{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T05:57:18Z","timestamp":1790575038982,"version":"4.1.0"},"reference-count":88,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T00:00:00Z","timestamp":1790553600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"<jats:p>Pneumonia remains a major global public health challenge, particularly among children, older adults, and individuals with weakened immune systems. Although deep learning has made great progress in automated pneumonia diagnosis, most current methods are unimodal, using only chest X-ray images. This results in limited robustness, cross-institutional generalization and clinical applicability. Moreover, the current reviews mainly focus on summarising single data modalities or model architectures and lack comparative analysis of multimodal fusion strategies, explainability methods, benchmark datasets, and new artificial intelligence paradigms. To fill these gaps, this systematic review offers a common analytical framework for systematically comparing multimodal deep learning architectures, fusion strategies, explainability methods, benchmark datasets, and emerging technologies in the field of artificial intelligence for pneumonia diagnosis. Research articles were systematically reviewed to identify the latest developments, technological challenges, and future research directions. Evidence suggests that multimodal models can provide information from different sources and may improve diagnostic performance in specific settings. However, direct comparisons between studies are limited by differences in datasets, clinical tasks, and validation protocols. Attention-based and hybrid fusion strategies can learn useful information from various modalities. Explainable artificial intelligence methods provide mechanisms for improving the transparency and interpretability of model predictions.The clinical translation is still limited, however, due to the lack of large-scale multimodal datasets, data heterogeneity, missing modalities, inconsistent evaluation protocols, and lack of external validation. In summary, this review summarizes the current research trends, outlines important technological challenges, and provides a research agenda for the future to build strong, interpretable, and clinically viable artificial intelligence systems for next-generation pneumonia diagnosis.<\/jats:p>","DOI":"10.3389\/fdgth.2026.1949796","type":"journal-article","created":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T05:45:09Z","timestamp":1790574309000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence for pneumonia diagnosis: a systematic review of multimodal learning, fusion strategies, and explainability"],"prefix":"10.3389","volume":"8","author":[{"given":"K. 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