Artificial intelligence in NSCLC management for revolutionizing diagnosis, prognosis, and treatment optimization: A systematic review
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View abstract on PubMed
Summary
This summary is machine-generated.Artificial intelligence (AI), artificial neural networks (ANNs), and machine learning (ML) show promise in improving non-small cell lung cancer (NSCLC) diagnosis and survival prediction. However, their impact on patient-reported outcomes and cost-effectiveness requires further investigation.
Area Of Science
- Oncology
- Medical Informatics
- Artificial Intelligence
Background
- Non-small cell lung cancer (NSCLC) is a leading cause of cancer mortality.
- Artificial intelligence (AI), artificial neural networks (ANNs), and machine learning (ML) offer potential advancements in NSCLC management.
- The impact of AI on patient-reported outcome measures (PROM), overall survival (OS), and cost-effectiveness in NSCLC is not well understood.
Purpose Of The Study
- To systematically review the impact of AI, ANNs, and ML on PROM, OS, and cost-effectiveness in adult NSCLC patients.
- To compare AI-driven approaches with conventional methodologies and standard-of-care in NSCLC research.
Main Methods
- Systematic review following PRISMA guidelines.
- Data synthesis using the Synthesis Without Meta-analysis (SWiM) approach.
- Focus on study design, patient characteristics, AI methodology, and outcomes; meta-analysis deemed inappropriate due to heterogeneity.
Main Results
- Ten studies were included, showing AI models improve diagnostic precision, treatment optimization, and survival prediction.
- AI-enhanced approaches outperformed conventional models in prognosis and resource allocation.
- Challenges include data heterogeneity, model generalizability, and algorithmic transparency.
Conclusions
- Evidence suggests AI models are associated with prognostic stratification for OS in NSCLC.
- No evaluable evidence exists for AI's impact on PROM or cost-effectiveness in NSCLC.
- Future research should integrate validated PROM and economic evaluations, addressing AI's clinical applicability, biases, and ethical concerns.
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