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Artificial intelligence in clinical risk prediction: promise, performance and the path forward?

Year: 2025

Type: article

Abstract: Artificial intelligence (AI) and machine learning are reshaping clinical risk prediction and patient monitoring.1 Two studies show this transformation, highlighting both promise and challenges. Yoshihara et al investigate deep learning for hypertension detection from pharyngeal images in Japanese primary care settings,2 while Watson et al assess transformer-based models for predicting patient deterioration in emergency admissions, comparing them to the widely used National Early Warning Score (NEWS).3 Together, these studies show AI’s expanding diagnostic capabilities, document improvements over traditional methods and reveal hurdles for widespread clinical adoption.

Source: BMJ Health & Care Informatics

DOI: 10.1136/bmjhci-2025-101707

Authors: Padmanesan Narasimhan, Usman Iqbal, Yu-Chuan Li

Institutions: UNSW Sydney, Bond University, Taipei Medical University

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,207