Shared decision-making in radiology: leadership levers for patient-centred imaging
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it