Mount Sinai experts will present new research on AI and imaging for lung nodules, sleep apnea, and cardiovascular risk at the ATS 2026 Conference.
Key Details
- 1Mount Sinai researchers are presenting at ATS 2026 International Conference (May 15–20, Orlando).
- 2A deep learning AI model (RADLogics Malignancy Index) was externally validated for malignancy prediction in indeterminate lung nodules using sequential CT-based risk scoring.
- 3Research explores temporal stability and performance drift of deployed AI clinical deterioration prediction models (MEWS++) in hospital workflows.
- 4Machine learning and natural language processing techniques are being applied to sleep apnea and cardiovascular outcomes, including data extraction from EHRs and risk prediction.
- 5Investigators also report on disparities in lung nodule follow-up among never-smokers and propose ML-driven risk stratification models for obstructive sleep apnea.
- 6Emphasis on improving generalizability and real-world translation of AI/ML models in clinical and imaging settings.
Why It Matters
These studies illustrate the rapid expansion and real-world implementation of AI/ML in imaging (especially CT for lung nodules) and related clinical decision support. Their findings have direct implications for enhancing diagnosis, risk stratification, and follow-up in respiratory and cardiovascular disease, crucial for radiologists and the imaging AI community.
Source
EurekAlert
Related News
•EurekAlert
AI Model Accelerates Accurate Dose Prediction for CyberKnife Radiotherapy
A physics- and spatially informed diffusion model accelerates Monte Carlo–level dose estimation for CyberKnife radiotherapy using planning CT and anatomical data.
•EurekAlert
New G-AUDIT Tool Detects Hidden Bias in Medical AI Training Data
Johns Hopkins and FDA researchers unveil G-AUDIT, a tool to identify hidden biases in medical AI datasets before model training.
•EurekAlert
AI-Driven Bone Marrow Mapping Tool Improves Blood Cancer Assessment
Weill Cornell Medicine develops an AI tool to score and track disease severity in blood cancer from bone marrow imaging.