White Coat Oversight of Black-Box Algorithms: Ethical Challenges in the Application of Artificial Intelligence in Healthcare
Bibliographic record
Abstract
Artificial intelligence (AI) is rapidly influencing the future of healthcare by increasing diagnostic accuracy, supporting personalised treatments, and improving system efficiency. This paper examines the ethical and regulatory issues that arise from incorporating AI into medical practice. Drawing on the evolution of AI from early systems such as MYCIN to more recent applications such as convolutional neural networks in imaging, the discussion highlights the importance of ethical oversight from the outset of development. Central themes include the necessity for transparency, strong data protection measures, algorithmic fairness, and responsible deployment. Explainable AI (XAI) technologies, international regulatory responses such as the European Union's AI Act, and inclusive design strategies are explored as key tools for ensuring equity in care delivery. Risks, including data misuse, embedded bias in training sets, and inappropriate reliance on opaque systems, are analysed with real-world examples. Ultimately, the paper calls for interdisciplinary cooperation among healthcare providers, developers, and regulators to create systems that enhance patient outcomes while remaining aligned with ethical and societal values.
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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 | 0.171 | 0.226 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.058 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.013 | 0.018 |
| Insufficient payload (model declined to judge) | 0.002 | 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".