Ethical considerations for large language models in ophthalmology

被引:2
作者
Kalaw, Fritz Gerald P. [1 ,2 ,3 ]
Baxter, Sally L. [1 ,2 ,3 ]
机构
[1] Univ Calif San Diego, Viterbi Family Dept Ophthalmol, Div Ophthalmol Informat & Data Sci, La Jolla, CA USA
[2] Univ Calif San Diego, Shiley Eye Inst, La Jolla, CA USA
[3] Univ Calif San Diego, Univ Calif San Diego Hlth Syst, Dept Biomed Informat, La Jolla, CA USA
基金
美国国家卫生研究院;
关键词
bias; clinical ethics; data privacy; large language model; misinformation; DISEASES; CHATGPT; IMAGES; RECORD;
D O I
10.1097/ICU.0000000000001083
中图分类号
R77 [眼科学];
学科分类号
100212 ;
摘要
Purpose of reviewThis review aims to summarize and discuss the ethical considerations regarding large language model (LLM) use in the field of ophthalmology.Recent findingsThis review of 47 articles on LLM applications in ophthalmology highlights their diverse potential uses, including education, research, clinical decision support, and surgical assistance (as an aid in operative notes). We also review ethical considerations such as the inability of LLMs to interpret data accurately, the risk of promoting controversial or harmful recommendations, and breaches of data privacy. These concerns imply the need for cautious integration of artificial intelligence in healthcare, emphasizing human oversight, transparency, and accountability to mitigate risks and uphold ethical standards.SummaryThe integration of LLMs in ophthalmology offers potential advantages such as aiding in clinical decision support and facilitating medical education through their ability to process queries and analyze ophthalmic imaging and clinical cases. However, their utilization also raises ethical concerns regarding data privacy, potential misinformation, and biases inherent in the datasets used. Awareness of these concerns should be addressed in order to optimize its utility in the healthcare setting. More importantly, promoting responsible and careful use by consumers should be practiced.
引用
收藏
页码:438 / 446
页数:9
相关论文
共 89 条
  • [1] Breaking Barriers in Behavioral Change: The Potential of Artificial Intelligence-Driven Motivational Interviewing
    Abid, Areeba
    Baxter, Sally L.
    [J]. JOURNAL OF GLAUCOMA, 2024, 33 (07) : 473 - 477
  • [2] Alder S., 2023, HIPAA Journal
  • [3] ChatGPT and Lacrimal Drainage Disorders: Performance and Scope of Improvement
    Ali, Mohammad Javed
    [J]. OPHTHALMIC PLASTIC AND RECONSTRUCTIVE SURGERY, 2023, 39 (03) : 221 - 225
  • [4] American Psychiatric Association, 2022, DIAGN STAT MAN MENT, DOI 10.1176/appi.books.9780890425596
  • [5] Overview of artificial intelligence in medicine
    Amisha
    Malik, Paras
    Pathania, Monika
    Rathaur, Vyas Kumar
    [J]. JOURNAL OF FAMILY MEDICINE AND PRIMARY CARE, 2019, 8 (07) : 2328 - 2331
  • [6] Large language models in vitreoretinal surgery
    Anguita, Rodrigo
    Makuloluwa, Achini
    Hind, Jennifer
    Wickham, Louisa
    [J]. EYE, 2024, 38 (04) : 809 - 810
  • [7] [Anonymous], FDA Approves SYFOVRETM (pegcetacoplan injection) as the first and only treatment for geographic atrophy (GA), a leading cause of blindness
  • [8] Evaluating the Performance of ChatGPT in Ophthalmology
    Antaki, Fares
    Touma, Samir
    Milad, Daniel
    El -Khoury, Jonathan
    Duval, Renaud
    [J]. OPHTHALMOLOGY SCIENCE, 2023, 3 (04):
  • [9] Accuracy of automated machine learning in classifying retinal pathologies from ultra-widefield pseudocolour fundus images
    Antaki, Fares
    Coussa, Razek Georges
    Kahwati, Ghofril
    Hammamji, Karim
    Sebag, Mikael
    Duval, Renaud
    [J]. BRITISH JOURNAL OF OPHTHALMOLOGY, 2023, 107 (01) : 90 - 95
  • [10] Balas M., 2023, JFO Open Ophthalmology, V1, P100005, DOI DOI 10.1016/J.JFOP.2023.100005