PSB 2023: Precision Medicine: Using Artificial Intelligence to improve diagnostics and healthcare


This year’s session will solicit research papers related to methodology development and applications of precision medicine and machine learning with a focus on approaches to improve healthcare.

‘Omics data have already begun to lay the groundwork for stratifying patients according to their individual risk and identifying targeted therapies. For example, the Clinical Pharmacogenetics Implementation Consortium (CPIC) has written guidelines for the clinical use of genetic variants for dosing drugs and avoiding adverse events. However, most of the research done in genomics has been in European ancestry populations; equitable precision medicine will require the inclusion of diverse populations.

Rich medical datasets allow the creation of tools that can help streamline care and provide decision support. Many of these tools leverage machine learning and deep learning techniques to streamline processes or provide decision support. We are interested in research looking at the full stack from “bytes” to “bedside” – papers on data-centric artificial intelligence, novel methodologies or unique applications of previously developed methods, and clinical implementation of machine learning tools.

Author: Andrew Coelho

Session topics

We are interested in research looking at the full stack from “bytes” to “bedside” – papers on data-centric artificial intelligence, novel methodologies and unique applications of previously developed methods, and clinical implementation of machine learning tools.

The above are just a few of the ways healthcare can be improved from knowledge gained from large-scale ‘omics and multi-modal medical datasets.

Broadly, we are interested in:

  • Methods for analyzing genomics and functional genomics data to identify patients at higher risk of disease or who might benefit from particular therapies (pharmacogenomics)
  • Methods that analyze multi-omic data to discover and understand mechanisms of disease, or which help inform development of therapies
  • 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
  • Methods for interpreting ‘omics data and/or incorporating these interpretations into the clinical care pipeline
  • Methods for using multi-omic and lifestyle data to improve personalized diagnostics that take into account an individual’s genetic background and environmental factors
  • Methods for analyzing population-specific genomic sequences that are not captured in the current reference human genome sequence
  • Methods for interpreting rare genetic variants in the context of disease or pharmacogenomics, particularly in traditionally under-represented populations whose genetics may not be well represented by commonly used genetic datasets or risk scores
  • Tools or methods that aid in data-centric artificial intelligence, with applications to medical tasks
  • Tools and methods for assessing bias in machine learning datasets or algorithms
  • Applications of machine learning methods within the healthcare system
  • 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

Steven E. Brenner

University of California, Berkeley

✉️ e-mail

Jonathan H. Chen

Stanford School of Medicine

✉️ e-mail

Dana C. Crawford

Case Western Reserve University

✉️ e-mail

Roxana Daneshjou

Stanford School of Medicine University

✉️ e-mail

Łukasz Kidziński

Stanford University & Clario

✉️ e-mail

David Ouyang

Smidt Heart Institute, Cedars-Sinai Medical Center

✉️ e-mail

Michelle Whirl-Carrillo*

Stanford University

✉️ e-mail

Primary contact:
Michelle Whirl-Carrillo,

Submission Information

The submitted papers are fully reviewed and accepted on a competitive basis.

Important dates

  • August 1, 2022: Call for papers deadline (no extensions will be granted)
  • September 1, 2022: Notification of paper acceptance.
  • October 3, 2022: Camera-ready final papers deadline.
  • December 5, 2022: Poster abstract submission deadline.
  • January 3-7, 2023: Conference dates.

Paper Format and Submission Portal

Please see the PSB paper format template and instructions at

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"W x 40"H (80x100cm). One poster from each paid participant is permitted. See the submission portal web site for the instructions regarding poster submissions.

Last updated: April 4th, 2022