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Notes on ai in healthcare
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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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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