healthcare AI

72 items

ARTICLEDEV.to AI·6h ago

How accurate are AI transcripts for technical or medical terms?

This article discusses the critical issue of AI transcription inaccuracy when dealing with technical and domain-specific terminology, using a medical error example where a transcription mistake led to a dangerous medication mix-up. It highlights how such errors, not limited to healthcare, can turn useful AI tools into liabilities, and explains why specialized terms are challenging for speech-to-text models.

accuracy errors AI transcription Speech-to-Text

ARTICLEDEV.to AI·1d ago

Why Canada Is the Best Place to Build Healthcare AI Right Now

The author argues that Canada, specifically Ontario, is currently the best place to build healthcare AI companies, challenging the perception that the US is more innovation-friendly. He highlights his company's experience with Canadian regulations and the complex US regulatory landscape as justification.

regulation Innovation Canada startups

RESEARCHarXiv CS.LG·19h ago

MedicalRec: Medical recommender system for image classification without retraining

This study introduces MedicalRec, a medical recommender system for image classification, designed to optimize model selection without the need for extensive retraining. It addresses the computational and energy challenges of model identification by leveraging a publicly available dataset, MedicalRec-Bench, compiled from 3,000 articles and over 5,000 tested model records.

recommender systems deep learning machine learning healthcare AI

RESEARCHarXiv CS.AI·19h ago

Reconstructing and forecasting disease trajectories of patients with Alzheimer's disease using routine data in resource-constrained settings

This research aims to reconstruct and forecast Alzheimer's disease trajectories using routine data in resource-constrained settings. It proposes a unified framework for bidirectional prediction of cognitive scores from irregular visits, enabling interpolation and extrapolation, and providing calibrated uncertainty estimates.

forecasting disease progression Alzheimer's disease predictive modeling

RESEARCHDEV.to AI·2d ago

Development and Validation of Deep Learning Algorithms for Detection of CriticalFindings in Head CT Scans

This content discusses the development and validation of deep learning algorithms designed to detect critical findings in head CT scans. The research explores the application of artificial intelligence to improve detection in medical diagnostics.

deep learning diagnostic imaging CT scans healthcare AI

RESEARCHarXiv CS.CL·4d ago

Improving Heart-Focused Medical Question Answering in LLMs via Variance-Aware Rubric Rewards with GRPO

This research investigates optimizing Large Language Models (LLMs) for heart-focused medical question answering using Group Relative Policy Optimization (GRPO) for post-training. A Variance-Aware Reward Framework is proposed to enhance rubric-based supervision with continuous analytical reward functions.

LLMs Medical Question Answering GRPO healthcare AI

RESEARCHDEV.to AI·4/8/2026

QIS Protocol vs Federated Learning: A Distributed Health Data Routing Alternative

O texto apresenta o QIS Protocol como uma alternativa ao Federated Learning para o roteamento de dados de saúde distribuídos, superando suas limitações como vazamento de gradientes e dependência de um agregador central. O QIS oferece privacidade por arquitetura, roteando resultados em vez de parâmetros de modelo com custo logarítmico para aplicações clínicas e de saúde populacional.

privacy federated learning QIS Protocol healthcare AI

ARTICLEDEV.to AI·4/12/2026

EU AI Act High-Risk Healthcare AI: Why Centralized Architectures Have a Structural Compliance Problem

This content addresses a structural compliance problem for centralized AI architectures in healthcare, classified as high-risk by the EU AI Act. It highlights the difficulty of these architectures in meeting requirements such as explainability and continuous risk monitoring, posing a significant challenge for systems with an August 2024 implementation deadline.

AI architecture High-Risk AI EU AI Act compliance

ARTICLEDEV.to AI·4/12/2026

QIS (Quadratic Intelligence Swarm) vs HPE Swarm Learning: Why Routing Outcomes Beats Routing Gradients

This content addresses the challenge of AI in healthcare collaboration between hospitals due to data restrictions, comparing two federation architectures: HPE Swarm Learning and QIS. The core distinction lies in distributing model training versus validated findings, with clear implications for use cases.

data privacy Distributed AI federated learning HPE Swarm Learning

DOCDEV.to AI·4/20/2026

ChatGPT Prompts for Chiropractors: Patient Communication, Documentation, and Practice Growth

This content offers practical ChatGPT prompts designed to assist chiropractors with patient communication, documentation, and practice growth. It demonstrates how AI can streamline tasks like synthesizing patient intake forms into structured clinical summaries, reducing administrative overhead.

AI applications prompt engineering productivity healthcare AI

ARTICLEDEV.to AI·4/22/2026

How AI Receptionists Work: A Technical Deep Dive into Dental Practice Phone Automation

This article provides a technical deep dive into how AI receptionists function in dental practices, detailing the call flow, challenges in speech-to-text accuracy, and the role of LLMs in processing transcripts for intent, entities, and sentiment.

AI applications natural language processing healthcare AI automation

ARTICLEDEV.to AI·4/11/2026

Why Chunking Is the Biggest Mistake in RAG Systems

Este artigo critica a técnica de 'chunking' em sistemas RAG, destacando seus problemas de perda de contexto e erros em documentos estruturados, como registros clínicos. Propõe a indexação ciente da estrutura e a sumarização como métodos mais eficazes para lidar com dados complexos.

chunking LLMs RAG Document Intelligence

CASEDEV.to AI·4/21/2026

Product Case Study- III Incomplete requirements aren’t the exception—they’re the baseline.

A healthcare AI product (mammography annotation tool) faced initial adoption failure despite being technically correct, because it didn't align with radiologists' ingrained workflows and expected interaction patterns. This highlights that requirements must be validated against real usage, and workflow mapping, early prototyping, and treating adoption as a product metric are crucial for success.

product development Workflow user experience healthcare AI

RESEARCHarXiv CS.LG·4/22/2026

Handling and Interpreting Missing Modalities in Patient Clinical Trajectories via Autoregressive Sequence Modeling

This work addresses the challenge of missing modalities in multimodal clinical data for diagnosis by reframing it as an autoregressive sequence modeling task. It leverages causal decoders from LLMs and a missingness-aware contrastive pre-training to outperform baselines on benchmarks like MIMIC-IV and eICU.

multimodal AI machine learning large language models healthcare AI

RESEARCHarXiv CS.AI·8d ago

EHRBench: An Automated and Reliable EHR-based Benchmark for Clinical Decision Making with LLMs

The paper introduces EHRBench, an automated and reliable EHR-grounded benchmark for evaluating LLM-based clinical decision-making, addressing the insufficient understanding of LLMs' reliability in real-world clinical tasks. Its goal is to ensure both scale and quality in the evaluation of Clinical Decision Making (CDM) models.

LLMs clinical decision support Benchmarking healthcare AI

RESEARCHDEV.to AI·4/10/2026

Deep Reinforcement Learning for Sepsis Treatment

Este conteúdo aborda a aplicação de Aprendizado por Reforço Profundo para o tratamento de sepse, uma condição médica grave. Ele explora como técnicas avançadas de IA podem otimizar decisões terapêuticas em ambientes clínicos complexos.

Medical Treatment deep learning reinforcement learning Sepsis

ARTICLEDEV.to AI·4/12/2026

QIS Protocol vs Matchmaker Exchange: Two Architectures for Cross-Institutional Rare Disease Intelligence

The text discusses the architectural challenge in rare disease intelligence, where crucial patient data remains globally isolated, hindering effective diagnoses and treatments. It describes the Matchmaker Exchange (MME) for finding similar phenotypes but highlights its limitation in guiding treatment responses, pointing to the need for a more comprehensive cross-institutional intelligence system.

Rare Disease data sharing Genomics interoperability

DOCDEV.to AI·6d ago

Building HealthcareAI with Safe MCP Tooling

This content outlines an architecture for securely deploying AI agents in healthcare by implementing a Permissioned MCP (Managed Capability Platform) tool. This MCP layer acts as a critical control boundary, mediating all AI access to internal systems and validating interactions based on strict security criteria.

AI architecture data privacy security access control

RESEARCHarXiv CS.LG·22d ago

MuteBench: Modality Unavailability Tolerance Evaluation for Incomplete Multimodal Fusion

MuteBench is a new benchmark for evaluating multimodal fusion architectures in clinical AI systems, specifically addressing sensor failures leading to missing data. It covers diverse datasets, fusion architectures, and two missing-data modes, revealing architecture family as the strongest predictor of robustness.

multimodal AI Data Robustness Benchmarking Sensor Failure

CASEDEV.to AI·5/9/2026

Real-World Applications of Machine Learning

Machine learning is now integral to daily business operations, improving performance and automating tasks across various industries. It provides practical solutions for real-world problems, significantly impacting healthcare by enabling early disease detection and enhancing diagnostic accuracy.

real-world applications machine learning Banking AI healthcare AI