Details

Autor(en) / Beteiligte

• Tan, Ting Fang
• Thirunavukarasu, Arun James
• Campbell, J. Peter
• Keane, Pearse A.
• Pasquale, Louis R.
• Abramoff, Michael D.
• Kalpathy-Cramer, Jayashree
• Lum, Flora
• Kim, Judy E.
• Baxter, Sally L.
• Ting, Daniel Shu Wei

Titel

Generative Artificial Intelligence Through ChatGPT and Other Large Language Models in Ophthalmology

Ist Teil von

• Ophthalmology science (Online), 2023-12, Vol.3 (4), p.100394, Article 100394

Ort / Verlag

Elsevier Inc

Erscheinungsjahr

2023

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• The rapid progress of large language models (LLMs) driving generative artificial intelligence applications heralds the potential of opportunities in health care. We conducted a review up to April 2023 on Google Scholar, Embase, MEDLINE, and Scopus using the following terms: “large language models,” “generative artificial intelligence,” “ophthalmology,” “ChatGPT,” and “eye,” based on relevance to this review. From a clinical viewpoint specific to ophthalmologists, we explore from the different stakeholders’ perspectives—including patients, physicians, and policymakers—the potential LLM applications in education, research, and clinical domains specific to ophthalmology. We also highlight the foreseeable challenges of LLM implementation into clinical practice, including the concerns of accuracy, interpretability, perpetuating bias, and data security. As LLMs continue to mature, it is essential for stakeholders to jointly establish standards for best practices to safeguard patient safety. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.