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Notes on healthcare
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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…
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.
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…
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…
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…
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…
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…
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…
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…
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…
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…
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