Integrating artificial intelligence into healthcare systems: opportunities and challenges
Bibliographic record
Abstract
This article examines the integration of artificial intelligence (AI) in healthcare, highlighting both the opportunities and challenges it presents. AI offers significant advancements in healthcare, such as improving diagnostic accuracy, streamlining workflow processes, and enhancing patient care. The article synthesizes evidence from empirical studies and scholarly literature, with a focus on credible and reputable sources. Research indicates that AI has transformed healthcare innovation, particularly in clinical decision support and personalized treatment. However, the adoption of AI is not without challenges. Ethical and legal concerns, including patient privacy, remain prominent obstacles. Technical limitations, such as inconsistent risk management across healthcare settings and the need for reliable IT infrastructure, further complicate AI implementation. Moreover, the development of high-quality and diverse datasets is essential to improve data sharing and enhance decision-making accuracy in healthcare. While tools like telemedicine and remote patient monitoring improve access to care, they also increase the risk of unauthorized data breaches. To address these concerns, healthcare organizations must promote a culture of accountability, ensuring that healthcare providers remain vigilant about patient data security. Overall, the article underscores the potential of AI to revolutionize healthcare while emphasizing the need to address the ethical, technical, and security challenges it brings.
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 | 0.035 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.020 | 0.032 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".