{
  "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.",
  "authors": [
    {
      "affiliations": [
        "School of Population Health, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia"
      ],
      "name": "Padmanesan Narasimhan"
    },
    {
      "affiliations": [
        "Bond University, Gold Coast (Robina), Queensland, Australia"
      ],
      "name": "Usman Iqbal"
    },
    {
      "affiliations": [
        "Graduate Institute of Biomedical Informatics, College of Medical Science & Technology, Taipei Medical University, Taipei, Taiwan"
      ],
      "name": "Yu-Chuan Li"
    }
  ],
  "title": "Artificial intelligence in clinical risk prediction: promise, performance and the path forward?",
  "uid": "18a673fa-ca89-5249-bf7c-deaaa62c9a32"
}