Contribution: Innovation for Defence Excellence and Security Program - Innovation Networks
McMaster University
$1,479,441.00
Quantifying Trust in Autonomous Medical Advisory Systems
FY 2019-20 · 2019-10-18
Summary
Artificial intelligence (AI) and machine learning (ML) are key to medical support and care in remote and austere environments. However, they introduce two primary challenges for trust and adoption, which are not adequately addressed in existing autonomous systems because they are not well understood; these include: 1) uncertainty in AI decision models, and 2) the impact of autonomous systems on human-machine interaction as a joint cognitive system (JCS). We propose to develop a framework to quantify trust in autonomous medical advisory systems (AMAS) decisions and optimize for integration into a joint cognitive system (JCS).
Department
Program
Innovation for Defence Excellence and Security Program - Innovation Networks
Recipient
Location
Hamilton, Ontario, Canada
Recipient Type
Academia
Type
Contribution
Source
Proactive Disclosure - Grants and Contributions
As published
Reference Number
180-2021-2022-Q2-CFPMN2-017
Amendment Number
1
Amendment Date
2021-09-07
Agreement Type
Contribution
Recipient Type
S
Recipient Legal Name
McMaster University
Recipient Operating Name
McMaster University
Research Organization
McMaster University
Recipient Country
CA
Recipient Province or Territory
ON
Recipient City
Hamilton
Recipient Postal Code
L8S 4L8
Federal Riding
Hamilton West--Ancaster--Dundas
Federal Riding (French)
Hamilton Ouest--Ancaster--Dundas
Program Name
Innovation for Defence Excellence and Security Program - Innovation Networks
Program Name (French)
Programme d’innovation pour la défense, l’excellence et la sécurité - Réseaux d'innovation
Program Purpose
Collaborate with academic and private sector partners to build a network of expertise to support Canada’s current and future defence and security needs.
Program Purpose (French)
Collaborez avec des partenaires des secteurs universitaire et industriel afin de mettre en place un réseau d’expertise pour soutenir les besoins actuels et futurs du Canada en matière de défense et de sécurité.
Agreement Title
Quantifying Trust in Autonomous Medical Advisory Systems
Agreement Title (French)
Mesure quantitative de la confiance envers les systèmes autonomes de consultation médicale.
Agreement Number
CFPMN2-017
Agreement Value
$1,479,441.00
Agreement Start Date
2019-10-18
Agreement End Date
2023-10-17
Description
Artificial intelligence (AI) and machine learning (ML) are key to medical support and care in remote and austere environments. However, they introduce two primary challenges for trust and adoption, which are not adequately addressed in existing autonomous systems because they are not well understood; these include: 1) uncertainty in AI decision models, and 2) the impact of autonomous systems on human-machine interaction as a joint cognitive system (JCS). We propose to develop a framework to quantify trust in autonomous medical advisory systems (AMAS) decisions and optimize for integration into a joint cognitive system (JCS).
Description (French)
L'intelligence artificielle (IA) et l'apprentissage machine (ML) sont essentiels au soutien et aux soins médicaux dans les environnements éloignés et austères. Cependant, elles introduisent deux défis principaux pour la confiance et l'adoption, qui ne sont pas traités de manière adéquate dans les systèmes autonomes existants car ils ne sont pas bien compris ; il s'agit de : 1) l'incertitude des modèles de décision de l'IA, et 2) l'impact des systèmes autonomes sur l'interaction homme-machine en tant que système cognitif conjoint (SCC). Nous proposons de développer un cadre pour quantifier la confiance dans les décisions des systèmes autonomes de conseil médical et d'optimiser leur intégration dans un système cognitif conjoint.
Expected Results
The research objectives of this Project are to quantify uncertainty in AI medical decision models; to evaluate the role of AMAS in the context of performing as a JCS with an individual and as a team member; and to optimize AMAS trust metrics for operational medicine applications employing body-worn sensors and portable diagnostic imaging from a Solution Readiness Level (SRL) 1 to SRL 6 over the life of the project.
Organization Code
dnd-mdn
Organization
National Defence | Défense nationale
