Evidence map›Paper›PMID 39134176›Full record

ReviewAsia-Pacific journal of ophthalmology (Philadelphia, Pa.)

A review of ophthalmology education in the era of generative artificial intelligence.

Anna Heinke, Niloofar Radgoudarzi, Bonnie B Huang, Sally L Baxter

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Asia-Pacific journal of ophthalmology (Philadelphia, Pa.). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Ethical considerations in generative artificial intelligence for synthetic data generation in ophthalmology.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026
    Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Review
  10. Article
  11. Article
  12. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Anna HeinkeDivision of Ophthalmology Informatics and Data Science, The Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, 9415 Campus Point Drive, La Jolla, CA 92037, USA; Jacobs Retina Center, 9415 Campus Point Drive, La Jolla, CA 92037, USA.
Niloofar RadgoudarziDivision of Ophthalmology Informatics and Data Science, The Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, 9415 Campus Point Drive, La Jolla, CA 92037, USA; Division of Biomedical Informatics, Department of Medicine, University of California San Diego Health System, University of California San Diego, La Jolla, CA, USA.
Bonnie B HuangDivision of Ophthalmology Informatics and Data Science, The Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, 9415 Campus Point Drive, La Jolla, CA 92037, USA; Division of Biomedical Informatics, Department of Medicine, University of California San Diego Health System, University of California San Diego, La Jolla, CA, USA; Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Sally L BaxterDivision of Ophthalmology Informatics and Data Science, The Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, 9415 Campus Point Drive, La Jolla, CA 92037, USA; Division of Biomedical Informatics, Department of Medicine, University of California San Diego Health System, University of California San Diego, La Jolla, CA, USA. Electronic address: s1baxter@health.ucsd.edu.

Funding

Bridge2AI:Salutogenesis Data Generation ProjectOT2OD032644 · OD · WASHINGTON UNIVERSITY · PI BAXTER, SALLY LIU, CHUTE, CHRISTOPHER G · 2022 to 2025
$32.7M
Vision BiostatisticsP30EY022589 · NEI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Derek Stuart Welsbie · 2012 to 2026
$10.3M
San Diego Biomedical Informatics Education & Research (SABER)T15LM011271 · NLM · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SHAMIM NEMATI · 2012 to 2026
$9.7M
Multi-modal Health Information Technology Innovations for Precision Management of GlaucomaDP5OD029610 · OD · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI BAXTER, SALLY LIU · 2020 to 2024
$2.1M
NEI NIH HHS P30 EY022589NIH HHS DP5 OD029610NIH HHS OT2 OD032644NLM NIH HHS T15 LM011271
6 · The paper itself

Abstract

purposeTo explore the integration of generative AI, specifically large language models (LLMs), in ophthalmology education and practice, addressing their applications, benefits, challenges, and future directions.

designA literature review and analysis of current AI applications and educational programs in ophthalmology.

methodsAnalysis of published studies, reviews, articles, websites, and institutional reports on AI use in ophthalmology. Examination of educational programs incorporating AI, including curriculum frameworks, training methodologies, and evaluations of AI performance on medical examinations and clinical case studies.

resultsGenerative AI, particularly LLMs, shows potential to improve diagnostic accuracy and patient care in ophthalmology. Applications include aiding in patient, physician, and medical students' education. However, challenges such as AI hallucinations, biases, lack of interpretability, and outdated training data limit clinical deployment. Studies revealed varying levels of accuracy of LLMs on ophthalmology board exam questions, underscoring the need for more reliable AI integration. Several educational programs nationwide provide AI and data science training relevant to clinical medicine and ophthalmology.

conclusionsGenerative AI and LLMs offer promising advancements in ophthalmology education and practice. Addressing challenges through comprehensive curricula that include fundamental AI principles, ethical guidelines, and updated, unbiased training data is crucial. Future directions include developing clinically relevant evaluation metrics, implementing hybrid models with human oversight, leveraging image-rich data, and benchmarking AI performance against ophthalmologists. Robust policies on data privacy, security, and transparency are essential for fostering a safe and ethical environment for AI applications in ophthalmology.

Indexed as

Artificial IntelligenceCurriculumOphthalmologyEducation, MedicalHumansArtificial Intelligence (AI)Generative AILarge Language Models (LLMs)Ophthalmology Education

Identifiers

PMID39134176
PMCPMC11934932

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.