Approaching Patient-Clinician Relationships Issues Involving Artificial Intelligence Using Ethics of Care and Nursing Ethics
Bibliographic record
Abstract
Abstract With the rise of artificial intelligence (AI) and its application within industries, there is no doubt that someday AI will be one of the key players in medical diagnoses, assessments and treatments. With the involvement of AI in health care and medicine comes concerns pertaining to its application, more specifically its impact on both patients and medical professionals. To further expand on the discussion, using ethics of care, literature and a systematic review, we will address the impact of allowing AI to guide clinicians with medical procedures and decisions. We will then argue that the impact of allowing AI to guide clinicians with medical procedures and decisions can hinder patient-clinician relationships, concluding with a discussion on the future of patient care and how ethics of care can be used to investigate issues within AI in medicine.
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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 | 0.187 | 0.250 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.033 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".