The Lancet

When to and when not to use machine learning in risk prediction models

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...

Independent Multi-Vendor Study Validates BostonGene's AI for Precision HER2 Scoring

BostonGene, the developer of the leading AI foundation model for tumor and immune biology, today announced another major independent validation of its AI and machine learning (ML) capabilities in a ...
ascopubs.org

Artificial Intelligence and Machine Learning Innovations to Improve Design and Representativeness in Oncology Clinical Trials

The integration of artificial intelligence (AI) and machine learning (ML) in oncology clinical trials is rapidly evolving alongside the broader field. For example, AI-driven adaptive trial designs may ...
Open Heart

Bridging machine learning and clinical practice: a multicentre nomogram for 90-day graft failure risk stratification in heart transplantation

Background Early graft failure within 90 postoperative days is the leading cause of mortality after heart transplantation. Existing risk scores, based on linear regression, often struggle to capture ...
Risk.net

Demand deposit balance prediction models under the interest rate risk in the banking book guidelines: an empirical analysis integrating time-series models and machine learning ...

The authors analyze the interest rate risk in the banking book regulations, arguing that financial institutions must develop robust models for forecasting ...
Devdiscourse

Artificial intelligence could change future of antimicrobial drug discovery: Here's why

Read more about Artificial intelligence could change future of antimicrobial drug discovery: Here's why on Devdiscourse ...