Part 9/11:

• Interpretability: AI models, especially deep neural networks, are often black boxes. Developing explanation methods to understand model decisions is vital for clinical trust and adoption.

• Evaluation Metrics: Metrics like Dice Score and IoU are useful, but clinical relevance and specific thresholds vary by application, necessitating careful validation.

The Broader Impact and Future Directions

The discourse emphasized that AI is not here to replace clinicians but to augment their capabilities, reduce workload, and improve diagnostic accuracy. The analogy drawn with engineers—fear of job loss in the face of automation—parallels the healthcare perspective: AI tools are meant to assist, not displace.

Presenters also touched upon emerging trends: