Medical Diagnosis

6 items

RESEARCHarXiv CS.LG·5d ago

Early Detection of Alzheimer's Disease Using Explainable Machine Learning on Clinical Biomarkers: A Multi-Class Classification Study Using the Alzheimer's Disease Neuroimaging Initiative (ADNI) Dataset

An XGBoost classifier was developed using clinical features from the ADNI dataset for multi-class detection of normal cognition, mild cognitive impairment, and Alzheimer's disease. The model achieved a high mean macro AUC of 0.983 and an accuracy of 0.944, with SHAP values providing feature explainability.

machine learning Alzheimer's disease Explainable AI XGBoost

RESEARCHarXiv CS.LG·15d ago

MedExpMem: Adapting Experience Memory for Differential Diagnosis

This paper introduces MedExpMem, an experience memory framework designed to enhance medical vision-language models (VLMs) with differential diagnosis expertise. It allows diagnostic agents to learn from their own failures by memorizing discriminative experiences as pairwise differential notes.

AI in medicine learning VLM machine learning

RESEARCHarXiv CS.LG·6d ago

Cross-Modal Contrastive Learning of ECG and Angiography Representations for Severe Stenosis Classification

This research introduces StenCE, a pretraining framework that enables patient stratification based on features derived from ECGs to support early diagnosis of severe coronary artery stenosis. It aims to overcome the limitations of current diagnostic methods by integrating ECG and angiography representations.

cardiovascular disease learning Medical Diagnosis ECG

RESEARCHarXiv CS.LG·4/24/2026

Validating a Deep Learning Algorithm to Identify Patients with Glaucoma using Systemic Electronic Health Records

This research validates a deep learning algorithm for glaucoma risk assessment using systemic electronic health records. The model, fine-tuned on Stanford patient data, achieved an AUROC of 0.883 and PPV of 0.657, showing strong potential for scalable and accessible pre-screening.

deep learning Medical Diagnosis healthcare AI EHR

RESEARCHarXiv CS.LG·21d ago

Forecasting Medium-Horizon Alzheimer's Disease Progression: Residual Gap-Aware Transformers for 24-Month CDR-SB Change from ADNI Clinical and Biomarker Histories

This paper introduces a residual gap-aware transformer for forecasting 24-month Alzheimer's disease progression using ADNI clinical and biomarker histories. The research analyzes changes in CDR-SB scores, anchoring samples at mild cognitive impairment visits.

Biomarkers machine learning Alzheimer's disease Medical Diagnosis

ARTICLEDEV.to AI·4/15/2026

Your AI Diagnostician: Trained on Art, Diagnosing Disease in 2026? — What You Need to Know in 2026

While the title proposes AI trained on art for disease diagnosis by 2026, the provided content broadly discusses technology's rapid evolution and the need to stay informed. It highlights that understanding these ongoing transformations is crucial for staying ahead in any industry.

Technology Trends future-of-AI AI Medical Diagnosis