Patient and clinician perspectives in the use of machine learning and artificial intelligence in the context of acute neurology
Bibliographic record
Abstract
Abstract Background Clinician perspectives on machine learning and artificial intelligence (ML/AI) vary with discipline. However, fewer studies describe patient perspectives and address medical situations that require rapid decisions with durable consequences for patient outcomes. This study characterized perspectives (qualitatively) and sentiment (quantitatively) regarding the use of ML/AI for patient management in neurological emergencies. Methods We conducted semi-structured interviews with survivors (or their proxy) of intracranial hemorrhage, and clinicians who care for such patients. Interviews were analyzed qualitatively using thematic analysis, and quantitatively using sentiment analysis to assess attitudes using a transformer-based language model with scores from -1 (most negative) to 1 (most positive). Results We enrolled 21 participants (14 patients, 1 proxy, and 6 clinicians) and reached thematic saturation. Help with clinical decision-making was cited as an advantage of ML/AI. Participants noted the importance of considering ML/AI as an adjunct to clinical care, not as a replacement for clinicians. Over-reliance on recommendations, potentially leading to diminution of clinician skill, incorrect ML/AI recommendations, potential liability, and bias were cited as challenges. Clinician and patient education were noted as a potential burden. Median sentiment scores ranged from 0.0 (neutral) to 0.3 (positive). Sentiment varied with question type (P < .001). Questions about clinicians using ML/AI for patient care had the highest sentiment score. Discussion and Conclusion Patients and clinicians expressed mixed views about ML/AI. Potential benefits related to improved decision-making and concerns focused on bias, liability, and the need for further education. Future work should address how best to incorporate ML/AI into education and obviate potential burdens as ML/AI is integrated into clinical care.
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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 | 0.015 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".