Toward an Ecologically Valid Conceptual Framework for the Use of Artificial Intelligence in Clinical Settings: Need for Systems Thinking, Accountability, Decision-making, Trust, and Patient Safety Considerations in Safeguarding the Technology and Clinicians
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Abstract
The health care management and the medical practitioner literature lack a descriptive conceptual framework for understanding the dynamic and complex interactions between clinicians and artificial intelligence (AI) systems. As most of the existing literature has been investigating AI’s performance and effectiveness from a statistical (analytical) standpoint, there is a lack of studies ensuring AI’s ecological validity. In this study, we derived a framework that focuses explicitly on the interaction between AI and clinicians. The proposed framework builds upon well-established human factors models such as the technology acceptance model and expectancy theory. The framework can be used to perform quantitative and qualitative analyses (mixed methods) to capture how clinician-AI interactions may vary based on human factors such as expectancy, workload, trust, cognitive variables related to absorptive capacity and bounded rationality, and concerns for patient safety. If leveraged, the proposed framework can help to identify factors influencing clinicians’ intention to use AI and, consequently, improve AI acceptance and address the lack of AI accountability while safeguarding the patients, clinicians, and AI technology. Overall, this paper discusses the concepts, propositions, and assumptions of the multidisciplinary decision-making literature, constituting a sociocognitive approach that extends the theories of distributed cognition and, thus, will account for the ecological validity of AI.
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Establishing a transparent framework for accountability mitigates risks.
“Establishing such a transparent framework for accountability not only mitigates risks but also fosters a culture of responsibility and collaboration among all involved parties [48].”
From Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education (2024) · cites this paper as [48]
Establishing a transparent framework for accountability fosters a culture of responsibility and collaboration among all involved parties.
“Establishing such a transparent framework for accountability not only mitigates risks but also fosters a culture of responsibility and collaboration among all involved parties [48].”
From Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education (2024) · cites this paper as [48]
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Self-citationClinical Reasoning in the Health Professions. 3rd Edition
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“The literature search identified 20 studies [1,3,7,14-30] that proposed a framework, guideline, or approach for the design, development, implementation, or evaluation of AI for clinical use cases (Figure 2).”
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- Authors
- Choudhury, A.
- Affiliations
- West Virginia University (US)
- Corresponding author
- Choudhury, Avishek
- ORCID
- Choudhury, Avishek
- Venue
- JMIR Human Factors
- Publisher
- JMIR Publications Inc.
- Published
- 2022-06-21
- Volume
- 9
- Issue
- 2
- Pages
- e35421
- ISSN
- 2292-9495 (ISSN-L)
- PMC
- PMC9257623
- OpenAlex
- W4283257876
- Cited by
- 56
- References
- 121
- Citations by year
- 2026: 20 · 2025: 12 · 2024: 14 · 2023: 4 · 2022: 6
- FWCI
- 2.71
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Human Factors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Keywords
- ecological validity, conceptual framework, technology acceptance model, expectancy theory, trust in AI, patient safety, accountability, decision-making, distributed cognition, bounded rationality, absorptive capacity, workload, AI adoption intention, human factors
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