Human Factors and Technological Characteristics Influencing the Interaction of Medical Professionals With Artificial Intelligence–Enabled Clinical Decision Support Systems: Literature Review
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Abstract
Background: The digitization and automation of diagnostics and treatments promise to alter the quality of health care and improve patient outcomes, whereas the undersupply of medical personnel, high workload on medical professionals, and medical case complexity increase. Clinical decision support systems (CDSSs) have been proven to help medical professionals in their everyday work through their ability to process vast amounts of patient information. However, comprehensive adoption is partially disrupted by specific technological and personal characteristics. With the rise of artificial intelligence (AI), CDSSs have become an adaptive technology with human-like capabilities and are able to learn and change their characteristics over time. However, research has not reflected on the characteristics and factors essential for effective collaboration between human actors and AI-enabled CDSSs. Objective: Our study aims to summarize the factors influencing effective collaboration between medical professionals and AI-enabled CDSSs. These factors are essential for medical professionals, management, and technology designers to reflect on the adoption, implementation, and development of an AI-enabled CDSS. Methods: We conducted a literature review including 3 different meta-databases, screening over 1000 articles and including 101 articles for full-text assessment. Of the 101 articles, 7 (6.9%) met our inclusion criteria and were analyzed for our synthesis. Results: We identified the technological characteristics and human factors that appear to have an essential effect on the collaboration of medical professionals and AI-enabled CDSSs in accordance with our research objective, namely, training data quality, performance, explainability, adaptability, medical expertise, technological expertise, personality, cognitive biases, and trust. Comparing our results with those from research on non-AI CDSSs, some characteristics and factors retain their importance, whereas others gain or lose relevance owing to the uniqueness of human-AI interactions. However, only a few (1/7, 14%) studies have mentioned the theoretical foundations and patient outcomes related to AI-enabled CDSSs. Conclusions: Our study provides a comprehensive overview of the relevant characteristics and factors that influence the interaction and collaboration between medical professionals and AI-enabled CDSSs. Rather limited theoretical foundations currently hinder the possibility of creating adequate concepts and models to explain and predict the interrelations between these characteristics and factors. For an appropriate evaluation of the human-AI collaboration, patient outcomes and the role of patients in the decision-making process should be considered.
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Reliance is inherently multifaceted, varying significantly between individuals.
“However, reliance is inherently multifaceted, varying significantly between individuals.”
From Investigating appropriate reliance on AI-Based decision support systems: the role of expertise, trust, and self-confidence (2025) · cites this paper as Knop et al., 2022
The Results section (Factors Influencing Collaboration; page not provided) describes trust in AI-enabled clinical decision support as depending on personality, system design, and cognitive biases, supporting a multifaceted account. It does not establish that reliance itself varies significantly between individuals; the discussion of individual factors concerns interaction and trust rather than measured variation in reliance. (LLM, conf=0.84)
Experimental — accuracy not yet measuredNeed for cognition is an important factor influencing acceptance of and trust in decision support systems.
“The concept of the need for cognition has been identified in various studies as an important factor influencing the acceptance of and trust in decision support systems [ 10 , 11 , 21 ].”
From Psychological Factors Influencing Appropriate Reliance on AI-enabled Clinical Decision Support Systems: Experimental Web-Based Study Among Dermatologists (2025) · cites this paper as [11]
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Psychological Traits and Appropriate Reliance: Factors Shaping Trust in AI
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- Authors
- Knop, M., Weber, S., Mueller, M., & Niehaves, B.
- Affiliations
- University of Siegen (DE)
- Corresponding author
- Knop, Michael
- Venue
- JMIR Human Factors
- Publisher
- JMIR Publications Inc.
- Published
- 2022-03-24
- Volume
- 9
- Issue
- 1
- Pages
- e28639
- ISSN
- 2292-9495 (ISSN-L)
- PMC
- PMC8990344
- OpenAlex
- W4210858667
- Cited by
- 103
- References
- 77
- Citations by year
- 2026: 43 · 2025: 23 · 2024: 17 · 2023: 15 · 2022: 5
- FWCI
- 1.03
- 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
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Technology and Human Factors in Education and Health
- Keywords
- clinical decision support systems, artificial intelligence, human-AI collaboration, medical professionals, human factors, technological characteristics, explainability, trust in AI, cognitive biases, training data quality, medical expertise, technical expertise, personality, artificial intelligence adoption, patient involvement in treatment decisions
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