Artificial intelligence in healthcare: a comprehensive review of its ethical concerns
Bibliographic record
Abstract
Purpose Nowadays, the digitized economy and technological advancements are increasing at a faster pace. One such technology that is gaining popularity in the healthcare sector is Artificial Intelligence (AI). AI has been debated much, searched so well due to the implications, issues and for its benefits in terms of ease, it will offer. The following research has focused on examining the ethical dilemmas associated with AI when it will be introduced in the healthcare sector. Design/methodology/approach A narrative review method focusing on content analysis has been used in the research. The authors have employed a deductive approach to determine the ethical facets of adopting AI in the healthcare sector. The current study is complemented by a review of related studies. The secondary data have been collected from authentic resources available on the Internet. Findings Patient privacy, biased results, patient safety and Human errors are some major ethical dilemmas that are likely to be faced once AI will be introduced in healthcare. The impact of ethical dilemmas can be minimized by continuous monitoring but cannot be eliminated in full if AI is introduced in healthcare. AI overall will increase the performance of the healthcare sector. However, we need to address some recommendations to mitigate the ethical potential issues that we could observe using AI. Technological change and AI can mimic the overall intellectual process of humans, which increases its credibility and also offers harm to humans. Originality/value Patient safety is the most crucial ethical concern because AI is a new technology and technology can lead to failure. Thus, we need to be certain that these new technological developments are ethically applied. The authors need to evaluate and assess the organizational and legal progress associated with the emergence of AI in the healthcare sector. It also highlights the importance of covering and protecting medical practitioners regarding the different secondary effects of this artificial medical progress. The research stresses the need of establishing partnerships between computer scientists and clinicians to effectively implement AI. Lastly, the research highly recommends training of IT specialists, healthcare and medical staff about healthcare ethics.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| 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".