TY - GEN

T1 - Foundation Models in Healthcare

T2 - 2023 CHI Conference on Human Factors in Computing Systems, CHI 2023

AU - Thieme, Anja

AU - Nori, Aditya

AU - Ghassemi, Marzyeh

AU - Bommasani, Rishi

AU - Andersen, Tariq Osman

AU - Luger, Ewa

N1 - Publisher Copyright: © 2023 Owner/Author.

PY - 2023

Y1 - 2023

N2 - Foundation models (FMs) are a new paradigm in AI. First pretrained on broad data at immense scale and subsequently adapted to more specific tasks, they achieve high performances and unlock powerful new capabilities to be leveraged in many domains, including healthcare. This SIG will bring together researchers and practitioners within the CHI community interested in such emerging technology and healthcare. Drawing attention to the rapid evolution of these models and proposals for their wide-spread adoption, we aim to demonstrate their strengths whilst simultaneously highlighting deficiencies and limitations that give raise to ethical and societal concerns. In particular, we will invite the community to actively debate how the field of HCI - with its research frameworks and methods - can help address some of these existing challenges and mitigate risks to ensure the safe and ethical use of the end-product; a requirement to realize many of the ambitious visions for how these models can positively transform healthcare delivery. This conversation will benefit from a diversity of voices, critical perspectives, and open debate, which are necessary to bring about the right norms and best practices, and to identify a path forward in devising responsible approaches to future FM design and use in healthcare.

AB - Foundation models (FMs) are a new paradigm in AI. First pretrained on broad data at immense scale and subsequently adapted to more specific tasks, they achieve high performances and unlock powerful new capabilities to be leveraged in many domains, including healthcare. This SIG will bring together researchers and practitioners within the CHI community interested in such emerging technology and healthcare. Drawing attention to the rapid evolution of these models and proposals for their wide-spread adoption, we aim to demonstrate their strengths whilst simultaneously highlighting deficiencies and limitations that give raise to ethical and societal concerns. In particular, we will invite the community to actively debate how the field of HCI - with its research frameworks and methods - can help address some of these existing challenges and mitigate risks to ensure the safe and ethical use of the end-product; a requirement to realize many of the ambitious visions for how these models can positively transform healthcare delivery. This conversation will benefit from a diversity of voices, critical perspectives, and open debate, which are necessary to bring about the right norms and best practices, and to identify a path forward in devising responsible approaches to future FM design and use in healthcare.

KW - ethics

KW - Foundation models

KW - healthcare

KW - interaction design

KW - responsible AI

KW - socio-technical systems

UR - http://www.scopus.com/inward/record.url?scp=85158143492&partnerID=8YFLogxK

U2 - 10.1145/3544549.3583177

DO - 10.1145/3544549.3583177

M3 - Article in proceedings

AN - SCOPUS:85158143492

BT - CHI 2023 - Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems

PB - Association for Computing Machinery, Inc.

Y2 - 23 April 2023 through 28 April 2023

ER -