Artificial Intelligence is no longer a futuristic concept—it is actively transforming how we diagnose diseases, stratify patients, and develop life-saving therapies. From AI-powered digital twins in oncology to real-time seizure detection and AI-driven drug discovery, this talk will explore how cutting-edge AI applications are reshaping precision medicine. Through three real-world case studies we will examine what can be the impact of AI in healthcare, and how entrepreneurs are solving tangible problems with AI-powered solutions. 

Chronic diseases affect billions of lives and account for over 80% of healthcare spending but lack effective diagnosis and treatment options due to the complex biology that underpins them. This presentation will discuss an AI-driven precision medicine approach that is enabling new understanding of the mechanisms that cause chronic diseases. I will demonstrate how these insights are being used to predict lifetime disease risk in patients, diagnose major diseases earlier with greater accuracy, identify effective treatments targeting the biological drivers of disease, and prevent the onset and progression of diseases to improve health outcomes and reduce health costs.

Despite advances in tumor measurement technologies and computational approaches such as AI for biomarker discovery, there remains a critical gap in adapting treatments to the dynamic biology of tumors as they mutate and develop resistance to therapies. This presentation will discuss approaches critical for the identification of multi-gene biomarkers from multi-modal data to inform therapy choices as cancer changes within evolutionary clinical trial designs that follow metastatic cancer patients longitudinally throughout their disease progression.

The FDA regulates artificial intelligence in precision medicine by ensuring that AI-based medical devices and software meet rigorous safety, efficacy, and quality standards before they are approved for clinical use. This presentation will cover some FDA frameworks for continuous oversight and post-market surveillance to address the unique challenges presented by adaptive learning algorithms.

Digital pathology serves as a discovery tool for biomarkers undetectable by the human eye, driving innovation in data generation to develop and validate novel biomarkers for patients with unmet needs, improving data accuracy, and treatment outcomes. Key challenges include developing and approving digital pathology (DP) algorithms as companion diagnostics (CDx). Collaboration among industry stakeholders and regulatory agencies is crucial to streamline approval processes and demonstrate clinical utility through follow-on strategies.

The ability of AI to integrate and interrogate multi-modal, large datasets and inputs within seconds has ushered in a new era in medicine that is changing every area of practice. Oncology lends itself well to AI applications because it is a genomically driven disease with complex management pathways. This presentation will discuss platforms that optimize treatment selection, drug discovery, and patient empowerment.

We apply the latest deep learning technologies to improve interpretation of human genetic variation in large biobank-scale cohorts, comprising hundreds of thousands of individuals with medical record data, and demonstrate novel insights for genetic risk prediction and drug target discovery.