Medical Machines: The Expanding Role of Ethics in Technology-Driven Healthcare
Bibliographic record
Abstract
Emerging technologies such as artificial intelligence are actively revolutionizing the healthcare industry. While there is widespread concern that these advances will displace human practitioners within the healthcare sector, there are several tasks – including original and nuanced ethical decision making – that they cannot replace. Further, the implementation of artificial intelligence in clinical practice can be anticipated to drive the production of novel ethical tensions surrounding its use, even while eliminating some of the technical tasks which currently compete with ethical deliberation for clinicians’ limited time. A new argument therefore arises to suggest that although these disruptive technologies will change the face of medicine, they may also foster a revival of several fundamental components inherent to the role of healthcare professionals, chiefly, the principal activities of moral philosophy. Accordingly, “machine medicine” presents a vital opportunity to reinvigorate the field of bioethics, rather than withdraw from it.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.038 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.086 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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".