ReviewOphthalmology and therapy2022
Translating the Machine: Skills that Human Clinicians Must Develop in the Era of Artificial Intelligence.
Review in Ophthalmology and therapy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Ethical and Regulatory Perception of Artificial Intelligence Among Dental Students: A Cross-Sectional Study.European journal of dental education : official journal of the Association for Dental Education in Europe · 2026Article
- A Competency Framework for Medical AI Education: Mixed Methods Study.JMIR medical education · 2026Article
- Attitudes and Knowledge Levels of Optometry Students and Educators Towards Artificial Intelligence in Optometric Practice: An Online Cross-Sectional Survey.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2026Article
- Beyond non-technical skills: training ophthalmologists to appraise and integrate emerging technologies.Eye (London, England) · 2025Article
- Clinical informatics training in medical school education curricula: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Towards the Use of Big Data in Healthcare: A Literature Review.Healthcare (Basel, Switzerland) · 2022Review
- Machine learning and pre-medical education.Artificial intelligence in medicine · 2022Article
- Machine Learning and Antibiotic Management.Antibiotics (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In coming decades, artificial intelligence (AI) platforms are expected to build on the profound achievements demonstrated in research papers towards implementation in real-world medicine. The implementation of AI systems is likely to be as an adjunct to clinicians rather than a replacement, but it still has the potential for a revolutionary impact on ophthalmology specifically and medicine in general in terms of addressing crucial scientific, socioeconomic and capacity challenges facing populations worldwide. In this paper we discuss the broad range of skills that clinicians should develop or refine to be able to fully embrace the opportunities that this technology will bring. We highlight the need for an awareness to identify AI systems that might already be in place and the need to be able to properly assess the utility of their outputs to correctly incorporate the AI system into clinical workflows. In a second section we discuss the need for clinicians to cultivate those human skills that are beyond the capabilities of the AI platforms and which should be just as important as ever. We describe the need for such an awareness by providing clinical examples of situations that might in the future arise in human interactions with machine algorithms. We also envisage a harmonious future in which an educated human and machine interaction can be optimised for the best possible patient experience and care.
Indexed as
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.