Exploring the Factors Influencing AI Integration in Clinical Diagnostic Decision-Making
Bibliographic record
Abstract
This study aimed to explore the key factors influencing the integration of artificial intelligence (AI) into clinical diagnostic decision-making from the perspective of healthcare professionals. This research employed a qualitative design based on semi-structured interviews with 23 healthcare professionals in Canada, including physicians, radiologists, clinical informaticians, nurse practitioners, and administrators. Participants were selected through purposive sampling to ensure diverse perspectives, and data collection continued until theoretical saturation was achieved. Interviews were transcribed verbatim and analyzed thematically using NVivo software, with codes and themes developed iteratively through inductive analysis and constant comparison. Four major themes emerged from the data: (1) technological infrastructure and readiness, (2) human and professional factors, (3) organizational culture and leadership, and (4) perceived value and impact of AI. Participants reported that outdated systems, poor interoperability, and insufficient technical support limited integration. Attitudes toward AI varied, with concerns about trust, autonomy, and training gaps. Organizational barriers included lack of leadership strategy and unclear implementation policies. While AI was recognized for enhancing diagnostic accuracy and efficiency, concerns about alert fatigue, liability, and ethical issues were prevalent. Patient trust, professional identity, and collaborative workflows also influenced AI adoption outcomes. Integrating AI into clinical diagnostics is a complex, multidimensional process shaped by technological, professional, organizational, and ethical factors. Beyond technical improvements, successful implementation requires a holistic, sociotechnical approach that addresses infrastructure, education, workflow design, and patient-clinician communication. Institutional strategies should prioritize clinician engagement, interdisciplinary collaboration, and transparent governance to foster responsible and effective AI adoption in healthcare settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.057 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".