineering
Researchers have developed an evidence-linked pipeline called Nimblemind Multi-Agent System (nMAS) to automate heart-failure feature engineering for electronic health record data. The system generates structured and…
Tracing the Heart: An Evidence-Linked Pipeline for Heart-Failure Feature Engineering
Researchers have developed a system called nMAS for automating heart-failure feature engineering in electronic health records (EHRs). The system generates structured and aggregated features that improve the accuracy of…
CoPlan: A Trustworthy Co-Intelligence Interface for Care Planning through Role-Based Contestable Argument Graphs
CoPlan is a new AI system designed to help with care planning for clinicians, patients, caregivers, and care teams. It allows stakeholders to inspect, challenge, and revise plans if they conflict with clinical…
CoPlan: A Trustworthy Co-Intelligence Interface for Care Planning through Role-Based Contestable Argument Graphs
The paper introduces CoPlan, a new AI system designed to aid in care planning. CoPlan allows various stakeholders like clinicians, patients, and caregivers to collaborate on complex decisions by generating potential…
Guideline-as-Oracle: Zero-Annotation Training of an Ophthalmic Telephone Triage Agent
Researchers present Guideline-as-Oracle (GAO), a method to train an ophthalmic telephone triage agent without relying on expensive expert annotations or confidential clinical conversations. Instead, they use a rule…
ClinFusion: A Vision-Centric Multimodal LLM System for Holistic Medical Understanding
Researchers have developed a new multimodal large language model called ClinFusion for understanding medical information from various image sources. The model outperforms leading open-source models and proprietary ones…
Closed-Loop Validation-Repair for Healthcare Interoperability: A Multi-Model Study of Schema Compliance in Clinical LLMs
The study evaluates three large language models (Qwen2.5 7B, Llama 3.1 8B, and Gemma2 9B) in the context of healthcare interoperability. The models were assessed across 320 clinical scenarios for their compliance with…
Failures Reveal What Metrics Miss: An Evidence-Driven Agent for Recursive Refinement of ECG Classifiers
Researchers present a new framework called RecursiveECG that uses a language model (LLM) to improve ECG classifiers by analyzing the reasons for individual case failures and suggesting targeted revisions based on…
KANEx: Translating Kolmogorov-Arnold Networks' Interpretability to Medical Explainability
A new framework, KANEx, is introduced to leverage the inherently interpretable components of Kolmogorov-Arnold Networks (KANs) for generating more reliable textual explanations in chest X-ray classifiers. The approach…
Myopia Prevention and Control 3.0: Artificial Intelligence--Driven Risk Stratification, Proactive Monitoring, and Personalized Intervention
The authors discuss Myopia Prevention and Control 3.0, a new approach that leverages artificial intelligence (AI), digital sensing, and ubiquitous computing to transform the way myopia is managed. This approach aims to…
Agent-Guided Relational Concept Discovery: Toward Interpretable Surgical Margin Assessment
Researchers propose a new framework called Agent-Guided Concept Discovery to improve the clinical adoption of deep learning models for surgical margin assessment using Rapid Evaporative Ionization Mass Spectrometry…
Agent-Guided Relational Concept Discovery: Toward Interpretable Surgical Margin Assessment
Researchers propose a new method called Agent-Guided Concept Discovery to help deep learning models assess surgical margins more effectively, particularly in noisy and unlabeled data acquired during surgery. The…
Agent-Guided Relational Concept Discovery: Toward Interpretable Surgical Margin Assessment
Researchers propose Agent-Guided Concept Discovery, a framework that helps deep learning models interpret REIMS data for surgical margin assessment by learning meaningful concepts directly from data without predefined…
Scaling Up Formal Representation of Clinical Trial Protocols in Ensemble Logic Using LLMs: A Preliminary Study
Researchers propose a pipeline called CT-TEL that uses Large Language Models (LLMs) to convert narrative clinical trial protocols into a format called Temporal Ensemble Logic (TEL). This conversion allows for automated…
Antigen-specific Antibody Multi-modal Foundation Model for Functional Antibody Design
Researchers have developed a new model called AAMFM to improve the design of functional antibodies. This model learns unified representations of antibody sequences and structures, considering antigen context, which…
GigaPath-Flash and GigaTIME-Flash: Efficient Pathology Foundation Models for Whole-Slide and Tumor Microenvironment Analysis
Researchers have developed two efficient deep learning models named GigaPath-Flash and GigaTIME-Flash for whole-slide pathology analysis and spatial proteomics prediction. These models are based on a compact ViT-S tile…
MedFailBench: A Clinician-Built Open-Source Benchmark for Medical AI Safety Boundary Inspection
The research paper introduces MedFailBench, an open-source benchmark for evaluating the safety of medical AI systems. The benchmark is designed by clinicians and focuses on identifying which safety boundary a system…
MedFailBench: A Clinician-Built Open-Source Benchmark for Medical AI Safety Boundary Inspection
MedFailBench is a new open-source benchmark for evaluating the safety of medical AI systems, created by clinicians. It focuses on identifying where a system has failed in terms of safety boundaries rather than just…
A Self-Evolving Agent for Longitudinal Personal Health Management
A research paper introduces HealthClaw, a self-evolving agent architecture for personal health management. The system updates support as a person's routines, preferences, measurements, and risks change. It was…
A Multi-Agent System for Autonomous, Fine-Tuning-Free Clinical Symptom Detection: Development and Validation Study
A new multi-agent system named Pythia was developed for automatically and independently creating and optimizing clinical concept extraction prompts without manual engineering or fine-tuning. The study compared Pythia…
A Multi-Agent System for Autonomous, Fine-Tuning-Free Clinical Symptom Detection: Development and Validation Study
Researchers present Pythia, a multi-agent system that automatically generates and optimizes extraction prompts for clinical concepts without manual engineering or fine-tuning. They compare Pythia with a curated lexicon…
A Multi-Agent System for Autonomous, Fine-Tuning-Free Clinical Symptom Detection: Development and Validation Study
A new system called Pythia was developed, which automatically generates and optimizes extraction prompts for clinical concepts without the need for manual prompt engineering or fine-tuning. The researchers compared…
Evidence-Grounded AI for Musculoskeletal Care
The paper introduces OrthoPilot, a clinical AI system that uses a large language model to integrate hospital data with external medical knowledge for continuous musculoskeletal management. The system outperformed 81…
DrugGen 2: A disease-aware language model for enhancing drug discovery
DrugGen 2 is a new model that generates small molecules for drug design by considering both disease ontology and target protein sequences. It was developed by fine-tuning a pre-trained GPT-2 model and outperformed…
OmniFood-Bench: Evaluating VLMs for Nutrient Reasoning and Personalized Health Advice
The paper introduces OmniFood-Bench, a new benchmark for evaluating Large Vision-Language Models (VLMs) in the context of personalized healthcare and dietary management. The benchmark is designed to assess the ability…
OmniFood-Bench: Evaluating VLMs for Nutrient Reasoning and Personalized Health Advice
A new benchmark, OmniFood-Bench, is introduced to evaluate the performance of Large Vision-Language Models (VLMs) in nutrient reasoning and personalized health advice for dietary management. The benchmark assesses VLMs…
The complexities of patient-centred conversational artificial intelligence
Researchers evaluated the performance of large language models in patient-chatbot conversations and found significant variations in communication patterns and emotional expression among real patients. They developed a…
The complexities of patient-centred conversational artificial intelligence
Researchers analyzed 2,053 conversations between patients and AI chatbots powered by large language models (LLMs) for symptom assessment. They found that communication patterns and emotional expressions vary widely…
Towards Precision Therapy in Hepatocellular Carcinoma: A Clinical-Reasoning LLM for Risk Stratification and Treatment Guidance
Researchers present HCC-STAR, a large language model that reads electronic medical records (EMRs) of patients with Hepatocellular Carcinoma (HCC), a common and deadly type of liver cancer. The model suggests treatments…
Vision Foundation Models in Radiology: A Scoping Review of Data, Methodology, Evaluation and Clinical Translation
The text discusses a scoping review of vision foundation models (VFMs) used in radiology. The study found that while these models are becoming common in radiological imaging, their development and evaluation methods…
Finding H. pylori in the Fine Print: Evidence-Linked Multi-Agent Case Finding from Gastric Biopsy Reports
Researchers evaluated a system called nMAS for extracting information about Helicobacter pylori infections from gastric biopsy reports. The system correctly identified the presence of H. pylori and associated…
Finding H. pylori in the Fine Print: Evidence-Linked Multi-Agent Case Finding from Gastric Biopsy Reports
Researchers evaluated a system called nMAS that can automatically extract information about Helicobacter pylori infection from gastric biopsy reports. The system had an accuracy of 98.61% in classifying four binary…
Evaluating and Understanding Model Editing for Medical Vision Language Models
A new clinical benchmark called M3Bench has been introduced to evaluate the effectiveness of model editing in medical vision-language models (VLMs). This benchmark tests whether an edit remains reliable, precise, and…
Toward Trustworthy Large Language Model Agents in Healthcare
A study presents CareConnect, a safety-first conversational agent for automating healthcare appointment scheduling using large language models (LLMs), retrieval-augmented generation (RAG), and safety guardrails. The…
Breaking Failure Cascades: Step-Aware Reinforcement Learning for Medical Multimodal Reasoning
The paper introduces Medical Reasoning-aware Policy Optimization (MRPO), a reinforcement learning algorithm for medical multimodal reasoning, which aims to optimize the reasoning process and reduce cascading errors…
Scientific Explanations in Health Sciences: Causality, Trust, and Epistemic Adequacy
This research paper discusses the need for better understanding and transparency in Artificial Intelligence (AI) used in healthcare, focusing on the explanatory process of Machine Learning models. It highlights that…
SIMAX: A Scalable and Interpretable Framework for Multi-Fidelity and Annotated Clinician-Patient Dialogue Simulation
The study introduces SIMAX, a framework that generates controlled clinical dialogue data with reference behavioral annotations for evaluating AI-driven communication coding systems. The generated dialogues are of…
A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Models
A new synthetic clinical notes pipeline is introduced to generate a dataset that can be used for developing and evaluating AI tools in healthcare. The pipeline generates structured patient information, simulates…
A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Models
A new pipeline for creating synthetic clinical notes is introduced to help develop AI systems in healthcare without compromising privacy. The pipeline generates structured, semi-structured, and unstructured clinical…
AI Healthcare Chatbots as Information Infrastructure: A Large-Scale Study of User-Reported Breakdowns
Researchers analyzed over 15,000 user reviews from 59 AI healthcare chatbot apps to understand the performance of these systems in everyday use. They identified three main issues: access barriers and service…
AI Healthcare Chatbots as Information Infrastructure: A Large-Scale Study of User-Reported Breakdowns
This study analyzes over 15,000 user reviews of AI healthcare chatbot apps to identify common issues reported by users. The research found three main breakdowns: access barriers and service unreliability, user…
AI Healthcare Chatbots as Information Infrastructure: A Large-Scale Study of User-Reported Breakdowns
This research study examines over 15,000 user reviews of AI healthcare chatbot apps to identify common breakdowns in their performance. The study found three main issues: access barriers, service unreliability, user…
AI Healthcare Chatbots as Information Infrastructure: A Large-Scale Study of User-Reported Breakdowns
A study analyzing over 15,000 user reviews of AI healthcare chatbot apps found recurring issues with access barriers, service unreliability, user experience, interaction quality, billing, and customer support. Privacy…
Language-Based Digital Twins for Elderly Cognitive Assistance
The paper proposes a language-based digital twin framework that uses large language models to mimic the conversational behavior of elderly individuals, helping in early detection of Mild Cognitive Impairment (MCI). A…
Accuracy and Satisfaction in Multi-Turn LLM Dialogues for NFR Assessment
Researchers evaluate the accuracy and quality of conversations between developers and a Language Model (LLM)-based agent in the context of Health Insurance Portability and Accountability Act (HIPAA) regulatory…
DeepBD: A Grounded Agentic Workflow for Variant Prioritization and Diagnosis of Genetic Birth Defects
DeepBD is a workflow developed by researchers to help diagnose genetic birth defects using exome and genome sequencing data. The workflow uses a language model (LLM) to organize the interpretation process, and it…
DeepBD: A Grounded Agentic Workflow for Variant Prioritization and Diagnosis of Genetic Birth Defects
Researchers present DeepBD, an AI system designed to help diagnose genetic birth defects by prioritizing patient-specific candidate variants. The system uses a workflow with various components such as rule evidence…
EHR-Complex: Benchmarking Medical Agents for Complex Clinical Reasoning
Researchers introduce EHR-Complex, a new benchmark for evaluating the performance of medical AI agents in handling complex clinical database reasoning tasks. The benchmark is built on MIMIC-IV data and comprises about…
PsyBridge: A Hybrid Intelligent Framework for Multi-Dimensional Mental Health Assessment and Decision Support
The study introduces PsyBridge, a hybrid intelligent framework aimed at improving the multi-dimensional assessment of mental health by integrating clinically validated screening tools, cognitive evaluations, and…
