Factors Influencing the Effectiveness of Artificial Intelligence-assisted Decision-Making in Medicine: A Scoping Review
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Moderator
Amy Krefman, MS
Health & Biomedical Informatics PhD Student
Presenter
Nicholas Jackson, PhD
PhD Student Biomedical Informatics
Statement of Purpose
AI systems can now match or exceed clinician performance on a growing range of diagnostic tasks, and health systems are rapidly deploying AI-based clinical decision support (AI-CDS). However, a high-performing AI model does not guarantee improved clinical decisions: recent trials have found that giving clinicians access to accurate AI assistance sometimes fails to improve their performance at all, and in real-world deployments clinicians frequently reject AI recommendations that conflict with their initial judgment. It remains unclear why AI assistance helps in some settings and not others, and the existing literature on this question is fragmented across disciplines with inconsistent terminology and mixed results.
In this webinar, I will present a scoping review that identified the clinician-level and technology design factors that influence the effectiveness of AI-assisted decision-making in medicine. Our synthesis found, intuitively, that clinicians' baseline attitudes toward AI consistently predict whether they accept its recommendations, and that incorrect AI advice reliably degrades clinician performance. However, we identified that several factors often proposed to improve AI-assisted decision-making have surprisingly heterogeneous effects. We interpret our findings under the concept of "appropriate trust": rather than seeking to maximize clinicians' trust in or acceptance of AI, the field should aim to ensure clinicians accept AI recommendations only when that trust is warranted. We further argue that this framework explains much of the heterogeneity in current evidence and offers concrete guidance for how health systems should evaluate, deploy, and study AI-CDS.
Learning Objectives
- Describe the clinician-level and technology design factors that influence whether AI-based clinical decision support improves decision-making performance.
- Explain the concept of appropriate trust and why interventions aimed at simply increasing clinicians' trust in AI can promote overreliance and degrade the quality of clinical decisions.
- Apply the appropriate trust framework when evaluating or implementing AI-based decision support in their own clinical or organizational settings, including anticipating how design components and user attributes may affect adoption and performance.
Additional Information
The target audience for this activity includes physicians, nurses, other healthcare providers, and medical informaticians.
No commercial support (funding from a governmental agency, ineligible company or in-kind donation) was received for this activity.
Completion of this “Enduring Material” is demonstrated by participating in the live webinar or viewing the on-demand recording, engaging with presenters during the live session by submitting questions, and completing the evaluation survey at the conclusion of the course.
Learners may claim credit and download a certificate upon submission of the evaluation. Participation in additional resources and the course forum is encouraged but optional.
ACCME Accreditation Statement
The American Medical Informatics Association is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.
Designation Statement
The American Medical Informatics Association designates this Enduring activity for a maximum of 1 AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
ANCC Accreditation Statement
The American Medical Informatics Association is accredited as a provider of nursing continuing professional development by the American Nurses Credentialing Center's Commission on Accreditation.
Nurse Planner (Content): Robin Austin, PhD, DNP, DC, RN, NI-BC, FAMIA, FAAN
Approved Contact Hours: 1
*Learners may earn 1 contact hour for each monthly Journal Club session, for a maximum of 6 contact hours per year. To receive the full 6 contact hours, participants must either attend the live webinar or view the on-demand recording for each Regularly Scheduled Series (RSS) Journal Club presentation.
It is the policy of the American Medical Informatics Association (AMIA) to ensure that Continuing Medical Education (CME) activities are independent and free of commercial bias. To ensure educational content is objective, balanced, and guarantee content presented is in the best interest of its learners and the public, the AMIA requires that everyone in a position to control educational content disclose all financial relationships with ineligible companies within the prior 24 months. An ineligible company is one whose primary business is producing, marketing, selling, re-selling or distributing healthcare products used by or on patients. Examples can be found at accme.org.
In accordance with the ACCME Standards for Integrity and Independence in Accredited Continuing Education, AMIA has implemented mechanisms prior to the planning and implementation of this CME activity to identify and mitigate all relevant financial relationships for all individuals in a position to control the content of this CME activity.
In accordance with the ACCME Standards for Integrity and Independence in Accredited Continuing Education, AMIA has implemented mechanisms prior to planning and implementation of this CME activity to identify and mitigate all relevant financial relationships for all individuals in a position to control the content of this CME activity.
Faculty and planners who refuse to disclose any financial relationships with ineligible companies will be disqualified from participating in the educational activity.
For an individual with no relevant financial relationship(s), course participants must be informed that no conflicts of interest or financial relationship(s) exist.
Planning Committee
The planning committee and reviewers reported that they have no relevant financial relationship(s) with ineligible companies to disclose.
- Joanna Abraham, PhD, FACMI, FAMIA
- Ratie Akabari, MS
- Zo Co
- Ivan Gu
- Andrew Lu, MSc, RN
- Elanore "Nora" Rae Scheer, ME
The following planning committee members have relevant financial relationship(s) with ineligible companies to disclose.
- Amy Krefman, MS - AbbVie; Individual Stocks/Stock Options
Presenter
The following presenters have no relevant financial relationship(s) with ineligible companies to disclose.
AMIA Staff
The AMIA staff have no relevant financial relationship(s) with ineligible companies to disclose.
*All of the relevant financial relationships listed for these individuals have been mitigated.