Forbes

Balancing Accuracy And Ethics In AI-Driven Insurance Risk Assessment

The insurance industry is at a pivotal moment, grappling with the transformative potential of artificial intelligence while navigating the ethical challenges it presents. As insurers strive to enhance ...
ascopubs.org

Ensuring Reliability of Curated Electronic Health Record–Derived Data: The Validation of Accuracy for Large Language Model–/Machine Learning–Extracted Information and ...

Early-Stage Breast Cancer in Women Younger Than 50 Years: Comparing American Joint Committee on Cancer Anatomic and Prognostic Stages With Partitioning Around Medoids Clusters in SEER Data Large ...
ascopubs.org

Comparative Analysis of Generative Pre-Trained Transformer Models in Oncogene-Driven Non–Small Cell Lung Cancer: Introducing the Generative Artificial Intelligence ...

Real-World and Clinical Trial Validation of a Deep Learning Radiomic Biomarker for PD-(L)1 Immune Checkpoint Inhibitor Response in Advanced Non–Small Cell Lung Cancer The authors present a score that ...
Forbes

Why Your 'Accurate' AI Model Might Still Be Dangerously Wrong: The Hidden Importance Of Model Calibration

Every week, I see another headline about an AI model achieving “96% accuracy” in healthcare or finance. The press releases are exciting, the demos look impressive and stakeholders are eager to deploy.