ICD-10
PulseAugur coverage of ICD-10 — every cluster mentioning ICD-10 across labs, papers, and developer communities, ranked by signal.
3 day(s) with sentiment data
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New benchmark CardioVascBench evaluates prioritized diagnosis generation
Researchers have introduced CardioVascBench, a new benchmark designed to evaluate the performance of models in generating prioritized diagnoses from real-world Chinese cardiovascular patient records. This benchmark incl…
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New benchmark MedicalBench evaluates LLMs on implicit medical concept extraction
Researchers have introduced MedicalBench, a new benchmark designed to evaluate the ability of large language models (LLMs) to extract medical concepts from electronic health records, particularly focusing on implicitly …
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VietMed dataset released for Vietnamese medical ASR
Researchers have introduced VietMed, a substantial new dataset and benchmark for Vietnamese medical Automatic Speech Recognition (ASR). This dataset is reportedly the largest of its kind globally, featuring 16 hours of …
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New AI model LAMAE integrates multimodal medical data for improved patient representation
Researchers have developed a new multimodal AI model called Latent-Attention Masked Autoencoder (LAMAE) designed to learn patient-level representations from diverse medical data. Unlike previous models that often proces…
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AI system automates clinical documentation queries to improve accuracy
Researchers have developed an automated system, termed DAU (Draft, Ask, Update), to address gaps in clinical documentation by generating clarifying queries for physicians. An audit of 3,000 clinical visits revealed the …
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New CASE framework enhances AI's longitudinal medical reasoning capabilities
Researchers have developed a new framework called CASE, which stands for Clinical Agents for Seeking Evidence, designed to improve longitudinal medical reasoning in foundation models. This framework includes a tool-use …
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New AI framework CASE enhances medical reasoning with autonomous evidence-seeking
Researchers have developed a new framework called CASE (Clinical Agents for Seeking Evidence) to improve AI's ability to reason with longitudinal medical data. This framework includes a multimodal benchmark built on UK …
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New framework LearnActCoder improves clinical coding accuracy by learning from errors
Researchers have developed LearnActCoder, a framework designed to improve the accuracy of clinical coding agents by learning from past errors. This system creates a structured Mistake Knowledge Database (MistakeKDB) tha…
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New CAST framework enhances audibility of clinical AI models
Researchers have developed a new framework called CAST (Concept-guided Artifact Suppression Tuning) to make clinical language models more auditable and robust. This method uses Sparse Autoencoders to identify and suppre…
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New MTDiag dataset evaluates LLMs in multi-turn clinical diagnosis
Researchers have developed MTDiag, a new multi-turn diagnostic dialogue dataset designed to better evaluate Large Language Models (LLMs) in clinical settings. Unlike static benchmarks, MTDiag simulates the interactive a…
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UK develops national EHR generative AI model for pandemic prediction
A new research paper details the development of Foresight-England, a large-scale generative AI model designed to predict medical events using electronic health records (EHRs) specifically within the context of the COVID…
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LLMs struggle with healthcare schema compliance, but validation-repair framework improves accuracy
A new study published on arXiv evaluates the schema compliance of three open-source large language models—Qwen2.5 7B, Llama 3.1 8B, and Gemma2 9B—in healthcare interoperability scenarios. The research found consistent s…
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New benchmark simulates physician-patient dialogues for medical AI evaluation
Researchers have introduced Doctorina MedBench-ICD10, a novel evaluation framework for AI systems designed for medical applications. This benchmark simulates realistic physician-patient dialogues, moving beyond traditio…
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AI pipeline for healthcare claims denial management detailed
This post details the engineering process for building an AI-powered denial management pipeline for healthcare claims. It outlines three key ML systems: a pre-submission scoring service, a remittance classifier, and a d…
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TEDDY foundation model predicts pediatric disease risk with high accuracy
Researchers have developed TEDDY, a novel foundation model designed to predict the risk of various diseases in children using historical diagnostic data. Trained on millions of ICD-10 diagnoses from over a million child…
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Health system semantic search feasible with Qwen3 embeddings
Researchers have developed a semantic search system capable of indexing and querying 166 million clinical notes from a large children's hospital, demonstrating the feasibility of large-scale clinical data retrieval. The…
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AI Reshapes Healthcare Jobs, Posing Risks and Creating New Roles
Artificial intelligence is rapidly transforming the healthcare sector, with AI tools now assisting in areas like clinical decision support, documentation, and administrative tasks. While some roles, particularly those i…
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AI framework integrates patient data with FDA reports for drug safety
Researchers have developed a novel multi-agent AI framework that integrates patient-generated data from platforms like Reddit and WebMD with official FDA adverse event reports for antidepressants. This system uses a kno…
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Clinical AI pipelines propagate transcription errors into SOAP notes
Clinical AI pipelines that transcribe audio and generate SOAP notes are prone to error propagation, where mistakes in early stages are amplified downstream. If a speech-to-text model mishears a drug name, the subsequent…
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New MedSynth Dataset Boosts AI for Medical Documentation
Researchers have introduced MedSynth, a novel dataset of synthetic medical dialogues and notes designed to improve AI models for medical documentation. The dataset contains over 10,000 dialogue-note pairs, covering more…