This manuscript provides a comprehensive overview of the application of artificial intelligence (AI) in lung pathology, particularly in the diagnosis of lung cancer. It discusses various AI models designed to support pathologists and clinicians. AI models supporting pathologists are to standardize diagnosis, score PD-L1 status, supporting tumor cellularity count, and indicating explainability for pathologic judgements. Several models predict outcomes beyond pathologic diagnosis and predict clinical outcomes like patients' survival and molecular alterations. The manuscript emphasizes the potential of AI to enhance accuracy and efficiency in pathology, while also addressing the challenges and future directions for integrating AI into clinical practice.
Keyphrases
- artificial intelligence
- machine learning
- big data
- deep learning
- end stage renal disease
- clinical practice
- neoadjuvant chemotherapy
- ejection fraction
- newly diagnosed
- chronic kidney disease
- peritoneal dialysis
- type diabetes
- prognostic factors
- metabolic syndrome
- palliative care
- radiation therapy
- current status
- peripheral blood
- insulin resistance
- climate change
- patient reported
- glycemic control
- rectal cancer
- free survival