Table 2.

Characteristics of the publications of quantitative data.

Study	Research methods	Sample size	Participants	Outcome variables	Concerned dimensions of explainability or integrability
Liaw et al [23], 2023	Semistructured interviews and surveys	22	Clinicians managing diabetes	Factors influencing the adoption of the tool, perception of the tool’s usefulness, and ease of use	Transparency, usability, and impact on clinic workflows need to be tailored to the demands and resources of clinics and communities.
Schoonderwoerd et al [38], 2021	Domain analysis, interviews, surveys, and scenario experiment	6	Pediatrician clinicians	Diagnosis, information they have used in their decision-making, and the importance ranking of different types of explanations in various contexts	The information that is used to make a diagnosis, the information that supports the diagnosis, how certain the clinician is of the diagnosis, and the relevance of the information for their diagnosis
Panagoulias et al [30], 2023	Survey	39	Medical personnel (including medical students and medical practitioners)	Suggested level of explainability, knowledge of AIa, ways to better integrate AI, and AI concerns	The overall system functions, user-friendly software, and impact on workflow
Ghanvatkar and Rajan [11], 2024	Theoretical construction and case analysis	—b	Clinicians	Usefulness of AI explanations for clinicians	Local explanations and global explanations
Fogliato et al [44], 2022	Scenario experiment	19	Radiologists	Anchoring effects; human-AI team diagnostic performance and agreement; time spent and confidence in decision-making; perceived usefulness of the AI	Do not waste time and no additional workload.

^aAI: artificial intelligence.

^bNot available.