The ethical challenges facing the widespread adoption of digital healthcare technology
Bibliographic record
Abstract
With the rise of telemedicine, wearable healthcare, and the greater leverage of 'big data' for precision medicine, various challenges present themselves to organisations, physicians, and patients. Beyond the practical, financial, and clinical considerations, we must not ignore the ethical imperative for fair and just applications to improve the field of healthcare for all. Given the increasing personalisation of medicine and the role technology will play at the interface of healthcare delivery, a thorough understanding of the challenges presented is critical for future physicians who will navigate a novel environment. This article aims to explore the ethical challenges that the adoption of digital healthcare technology presents, contextualised at multiple levels. Potential solutions are suggested to initiate a discussion about the future of medicine and digital healthcare.
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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 | 0.111 | 0.160 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.074 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.013 | 0.029 |
| Insufficient payload (model declined to judge) | 0.003 | 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".