Write a rigorous Scoping Review on the application of Foundation Models (FMs) in chest medicine based on the PCC framework below.
Scope:
- Models: Large Language Models (LLMs) and Vision Transformers (ViTs/multimodal). Exclude legacy CNNs/Deep Learning unless used as baselines.
- Data: EHR (unstructured text) and Chest CT imaging.
PCC Framework (Inclusion Criteria):
- Population:
- Interstitial Lung Disease (ILD): Fibrosis quantification and mortality risk.
- COPD and Asthma: Phenotyping and exacerbation prediction.
- Immunosuppressed Patients: Distinguishing Pneumonitis vs. Infection (e.g., HSCT/chemo).
- Oncology Patients: Drug-Induced Pneumotoxicity (DI-ILD) from immunotherapy (ICI) or ADCs (e.g., Trastuzumab deruxtecan/Enhertu).
- Concept: Application of FMs for prognostication, outcome prediction, deep phenotyping, and risk stratification.
- Context: Clinical analysis of EHR text and CT imaging.
Required Structure:
- Title: Formal academic title.
- Abstract: Structured summary (Background, Objectives, Results, Conclusion).
- Introduction: Define FMs vs. traditional AI. Explain the rationale for mapping FM capabilities to complex respiratory tasks.
- Methodology: Simulate a search strategy focusing on model architecture and specific clinical concepts.
- Thematic Synthesis (Results):
- Theme 1: NLP/LLMs in Obstructive Disease (Phenotyping/risk from notes).
- Theme 2: Vision Transformers in ILD (Severity/progression prediction).
- Theme 3: Complex Diagnostics (Pneumonitis vs. Infection in immunosuppressed hosts).
- Theme 4: Toxicology/Oncology (AI surveillance of Enhertu/ICI toxicity).
- Discussion: Summarize evidence, highlight gaps (validation/multimodal integration), and address technical limitations (interpretability, hallucinations).
- Conclusion: Assessment of clinical readiness.