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 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 | 0.015 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".