The long road ahead: navigating obstacles and building bridges for clinical integration of artificial intelligence technologies
Bibliographic record
Abstract
Abstract: Artificial intelligence (AI) holds immense promise for transforming healthcare, yet its real-world implementation faces significant obstacles. This comprehensive review synthesizes findings from over 40 peer-reviewed articles supplemented by reports from key institutions, to provide a thorough assessment of the challenges impeding AI integration in clinical settings and propose practical solutions. The paper identifies several major barriers: limited access to diverse, high-quality datasets, which hinders the development of robust, generalizable AI models; the “black box” nature of many AI systems, which impedes clinician trust and adoption; lack of clear legal and regulatory frameworks, raising liability concerns and safety issues; difficulties in adapting existing clinical workflows to incorporate AI tools, which can be disruptive and time-consuming; and challenges in protecting sensitive patient data while enabling AI development. To address these complex issues, the paper proposes a range of strategies, including standardizing data capture and labelling practices across healthcare institutions, developing explainable AI techniques tailored to clinical contexts, establishing clear regulatory guidelines for AI in healthcare, engaging healthcare professionals in AI development and implementation processes, and implementing robust data governance and cybersecurity measures. The review emphasizes the critical need for a multidisciplinary approach, involving close collaboration between AI developers, clinicians, policymakers, and patients. It highlights successful case studies where AI has been effectively integrated into clinical practice. However, the authors argue that while AI has the potential to be a powerful tool in the medical arsenal, it should be viewed as a complement to, rather than a replacement for, human clinical expertise. This approach paves the way for a future where AI meaningfully contributes to advancing healthcare while maintaining the highest standards of patient safety and ethical practice.
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 | 0.113 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.021 | 0.044 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".