In this episode, we explore the intersection of model compression and interpretability in medical AI with the authors of the newly published research paper, Interpretability-Aware Pruning for Efficient Medical Image Analysis. Join us as Vinay Kumar Sankarapu, Pratinav Seth and Nikita Malik from AryaXAI discuss how their framework enables deep learning models to be pruned using attribution-based methods—retaining critical decision-making features while drastically reducing model complexity.

We cover:

• Why traditional pruning fails to account for interpretability
• How techniques like DL-Backtrace (DLB), Layer-wise Relevance Propagation (LRP), and Integrated Gradients (IG) inform neuron importance
• Results from applying this method to VGG19, ResNet50, and ViT-B/16 across datasets such as MURA, KVASIR, CPN, and Fetal Planes
• Practical implications for healthcare AI deployment, edge inference, and clinical trustworthiness

Whether you're a machine learning researcher, AI engineer in medtech, or working on explainable AI (XAI) for regulated environments, this conversation unpacks how to build models that are both efficient and interpretable—ready for the real world.

📄 Read the full paper: https://arxiv.org/abs/2507.08330

The AI Broadcast by Arya.ai is a bi-monthly event series that delves into the latest advancements, trends, and challenges in artificial intelligence. Each episode features in-depth discussions on groundbreaking AI topics such as Machine Learning, Generative AI, Responsible AI, and more. We’re excited to launch a brand-new theme: The Future of Agentic Stack in which we’ll explore how intelligent, autonomous AI agents—capable of making decisions, collaborating, and adapting in real time—are evolving and reshaping industries.

In our latest session, Vinay Kumar, Founder and CEO of Arya.ai, shared his thoughts on the transformative potential of Agentic AI and how it is shaping industries. The session explored the evolution of AI, key challenges, and how businesses can leverage agentic AI to stay competitive.

AI is becoming deep-rooted across processes and industries and holds immense promise. As the technologies advance, navigating the complexities of IP in AI becomes increasingly important. This need for IP is driven by the desire to protect innovation, create competitive advantages, monetize assets, attract investment, ensure legal protection, and contribute to the overall growth and success of AI-driven companies.

Moreover, Governments and organizations worldwide are recognizing the need for comprehensive regulations to ensure AI's responsible development and deployment.

Tune in to learn:

• ‘IP’ challenges for Gen ‘AI’ applications
• What is IP?
• Need for human guidance and oversight in developing and using AI technology
• The changing AI regulatory landscape