BioPathways that Impact

Multimodal data integration stands to benefit all interconnected within the complex tapestry of healthcare.

Explainability in AI underpins responsible training, understanding, and deployment of novel, potentially transformative, technologies. By sharing their pioneering work, innovative initiatives, and insightful experiences, our speakers offer a form of explainability. By elucidating the intricate networks supporting AI systems and their implications, more ethical and effective healthcare solutions may form. Such ensures that AI advancements are accessible and comprehensible to all stakeholders involved in patient care, bridging the gap between high-tech algorithms and everyday medical practice.

Our integration of content spanning operational, computational, ethical, exploratory and practical discussions mirrors the multifaceted impact of multimodal data integration and AI throughout the entire healthcare spectrum—spanning from enhanced diagnostic precision to improved outcomes for patients and practitioners alike.

Dr. Drew F.K. Williamson

  • This presentation explores the history of extraction of features from whole slide images (WSIs), high-resolution digitized versions of tissue samples typically viewed under a microscope. Histologic images, derived from these WSIs, capture the intricate cellular and structural details of tissues, allowing pathologists to identify patterns critical for disease diagnosis. We will also explore the various options available in 2024 that are being used to extract these features.

Dr. Liron Pantanowitz

  • Artificial Intelligence (AI) has the potential to transform the field of pathology by addressing unmet needs and enhancing the efficiency and accuracy of diagnostic processes. This presentation aims to explore the feasibility of implementing AI in clinical pathology practices. We review the specific unmet needs within pathology that AI technology can potentially fulfill. We discuss the synergy of digital pathology with deep learning technologies. We also present various generative and non-generative AI applications in pathology.

Dr. Anil V. Parwani

  • Biobanks play an integral role in the nexus of personalized medicine, big data, and AI. Biobanks, as essential repositories for multimodal data, including digital pathology (DP) data and whole slide images (WSIs), are critical for supporting AI-driven research and clinical applications. This presentation will discuss the critical importance of biobanks in collecting and preserving biological samples, thereby enabling complex analyses and the development of personalized medicine solutions. We will explore how biobanks like the Cooperative Human Tissue Network (CHTN), a nationwide nonprofit offering academic and commercial investigators queried cancer samples from curated collections, are uniquely positioned to facilitate advanced medical research by providing high-quality, well-characterized biospecimens that are used to train and validate AI models. These models are increasingly applied in fields such as genitourinary (GU) oncopathology, where AI-enabled technologies offer greater diagnostic and prognostic precision and new disease insights from previously undiscoverable morphologic and molecular biomarkers. This session will also highlight the key challenges and innovations within the biobanking sector, focusing on aspects such as data management, interoperability, and ethical considerations in the collection and use of human tissue samples.

Dr. Steven Hart

  • In this presentation, we delve into the multifaceted nature of clinical AI deployment, emphasizing its intrinsic multimodal characteristics. The integration of artificial intelligence and machine learning within clinical environments transcends simple data analysis, encompassing a complex interplay of multidimensional data, technological frameworks, procedural nuances, and interdisciplinary collaboration. We advocate for the adoption of the Clinical AI Readiness Evaluator (CARE) framework, presenting it as a rigorous methodology to navigate and optimize this intricate landscape, thereby enhancing the efficacy, responsibility, and sustainability of healthcare innovations. This session is an invitation to the scientific community to reconceptualize clinical AI deployment as a rich, multidisciplinary endeavor, necessitating a concerted and informed approach. We will examine strategies to harness this complexity, ensuring that our advancements in AI-driven healthcare are both scientifically robust and aligned with the ultimate goal of enhancing patient outcomes.

Dr. John Paul Graff

Dr. Samir Atiya

Dr. Jason Hipp

Dr. Essa Mohamed