AI and Medicine: Advancing Healthcare Through Innovation

Wednesday’s focus was on how AI is revolutionizing healthcare. The day began with Dr. Omar Aljawfi’s lecture on “AI Applications in Medicine and Healthcare.” Dr. Aljawfi discussed real-world breakthroughs such as AI-assisted radiology tools that identify abnormalities more quickly and accurately than traditional methods, as well as predictive algorithms that support early detection of diseases. He emphasized how these technologies are designed to complement, rather than replace medical professionals, helping to save lives, improve patient outcomes, and reduce diagnostic errors. Students were encouraged to reflect on the ethical responsibilities and opportunities that come with integrating AI into clinical practice.

The morning continued with Lab 5: Predictive Analytics in Health, where interns worked hands-on to build models that analyzed medical datasets and predicted the likelihood of conditions like breast cancer, heart disease, or diabetes. By engaging directly with these tools, students saw how data-driven decision-making is becoming an essential part of preventive medicine and personalized care.

In the afternoon, Dr. Sarah Adel Bargal led an in-depth session on “Computer Vision and Convolutional Neural Networks (CNNs).” Students learned how CNNs power technologies such as facial recognition and advanced imaging diagnostics. Dr. Bargal broke down the architecture of CNNs, explaining how layers of convolution and pooling enable computers to detect patterns within visual data, including medical images. She showcased examples of how CNNs are being used to identify tumors in CT scans, flag abnormalities in X-rays, and even detect rare diseases in their early stages. Her lecture emphasized not only the technical sophistication of these tools but also their potential to expand access to quality diagnostics in under-resourced settings.

This session transitioned into Lab 6, where students applied what they had learned by training their own CNN models to classify medical images. They practiced using transfer learning techniques to improve accuracy, gaining a deeper appreciation for how AI systems learn and adapt to new challenges.

The evening was spent in focused project time with staff mentors, as interns began integrating technical insights from the day’s healthcare sessions into their AI business proposals. With a clearer understanding of how AI can impact global health, students were better equipped to design solutions that combined innovation with social good.