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Background: Integrating complementary diagnostic data sources promises enhanced robustness in the predictive performance of artificial intelligence (AI) models, a crucial requirement for future clinical validation/implementation. In this study, we investigate the potential value of integrating data from noninvasive diagnostic modalities, including chest computed tomography (CT) imaging, routine laboratory blood tests, and clinical parameters, to retrospectively predict 1-year survival in a cohort of patients with advanced non-small-cell lung cancer, melanoma, and urothelial cancer treated with immunotherapy., Patients and Methods: The study included 475 patients, of whom 444 had longitudinal CT scans and 475 had longitudinal laboratory data. An ensemble of AI models was trained on data from each diagnostic modality, and subsequently, a model-agnostic integration approach was adopted for combining the prediction probabilities of each modality and producing an integrated decision., Results: Integrating different diagnostic data demonstrated a modest increase in predictive performance. The highest area under the curve (AUC) was achieved by CT and laboratory data integration (AUC of 0.83, 95% confidence interval 0.81-0.85, P < 0.001), whereas the performance of individual models trained on laboratory and CT data independently yielded AUCs of 0.81 and 0.73, respectively., Conclusions: In our retrospective cohort, integrating different noninvasive data modalities improved performance., (© 2024 The Author(s).)

Antimicrobial resistance remains a significant global public health threat. Although development of novel antibiotics can be challenging, several new antibiotics with improved activity against multidrug-resistant Gram-negative organisms have recently been commercialised. Expanding access to these antibiotics is a global public health priority that should be coupled with improving access to quality diagnostics, health care with adequately trained professionals, and functional antimicrobial stewardship programmes. This comprehensive approach is essential to ensure responsible use of these new antibiotics., Competing Interests: Declaration of interests We declare no competing interests., (Copyright © 2024 World Health Organization. Published by Elsevier Ltd. All rights reserved. This is an Open Access article published under the CC BY NC ND 3.0 IGO license which permits users to download and share the article for non-commercial purposes, so long as the article is reproduced in the whole without changes, and provided the original source is properly cited. This article shall not be used or reproduced in association with the promotion of commercial products, services or any entity. There should be no suggestion that WHO endorses any specific organisation, products or services. The use of the WHO logo is not permitted. This notice should be preserved along with the article's original URL.)