Representation and Bias in Artificial Intelligence Models for Thyroid Cancer: A Systematic Review
Bibliographic record
Abstract
Background: There has been growing interest in the application of artificial intelligence (AI) in thyroid cancer care, given its potential to enhance diagnostic accuracy, predict patient outcomes, and personalize treatment plans. However, bias introduced during the development of AI algorithms used for thyroid cancer care poses a significant challenge, as biased datasets can lead to disparities in diagnosis and treatment recommendations, particularly in underrepresented populations. This systematic review evaluates the current landscape of AI models for thyroid cancer, focusing on demographic representation and potential biases. Methods: This systematic review was registered on PROSPERO (ID: CRD42024519238) and conducted in accordance with the Cochrane handbook and reported in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A literature search was performed on EMBASE, PubMed, and Google Scholar up to January 2024. Studies were included if they involved AI models for thyroid cancer management and provided demographic details. Data extraction and risk-of-bias assessments were conducted by two independent reviewers. Results: A total of 197 studies were included in the review, with the majority focusing on diagnosis ( n = 133) and prediction/prognosis ( n = 47). Most studies predominantly involved participants from China ( n = 124) and the United States ( n = 26), with more female participants ( n = 12,410) than males ( n = 4222). Ethnicity data from 197 studies (248,896 participants) revealed a significant underrepresentation of East Asians (14.6%) compared with their global thyroid cancer prevalence (18.7%), while White (26.8%) and Black participants (26.8%) were overrepresented relative to their global prevalence (20.7% and 11.3%, respectively). Socioeconomic factors, marital status, and race/ethnicity were less frequently considered in the models. Conclusion: The findings highlight significant gaps in the diversity and representativeness of data used in thyroid cancer AI models. Current models align with epidemiological trends but lack comprehensive demographic inclusion. As such, more representative AI models are required that account for all aspects of a patient’s demographics and sociocultural background. Future research should focus on developing and validating more equitable AI models to improve thyroid cancer care across diverse populations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".