Pacific Symposium on Biocomputing 2026

Session Name: AI and Machine Learning in Clinical Medicine: Bridging or Separating Model Intelligence and Human Expertise

Short Description:

As AI increasingly shapes clinical decision-making, the challenge lies in aligning model intelligence with real-world medical needs. This session will highlight cutting-edge research on AI-driven decision support, clinician-AI collaboration, and responsible deployment of models in healthcare. From large language models enhancing documentation and patient communication to AI-assisted diagnostics and treatment planning, we will explore how these innovations improve clinical workflows while addressing concerns around trust, interpretability, and real-world impact.

Solicitation Webpage Content:

Machine learning technologies have transformed the capacity to analyze multi-dimensional and complex medical datasets. The advent of generative AI has further given rise to sophisticated large language models (LLMs) and text-to-image generators with dynamic interactive capabilities. Utilizing these advancements can improve patient care by strengthening clinical decision-making, enhancing monitoring, interpreting medical images, optimizing triage processes, and more.

In this session, we invite submissions within the broad spectrum of emerging machine learning advancements that offer solutions to solve healthcare challenges. Our focus is on research areas that demonstrate how AI can address specific clinical needs. While we anticipate that some algorithms may need further refinement for clinical application, we encourage submissions that propose clear, actionable use cases within the healthcare domain. We are particularly interested in papers that cover a variety of research topics, such as predictive analytics for patient outcomes, AI-driven personalized medicine approaches, natural language processing, federated learning, and LLMs for improved patient interaction and documentation which showcase the power of collaborative AI model development while upholding data privacy and compliance and enhancing diagnostic accuracy. Our session will be dedicated exclusively to the clinical applications of these methodologies and excludes multi-omics methods that are well covered by other PSB sessions. Our goal is to promote discussions that explore how researchers in machine learning can collaborate with healthcare practitioners to enhance the efficiency and effectiveness of modern healthcare systems.

Session topics

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The session is interested in research on the applications of emerging artificial intelligence models in solving real-world and well-defined problems in healthcare, novel methodologies and unique applications of previously developed methods, and clinical implementation of artificial intelligence tools.

Below are examples of submission topics that would be of interest:

● Generative artificial intelligence methods to solve real-world problems in healthcare.

● Rigorous evaluation of large language models and chatbots in analyzing clinical notes and solving narrow and well-defined healthcare tasks.

● Generative image and video processing models for medical image and video analysis.

● Multi-modal healthcare data analysis using artificial intelligence models to solve well-defined clinical tasks.

● Clinical validation of language and image analysis models.

● Novel applications of artificial intelligence in healthcare.

● Computational methods for public health that can screen large populations with high specificity.

● Computational approaches to analyze data, especially of varied types, to help inform diagnosis, including decision support tools to help streamline diagnosis or treatment.

● Methods for integrating the most up-to-date literature evidence and guidelines into clinical practice.

● Tools for analyzing multi-modal data such as lifestyle, environmental, geographic, and healthcare records to gain new insights for delivering better or tailored clinical care.

● Tools or methods that aid in data-centric artificial intelligence, with applications to medical tasks.

● Tools and methods for assessing bias in artificial intelligence algorithms.

● Tools and methods that aid in machine learning auditing and monitoring in the healthcare system.

● Tools or methods that aid in interpretability or explainability for machine learning in healthcare.

Session Organizers

	Dr. Jonathan Chen, MD/PhD is an Assistant Professor of Medicine and works with the Stanford Center for Biomedical Informatics Research at Stanford School of Medicine. He is a practicing physician who holds a PhD in computer science and has worked on clinical decision support tools using machine learning.
	Dr. Roxana Daneshjou, MD/PhD, is a board-certified dermatologist and Assistant Professor in Biomedical Data Science and Dermatology at Stanford.
	Dr. Fateme Nateghi Haredasht, PhD, is a postdoctoral scholar at the Stanford Center for Biomedical Informatics Research, where she is advancing machine learning integration in healthcare to unravel complex healthcare challenges and improve patient outcomes.
	Dr. Carsten Görg, PhD is an Assistant Professor of Biostatistics at the Colorado School of Public Health. He works on the translation of AI-based approaches into clinical settings using human-centered biomedical computing techniques.
	Dr. Joseph D. Romano, PhD, MPhil, MA is an Assistant Professor of Informatics and Pharmacology at the University of Pennsylvania. He is an expert in the integration and analysis of clinical and environmental health data using graph machine learning and other AI-based techniques and is a founding member of the NIH/NIEHS Environmental Health Language Collaborative.
	Dr. Dokyoon Kim is an associate professor of informatics in Biostatistics and Epidemiology at the University of Pennsylvania. As a Senior Fellow at the Institute of Biomedical Informatics and Associate Director of Informatics for Immune Health at the Perelman School of Medicine, Dr. Kim brings robust expertise in the integration of AI into translational informatics. He also serves as the Director of the Center for AI-Driven Translational Informatics (CATI).
	Dr. Alexander Ioannidis is an assistant professor of biomolecular engineering at UC Santa Cruz and an adjunct professor of computational and mathematical engineering at Stanford University. His research focuses on computational techniques and deep learning methods for genomics & precision health.
	Dr. Brett K Beaulieu-Jones is an Assistant Professor of Medicine at the University of Chicago. His research interests center on the use of machine learning in biomedical data, with a focus on phenotype definition for complex conditions and the integration of artificial intelligence tools into healthcare settings. He is an organizer of SAIL and on the board of AHLI (ML4H and CHIL) and will advertise this session to these communities.
	Dr. Geoffrey H. Tison, MD MPH is a practicing cardiologist, Associate Professor, and Co-Director of the Center for Biosignal Research at the University of California, San Francisco. He leads a computational research lab at UCSF (tison.ucsf.edu) that aims to improve cardiovascular disease prevention by applying artificial intelligence and statistical methods to large-scale medical data.

Submission Information

Important dates

● August 1, 2025: Call for papers deadline (no extensions will be granted)

● September 8, 2025: Notification of paper acceptance.

● October 1, 2025: Camera-ready final paper deadline.

● December 1, 2025: Poster abstract submission deadline.

● January 3-7, 2026: Conference dates

● All deadlines are due by 11:59 pm PT

Paper Format and Submission Portal

Please see the PSB paper format template and instructions at http://psb.stanford.edu/psb-online/psb-submit.

Paper Submissions

Unlike the abstracts at most biology conferences, papers in the PSB proceedings are archival, rigorously peer-reviewed publications. PSB publications are Open Access and linked directly from MEDLINE/PubMed and Google Scholar for wide accessibility. They should be thought of as short journal articles that may be cited on CVs and grant reports.

Travel Fellowships for Trainees

PSB traditionally provides fellowships for select trainees. The application process opens upon paper acceptance. Individuals from underrepresented communities are particularly encouraged to participate in the conference and apply for travel support.

Poster Format and Submission Portal

Poster presenters will be provided with an easel and a poster board 32" x 40" (80x100cm); either portrait or landscape orientation is acceptable. One poster from each paid participant is permitted. See the submission portal website for the instructions regarding poster submissions.