Part 10/11:
Reducing Dependence on Large Labeled Datasets: Techniques like transfer learning, self-supervised learning, and semi-supervised learning are gaining traction to train effective models with less labeled data.
Enhanced Interpretability: Research into explainable AI aims to make model outputs more transparent, increasing clinician confidence.
Integration with Clinical Workflow: Future systems will seamlessly integrate into hospital PACS (Picture Archiving and Communication Systems) and EHR (Electronic Health Records), offering real-time support during patient care.
Regulatory and Ethical Frameworks: As AI in medical imaging advances, establishing standards for validation, validation, and deployment will be essential to ensure safety and efficacy.