Exploring patient perspectives on how they can and should be engaged in the development of artificial intelligence (AI) applications in health care
Bibliographic record
Abstract
BACKGROUND: Artificial intelligence (AI) is a rapidly evolving field which will have implications on both individual patient care and the health care system. There are many benefits to the integration of AI into health care, such as predicting acute conditions and enhancing diagnostic capabilities. Despite these benefits potential harms include algorithmic bias, inadequate consent processes, and implications on the patient-provider relationship. One tool to address patients' needs and prevent the negative implications of AI is through patient engagement. As it currently stands, patients have infrequently been involved in AI application development for patient care delivery. Furthermore, we are unaware of any frameworks or recommendations specifically addressing patient engagement within the field of AI in health care. METHODS: We conducted four virtual focus groups with thirty patient participants to understand of how patients can and should be meaningfully engaged within the field of AI development in health care. Participants completed an educational module on the fundamentals of AI prior to participating in this study. Focus groups were analyzed using qualitative content analysis. RESULTS: We found that participants in our study wanted to be engaged at the problem-identification stages using multiple methods such as surveys and interviews. Participants preferred that recruitment methodologies for patient engagement included both in-person and social media-based approaches with an emphasis on varying language modalities of recruitment to reflect diverse demographics. Patients prioritized the inclusion of underrepresented participant populations, longitudinal relationship building, accessibility, and interdisciplinary involvement of other stakeholders in AI development. We found that AI education is a critical step to enable meaningful patient engagement within this field. We have curated recommendations into a framework for the field to learn from and implement in future development. CONCLUSION: Given the novelty and speed at which AI innovation is progressing in health care, patient engagement should be the gold standard for application development. Our proposed recommendations seek to enable patient-centered AI application development in health care. Future research must be conducted to evaluate the effectiveness of patient engagement in AI application development to ensure that both AI application development and patient engagement are done rigorously, efficiently, and meaningfully.
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How this classification was reachedexpand
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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".