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Peer-reviewedCC BY 4.0Respirology (Carlton, Vic.)

From Pattern Recognition to Decision Guidance: Artificial Intelligence in Interstitial Lung Disease Diagnosis and Surveillance

  • Deliang Yang1,2,3
  • Xue Li1,2,3
  • David Chi Leung Lam1
  1. 1Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine The University of Hong Kong Hong Kong SAR China
  2. 2The University of Hong Kong–Shenzhen Hospital China
  3. 3Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine The University of Hong Kong Hong Kong SAR China
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Abstract

Key Points: Current ILD guidelines centre diagnosis on clinical context, exposure and autoimmune assessment, high‐resolution CT, pulmonary function testing, and multidisciplinary discussion, while explicit endorsement of artificial intelligence remains limited.

Some artificial intelligence tools have already entered ILD‐related clinical workflows by flagging interstitial abnormalities on routine CT, supporting non‐invasive assessment of suspected fibrotic ILD, and quantifying fibrotic burden and interval progression on serial imaging.

Research‐stage AI models are beginning to address unresolved gaps in clinical guidelines by reducing diagnostic uncertainty in borderline cases, enabling subgroup‐specific risk stratification in connective tissue disease‐associated ILD, and forecasting progression or follow‐up needs from longitudinal multimodal data.

The remaining need is to develop a validated personalised multimodal decision‐support system that can integrate imaging, clinical, serological, pathological, and longitudinal data to guide biopsy, surveillance, and management decisions for uncertain ILD cases.

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Cite

BibTeXRIS
BibTeX
@article{yang2026from,
  title = {{From Pattern Recognition to Decision Guidance: Artificial Intelligence in Interstitial Lung Disease Diagnosis and Surveillance}},
  author = {Deliang Yang and Xue Li and David Chi Leung Lam},
  journal = {Respirology (Carlton, Vic.)},
  year = {2026},
  month = aug,
  doi = {10.1002/resp.70298},
  url = {https://doi.org/10.1002/resp.70298},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - From Pattern Recognition to Decision Guidance: Artificial Intelligence in Interstitial Lung Disease Diagnosis and Surveillance
AU  - Deliang Yang
AU  - Xue Li
AU  - David Chi Leung Lam
PY  - 2026
DA  - 2026/08/12
JO  - Respirology (Carlton, Vic.)
DO  - 10.1002/resp.70298
UR  - https://doi.org/10.1002/resp.70298
AB  - Key Points: Current ILD guidelines centre diagnosis on clinical context, exposure and autoimmune assessment, high‐resolution CT, pulmonary function testing, and multidisciplinary discussion, while explicit endorsement of artificial intelligence remains limited.  Some artificial intelligence tools have already entered ILD‐related clinical workflows by flagging interstitial abnormalities on routine CT, supporting non‐invasive assessment of suspected fibrotic ILD, and quantifying fibrotic burden and interval progression on serial imaging.  Research‐stage AI models are beginning to address unresolved gaps in clinical guidelines by reducing diagnostic uncertainty in borderline cases, enabling subgroup‐specific risk stratification in connective tissue disease‐associated ILD, and forecasting progression or follow‐up needs from longitudinal multimodal data.  The remaining need is to develop a validated personalised multimodal decision‐support system that can integrate imaging, clinical, serological, pathological, and longitudinal data to guide biopsy, surveillance, and management decisions for uncertain ILD cases.
SN  - 1323-7799
SN  - 1440-1843
ER  -