Artificial intelligence in respiratory medicine: From diagnosis to treatment and future directions
Zhe Chen, Zhiyong Tang, Rob M. Ewing, Zehor Belkhatir, Yihua Wang · 2026
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Description
Zhe Chen and co-authors survey how artificial intelligence is changing lung care, from spotting disease to guiding treatment. Their 2026 review spans lung cancer, chronic obstructive pulmonary disease, asthma, interstitial lung disease, and cystic fibrosis, illnesses that still cause heavy sickness and death worldwide. It follows the field from early machine learning to large language models and multimodal systems that join scans, lab data, and clinical notes.
The review weighs AI at each step of care: nodule detection at radiologist level, automated diagnosis, decision support, surgery and radiotherapy planning, prognosis, and digital twin models that track patients over time. It also covers biomarker discovery from multi-omics data and liquid biopsies, plus drug repurposing. The authors close with the barriers that remain, including data control, legal liability, and the need for prospective trials, and sketch a roadmap for bringing generalist medical AI safely into clinics.
Catalog record
| CREATOR | Zhe Chen, Zhiyong Tang, Rob M. Ewing, Zehor Belkhatir, Yihua Wang |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 127 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |