Bibliographic record
Abstract
The integration of Artificial Intelligence (AI) into healthcare represents a transformative shift towards more accurate and efficient patient care, highlighted by advancements in disease detection, treatment adherence, and patient interaction. However, this technological evolution introduces significant ethical dilemmas, including concerns over empathetic care provision, data security, and bias in clinical decision-making. This literature review critically examines these ethical challenges and proposes potential solutions to ensure AI's beneficial integration into healthcare without compromising patient well-being. The exploration into AI's capacity for empathetic care reveals its limitations to cognitive empathy, suggesting a complementary role to human providers rather than a replacement. Data privacy concerns underscore the imperative for secure handling and consent mechanisms in the utilization of patient information, amidst the risk of misuse by large corporations. Furthermore, the review addresses AI-induced biases, advocating for diverse data representation and algorithmic transparency to mitigate discrimination and enhance treatment efficacy across varied populations. While acknowledging the potential of AI to revolutionize healthcare, this paper advocates for a cautious and ethically informed approach to its adoption, emphasizing the need for comprehensive legislation, stakeholder engagement, and ongoing scrutiny to safeguard against the erosion of trust and equity in patient care.
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 | 0.105 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.104 |
| Scholarly communication | 0.020 | 0.022 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.018 | 0.019 |
| 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".