Artificial intelligence bias in medical system designs: a systematic review
Bibliographic record
Abstract
Inherent bias in the artificial intelligence (AI)-model brings inaccuracies and variabilities during clinical deployment of the model. It is challenging to recognize the source of bias in AI-model due to variations in datasets and black box nature of system design. Additionally, there is no distinct process to identify the potential source of bias in the AI-model. To the best of our knowledge, this is the first review of its kind that addresses the bias in AI-model by categorizing 48 studies into three classes, namely, point-based, image-based, and hybrid-based AI-models. Selection strategy using PRISMA is adopted to select the 72 crucial AI studies for identifying bias in AI models. Using the three classes, bias is identified in these studies based on 44 critical AI attributes. Bias in the AI-models is computed by analytical, butterfly, and ranking-based bias models. These bias models were evaluated using two experts and compared using variability analysis. AI-studies that lacked sufficient AI-attributes are more prone to risk-of-bias (RoB) in all three classes. Studies with high RoB loses fins in the butterfly model. It has been analyzed that the majority of the studies in healthcare suffer from data bias and algorithmic bias due to incomplete specifications mentioned in the design protocol and weak AI design exploited for prediction.
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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 | 0.083 | 0.301 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.013 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".