#healthcare

40 curated events

papersTODAY 04:00 UTC

Unsupervised Keypoint Method Detects Falls in Real Time Using Less Video Bandwidth

A new arXiv paper proposes an unsupervised approach to learning body keypoints for real-time fall detection, aimed at monitoring older adults in home and clinical settings. The authors compare their method against alternatives under realistic conditions and add predictive bandwidth reduction so that continuous video monitoring uses less data. The work targets a known gap: sustained in-person supervision is hard to maintain, while video streams must be practical to transmit.

fall-detection/computer-vision/video-monitoring/healthcare

papersTODAY 04:00 UTC

Deep Learning Study Targets Biomarkers of Early-Stage Liver Cancer

A new arXiv paper examines whether deep learning and explainable AI methods can help diagnose hepatocellular carcinoma and pinpoint biomarkers across five stages of disease progression. The work uses a transcriptomic biomarker dataset for liver cancer, aiming for models whose predictions can be traced to interpretable biological features. The authors frame it as an early exploration of combining accuracy with explainability in cancer diagnostics.

healthcare/deep-learning/explainable-ai/biomarkers

papersTODAY 04:00 UTC

KnowBench proposes effort-reduction benchmark for clinical AI evaluation

A new arXiv preprint introduces KnowBench, a benchmark that assesses clinical AI systems by how much work they save clinicians instead of how closely their outputs match reference texts or expert rubrics. The authors argue that existing evaluation methods were built for research settings and measure resemblance to an artifact rather than reduction of a real-world burden. The paper frames deployment-grounded effort reduction as a unified metric for clinical AI.

benchmarks/clinical-ai/evaluation/healthcare

papersTODAY 04:00 UTC

OptoAgent: Multi-Agent Framework for Vision Screening in Classrooms

Researchers introduced OptoAgent, a multi-agent AI framework designed to conduct opportunistic vision micro-screening of students in classroom settings. The system targets reduced distance vision in children, which often goes unnoticed because kids adapt to it rather than reporting problems. The work is published as an arXiv preprint.

agents/healthcare/arxiv/multi-agent

papersTODAY 04:00 UTC

Cross-Modal Attention Network Targets Speech Biomarkers of Cognitive Decline

A new arXiv paper introduces CCMAN, a cross-modal attention model designed to detect early cognitive decline from verbal fluency speech tasks. Unlike prior approaches that pool features over an entire recording, the method explicitly accounts for cognitive instability and aims to produce interpretable temporal biomarkers. The work is framed as a scalable, non-invasive complement to conventional clinical assessment.

speech/healthcare/interpretability/attention

papersTODAY 04:00 UTC

arXiv paper maps visual attribution of hand-drawn patterns to Parkinson's screening

A new arXiv preprint proposes an explainable screening approach for Parkinson's disease based on hand-drawn spirals and meanders. The work argues that tremor-driven oscillations, irregular strokes, and unstable curvature in these drawings reveal early neuromotor impairment. It then connects visual attribution methods to clinical reasoning so the model's outputs can be interpreted by clinicians.

research/healthcare/explainability/computer-vision

papersTODAY 04:00 UTC

LongAgent: History-Guided Agentic Search for Longitudinal Outcome Prediction

A new arXiv paper introduces LongAgent, an agentic search method that uses patient history to predict future medical outcomes from longitudinal data. The authors note that such datasets are heterogeneous, with many variables collected across different sources and time points. The approach aims to extract representations from this messy, multi-source data that improve downstream outcome prediction.

agents/healthcare/research/longitudinal-data

papersTODAY 04:00 UTC

arXiv Paper Proposes Hierarchy-Grounded Domains for Clinical Domain Generalization

A new arXiv preprint introduces a method that organizes clinical data into hierarchy-based domains with adjustable granularity, aiming to improve model generalization across shifting patient populations. The authors argue that standard domain generalization techniques fall short in healthcare settings, where data distributions vary between patient groups. The work was posted as a replacement version on arXiv's machine learning and AI categories.

research/healthcare/domain-generalization/arxiv

papersTODAY 04:00 UTC

Claim-Level Evaluation of Verbatim Citations in Clinical Question Answering

A new arXiv paper addresses the problem that citations attached to LLM answers in clinical question answering usually reference whole documents, which makes verification slow for busy clinicians. The authors propose an evaluation approach that works at the level of individual claims, requiring verbatim citation support that can be checked directly. The stated goal is to make system outputs verifiable by construction rather than by trust.

clinical-qa/citations/llm-evaluation/healthcare

papersTODAY 04:00 UTC

Attention-Enhanced Deep Learning Classifies Autism from 3D Gait Data

A new arXiv preprint describes a deep learning pipeline that uses 3D gait recordings to help identify autism spectrum disorder, aiming to sidestep the subjectivity and cost of standard clinical assessments. The approach adds attention mechanisms to the model and evaluates results across multiple data folds to test how stable the performance is. The work is presented as a step toward more objective, non-invasive screening tools, though the abstract only covers the motivation and method rather than deployment.

autism/deep-learning/healthcare/gait-analysis

papersTODAY 04:00 UTC

arXiv primer surveys evaluation methods for LLMs in healthcare

A new arXiv paper reviews how large language models used in clinical and medical settings should be assessed. It argues that evaluating these systems is harder than conventional machine learning evaluation for a variety of reasons. The work is framed as an introductory guide to evaluation approaches for healthcare LLMs.

healthcare/evaluation/research/llm

papersSEP 12 04:00 UTC

Voice-Interactive LLM Multi-Agent System Proposed for Smart Operating Rooms

A new arXiv paper describes SurgicalRoomAgent, a multi-agent architecture built on large language models for use in smart operating rooms. The system is designed to handle spoken commands, control connected devices, and keep intraoperative records, with the authors outlining the architecture and the key enabling technologies. The work is presented as a design and technology study rather than a clinical evaluation.

agents/llm/healthcare/voice

papersTODAY 04:00 UTC

MedTRACE: tool-augmented multimodal agents for evidence-grounded clinical decisions

A new arXiv paper introduces MedTRACE, an agent framework that combines tools with multimodal clinical reasoning instead of mapping electronic health records, medical images, and physiological signals straight to diagnoses. The work targets evidence-grounded decision-making so that outputs can be traced back to the underlying patient data. It is a research contribution and has not been described as a deployed clinical product.

agents/multimodal/healthcare/research

papersTODAY 04:00 UTC

Edge AI Medical Device System Tested for Breast Cancer Team Meetings

Researchers present a system that runs AI models locally on edge hardware as a regulated medical device, aimed at supporting breast cancer multidisciplinary team meetings. The paper reports a feasibility evaluation, noting that existing AI-supported workflows for these meetings depend on cloud infrastructure. The work targets reducing documentation burden and time pressure in complex case reviews.

medical-ai/edge-ai/healthcare/research

papersTODAY 04:00 UTC

arXiv paper reviews barriers to trustworthy AI use in cancer genomics

A revised arXiv paper examines how AI and natural language processing are used to extract and interpret biomedical knowledge in cancer genomics. It argues that clinical adoption has lagged and lays out the barriers, risks and possible pathways needed for trustworthy translation into routine oncology. The work is a review and framing contribution rather than a report of new model results.

healthcare/nlp/clinical-translation/research

papersTODAY 04:00 UTC

Structured EHR Features Used to Predict 30-Day Readmission on MIMIC-IV

A new arXiv paper proposes knowledge-enriched structured features drawn from electronic health records to predict whether a patient will be readmitted within 30 days. The authors position the approach as an alternative to models that depend on discharge summaries and pretrained language models, which require clinical notes and carry heavy computational costs. Evaluation is conducted on the MIMIC-IV dataset.

healthcare/ehr/clinical-prediction/benchmarks

papersTODAY 04:00 UTC

PINN framework models blood flow dynamics in abdominal aortic aneurysms

Researchers built a three-dimensional physics-informed neural network to simulate pulsatile blood flow in the human aorta, focusing on abdominal aortic aneurysm haemodynamics. The approach embeds physical laws into the training process, allowing time-resolved simulation without conventional mesh-based solvers. The work is a preprint posted to arXiv.

research/physics-informed/healthcare/simulation

papersTODAY 04:00 UTC

Study Compares LLM-Generated Rules With Traditional Models for Heart Disease Prediction

A new arXiv paper evaluates rule-based systems produced by large language models against conventional machine learning classifiers for predicting heart disease. Using the UCI Heart Disease dataset, the authors benchmark models including logistic regression and k-nearest neighbors. The work examines whether LLM-derived decision rules can match or complement established clinical prediction methods.

research/healthcare/benchmark/llm

papersTODAY 04:00 UTC

AI Model Uses CRRT Pressure Waveforms to Predict Daily Mortality in AKI Patients

Researchers developed a machine learning approach that predicts day-by-day mortality risk for critically ill patients with acute kidney injury receiving continuous renal replacement therapy. Unlike existing tools that rely mainly on electronic health record data, this method incorporates minute-level machine pressure waveform signals. The work suggests that treatment device data can add predictive value beyond standard clinical parameters.

healthcare/machine-learning/mortality-prediction/research

papersTODAY 04:00 UTC

Transformer Model Detects Schizophrenia from EEG Spectrograms

A new arXiv paper presents a transformer-based framework that analyzes EEG signals converted into spectrogram form to identify schizophrenia. The approach aims to support diagnosis, which currently relies mainly on clinical evaluation, by using a non-invasive brain-activity measurement. The work is a preprint and has not yet been validated in clinical settings.

eeg/healthcare/transformers/research

papersTODAY 04:00 UTC

Frozen Physiological Encoder Keeps ICU Model Explanations Stable During Updates

A new arXiv paper proposes updating intensive care prediction models through a structurally bounded procedure that leaves the physiological encoder frozen. The authors argue this limits how much model behavior and its explanations can drift when patient data distributions change. The aim is to make adapted clinical models easier to audit after deployment.

healthcare/interpretability/model-updates/arxiv

papersTODAY 04:00 UTC

Study compares end-to-end models for clinical SOAP note generation from audio

A new arXiv paper examines how well audio-language models can turn long doctor-patient conversations into structured SOAP clinical notes. The authors compare lightweight and heavyweight end-to-end approaches, noting that while cascaded speech recognition pipelines remain strong, end-to-end models tend to lose information or produce hallucinations. The work targets the modality gap in long-form clinical audio.

clinical/speech/benchmark/healthcare

papersTODAY 04:00 UTC

arXiv Paper Proposes Adaptive Harness for Long-Horizon Clinical LLM Agents

A new arXiv preprint introduces Asclepius, a harness designed to keep LLM agents stable during long clinical tasks rather than short single-step prompts. The authors evaluate it in a Clinical Environment Simulator where an agent handles extended workflows under resource contention, arguing that current benchmarks miss this class of failures. The work targets reliability problems that appear only in hours-long deployments.

agents/healthcare/benchmarks/llm-agents

papersTODAY 04:00 UTC

Reinforcement Learning Optimizes CT Protocols via Virtual Imaging Trials

Researchers apply reinforcement learning to CT protocol tuning, where acquisition and reconstruction settings interact in ways that make brute-force testing impractical. Their framework relies on virtual imaging trials to search the parameter space and balance diagnostic image quality against radiation exposure. The work appears as a cross-listed arXiv submission in machine learning.

reinforcement-learning/medical-imaging/arxiv/healthcare

papersTODAY 04:00 UTC

ClinicalReTrial: Self-Evolving Agents for Clinical Trial Redesign

A new arXiv paper introduces ClinicalReTrial, a system that uses self-evolving AI agents to revise clinical trial protocols. The work targets the high cost and complexity of drug development, where trial designs are written as lengthy natural-language documents that are difficult to analyze manually. The authors frame agent-based redesign as an alternative to conventional manual protocol review.

agents/research/healthcare/clinical-trials

papersTODAY 04:00 UTC

SHIFT-M3 screens multimodal ECG records for cross-patient data mix-ups

A new preprint introduces SHIFT-M3, a method that uses pre-fusion alignment to check whether the waveform, text report, metadata, and predictions bundled in a clinical record actually come from the same patient. The authors note that multimodal clinical AI pipelines usually assume this consistency, yet linkage errors can silently combine individually plausible components from different patients. The approach aims to catch such mismatches before downstream fusion and prediction occur.

healthcare/multimodal/data-integrity/safety

papersTODAY 04:00 UTC

Unified Evaluation Benchmark Proposed for ECG-Based Emotion Recognition Models

A new arXiv paper argues that deep learning research on automated emotion recognition from electrocardiogram signals is hard to compare because studies differ in preprocessing, training and evaluation setups. The authors present a unified evaluation framework intended to allow fairer, more direct comparison between architectures. The work is a cross-listing on arXiv's machine learning section.

research/benchmark/healthcare/deep-learning

papersTODAY 04:00 UTC

KREL Method Uses LLM Reasoning Over Clinical Evidence for Automatic Medical Coding

A revised arXiv paper presents KREL, a technique for automatically assigning standardized ICD codes to clinical notes. The method relies on knowledge-guided reasoning over clinical evidence using large language models, aiming to support reimbursement, quality reporting and research. The submission is a replacement version of an earlier preprint.

llm/healthcare/reasoning/medical-coding

papersTODAY 04:00 UTC

Latent model encodes clinical conditions from vital signs in healthy subjects

A new arXiv preprint describes a latent-variable approach for representing clinical conditions using vital-sign data collected from healthy individuals. The authors frame the work within broader efforts to scale machine-learning signal processing in healthcare, where access to large, rich training datasets is often limited. The abstract provided is truncated, so reported methods, datasets, and results are not yet detailed.

research/healthcare/latent-models/arxiv

papersTODAY 04:00 UTC

Study Finds Clinical LLM Agents Give Inconsistent Orders Across Repeated Runs

A new arXiv paper examines how clinical LLM agents behave when given the same patient case multiple times. Although the agents often reach the same overall judgment, the tests, medications, and referrals they order can differ substantially between runs. The authors argue that evaluating these agents on a single run per task can hide this variability and misrepresent their reliability.

agents/healthcare/reliability/benchmarks

papersTODAY 04:00 UTC

SatIR: High-Recall Constraint-Satisfaction Retrieval for Clinical Trial Matching

A new arXiv paper introduces SatIR, a retrieval method aimed at settings where candidates must meet a specific profile's constraints rather than just be topically similar. Clinical trial matching is used as the motivating high-stakes case, where missing an eligible patient or trial carries real cost. The approach targets scalable, high-recall constraint satisfaction across many competing profiles.

information-retrieval/clinical-trials/arxiv/healthcare

papersSEP 10 04:00 UTC

Deep Neural Networks Decode Finger Intent from sEMG for Post-Stroke Rehabilitation

An arXiv paper explores using deep learning to interpret finger-specific movement intentions from surface electromyography (sEMG) signals in stroke survivors. Because measurable muscle activity often persists even when movement is weak or incomplete, these signals can serve as control inputs for rehabilitation hardware. The study frames the problem as five-finger multilabel intent decoding.

neurorehabilitation/emg/deep-learning/healthcare

papersSEP 10 04:00 UTC

Framework simulates patients to assess risks of conversational healthcare AI decision aids

Researchers have developed and validated a patient simulation framework designed to probe conversational healthcare AI systems for potential harms before deployment. The approach maps to the NIST AI Risk Management Framework's MAP and MEASURE functions and was demonstrated by testing an antidepressant decision-support assistant. According to the authors, it provides an empirical basis for identifying and measuring risks in clinical AI tools.

healthcare/safety/evaluation/simulation

papersSEP 10 04:00 UTC

Study Explores AI Support for Emergency Department Revisit Quality Review Screening

A new arXiv paper investigates how emergency departments screen patient return visits for quality assurance, a task often narrowed to 48-72 hour windows to boost actionable findings while limiting chart review workload. The research examines how humans make these screening decisions and how artificial intelligence could assist the process.

healthcare/human-ai-collaboration/clinical-decision-making/research

papersSEP 10 04:00 UTC

Trust-Network Federated Learning Framework Proposed for Multi-Center Aging Clock Prediction

A new preprint presents a federated learning framework that trains biological aging clock models across multiple research centers without centralizing sensitive data, relying on a trust network to coordinate participants. The work also examines which protein interactions, ranging from pairwise to higher-order, contribute most to accurate aging predictions.

federated-learning/healthcare/aging-clock/privacy

papersSEP 10 04:00 UTC

Study finds false positive bias in AI speech-based cognitive screening for UK multilinguals

New arXiv research investigates AI models that detect early signs of dementia and mild cognitive impairment from conversational speech, focusing on multilingual English speakers in the UK. The authors report that such screening tools show a false positive bias, disproportionately flagging multilingual speakers compared with monolingual ones. The findings suggest speech-based cognitive screening may disadvantage linguistically diverse populations unless corrected.

healthcare/bias/speech/uk

papersSEP 10 04:00 UTC

Deep Learning with Pseudo-Labeling Enables Contactless Heart-Rate Estimation from Video

Researchers describe a deep learning method for remote photoplethysmography (rPPG), which estimates heart rate from ordinary video without any physical contact, a capability aimed at telemedicine use. The approach relies on pseudo-labeling to lessen the need for manually annotated training data, and the authors report state-of-the-art results for contactless heart-rate estimation.

computer-vision/healthcare/pseudo-labeling/telemedicine

papersSEP 10 04:00 UTC

Cross-Attention Model Improves Cardiovascular Event Prediction from Medical Claims

A new arXiv paper describes a cross-attention approach for predicting major adverse cardiovascular events using medical claims data, which combines billing and clinical records. The method aims to improve on existing predictive models by better integrating these two types of information. The work is a preprint and has not yet been peer reviewed.

healthcare/research/attention/claims-data

papersSEP 10 04:00 UTC

Multi-label vs multi-class classification of blood cells in microfluidic channels

A new study examines how to classify blood cells and their aggregates imaged by deformability cytometry, a high-throughput imaging flow cytometry technique that measures cellular stiffness alongside properties like area and elongation. The researchers compare multi-label and multi-class machine learning approaches for identifying individual cells and clusters within microfluidic channels. The work aims to determine which labeling scheme better captures cases where multiple cell types or aggregates appear in a single measurement.

machine-learning/classification/imaging/healthcare

papersSEP 10 04:00 UTC

SAFER-Activities Dataset Introduced for Fall Detection and Routine Activity Recognition

Researchers have released SAFER-Activities, a dataset aimed at improving action recognition in smart healthcare monitoring systems, with a focus on detecting falls among people with mobility challenges. It addresses limitations of existing clip-based datasets by supporting assessment of both fall events and routine activities for timely intervention.

dataset/healthcare/fall-detection/activity-recognition