Bias Mitigation in Primary Healthcare Artificial Intelligence Models: A Scoping Review
Bibliographic record
Abstract
Background: Artificial intelligence (AI) predictive models in primary healthcare can potentially lead to benefits for population health. Algorithms can identify more rapidly and accurately who should receive care and health services, but they could also perpetuate or exacerbate existing biases toward diverse groups. We noticed a gap in actual knowledge about which strategies are deployed to assess and mitigate bias toward diverse groups, based on their personal or protected attributes, in primary healthcare algorithms. Objectives: To identify and describe attempts, strategies, and methods to mitigate bias in primary healthcare artificial intelligence models, which diverse groups or protected attributes have been considered, and what are the results on bias attenuation and AI models performance. Methods: We conducted a scoping review informed by the Joanna Briggs Institute (JBI) review recommendations and an experienced librarian developed a search strategy. Results: After the removal of 585 duplicates, we screened 1018 titles and abstracts. Of the remaining 189 after exclusion, we excluded 172 full texts and included 17 studies. The most investigated personal or protected attributes were Race (or Ethnicity) in (12/17), and Sex, using binary “male vs female” in (10/17) of included studies. We grouped studies according to bias mitigation attempts in 1) existing AI models or datasets, 2) sourcing data such as Electronic Health Records, 3) developing tools with “human-in-the-loop” and 4) identifying ethical principles for informed decision-making. Mathematical and algorithmic preprocessing methods, such as changing data labeling and reweighing, and a natural language processing method using data extraction from unstructured notes, showed the greatest potential. Other processing methods, such as groups recalibration and equalized odds, exacerbated predictions errors between groups or resulted in overall models miscalibrations. Conclusions: Results suggests that biases toward diverse groups can be more easily mitigated when data are open-sourced, multiple stakeholders are involved, and at the algorithm’ preprocessing stage. Further empirical studies with more diverse groups considered, such as nonbinary gender identities or Indigenous peoples in Canada, are needed to confirm and to expand this knowledge.
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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 | 0.137 | 0.458 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.028 | 0.025 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".