This review demonstrates advanced generative AI techniques for improving diagnostic outcomes in oncology, highlighting significant implementation challenges.
Key Points
The aim is to analyze how conditional generative AI can enhance oncology diagnostics through integrated data analysis and decision-making.
Conducted a systematic review of conditional generative AI models applicable to oncology diagnostics.
Evaluated the potential for robust data handling and clinical reporting in CDSS.
Identified gaps between experimental AI models and clinical application, focusing on challenges like bias and hallucinations.
Generative AI models show promise in improving data imputation and clinical reporting accuracy.
Highlighted the need for human validation to manage uncertainties in AI-generated diagnostics.
Provided solutions to address issues like model hallucinations and demographic bias in clinical applications.