Shared decision-making in radiology: leadership levers for patient-centred imaging
Bibliographic record
Abstract
BACKGROUND: Shared decision-making (SDM) is a cornerstone of patient-centred care, yet it has been underused in radiology. OBJECTIVE: To translate research into innovative strategies to empower radiology leaders to apply SDM and outline the cultural and structural changes required for meaningful integration into clinical practice. METHODS: This article synthesises case examples and evidence across imaging scenarios, evaluates emerging innovations and highlights leadership levers that can embed SDM as a core practice in radiology. RESULTS: Leadership interventions can transform radiology's contribution to SDM. Cases such as incidental pulmonary nodules, breast MRI in familial risk and Li-Fraumeni syndrome illustrate how radiologists can engage directly in preference-sensitive decisions. Key strategies include improving access to imaging data, using patient-friendly summaries, expanding opportunities for direct communication and incorporating patient-reported outcome measures, patient-reported experience measures and artificial intelligence (AI)-driven tools to support patient understanding. Barriers such as workflow demands, medicolegal uncertainty and lack of incentives can be addressed through leadership-driven reforms. CONCLUSIONS: Radiology plays a central role in care pathways, offers clinical and technical expertise and increasing patient-facing innovation. Leaders who embed SDM into training, workflows and systems can enhance radiology as a model of cutting-edge, patient-centred care. Clear actions include training, protected time, incentives, strategic application of AI and transformational leadership.
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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 | 0.054 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".