Purpose: To assess the accuracy of ophthalmic information provided by an artificial intelligence chatbot (ChatGPT).
Methods: Five diseases from 8 subspecialties of Ophthalmology were assessed by ChatGPT version 3.5. Three questions were asked to ChatGPT for each disease: what is x?; how is x diagnosed?; how is x treated? (x = name of the disease). Responses were graded by comparing them to the American Academy of Ophthalmology (AAO) guidelines for patients, with scores ranging from -3 (unvalidated and potentially harmful to a patient's health or well-being if they pursue such a suggestion) to 2 (correct and complete).
Main Outcomes: Accuracy of responses from ChatGPT in response to prompts related to ophthalmic health information in the form of scores on a scale from -3 to 2.
Results: Of the 120 questions, 93 (77.5%) scored ≥ 1. 27. (22.5%) scored ≤ -1; among these, 9 (7.5%) obtained a score of -3. The overall median score amongst all subspecialties was 2 for the question "What is x", 1.5 for "How is x diagnosed", and 1 for "How is x treated", though this did not achieve significance by Kruskal-Wallis testing.
Conclusions: Despite the positive scores, ChatGPT on its own still provides incomplete, incorrect, and potentially harmful information about common ophthalmic conditions, defined as the recommendation of invasive procedures or other interventions with potential for adverse sequelae which are not supported by the AAO for the disease in question. ChatGPT may be a valuable adjunct to patient education, but currently, it is not sufficient without concomitant human medical supervision.
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http://dx.doi.org/10.1038/s41433-023-02906-0 | DOI Listing |
J Chem Inf Model
January 2025
School of Information and Artificial Intelligence, Anhui Provincial Engineering Research Center for Beidou Precision Agriculture Information, Key Laboratory of Agricultural Sensors for Ministry of Agriculture and Rural Affairs, Anhui Agricultural University, Hefei, Anhui 230036, China.
Antimicrobial peptides (AMPs) are small peptides that play an important role in disease defense. As the problem of pathogen resistance caused by the misuse of antibiotics intensifies, the identification of AMPs as alternatives to antibiotics has become a hot topic. Accurately identifying AMPs using computational methods has been a key issue in the field of bioinformatics in recent years.
View Article and Find Full Text PDFJAMA Netw Open
January 2025
Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, Maryland.
Importance: Determining spectacle-corrected visual acuity (VA) is essential when managing many ophthalmic diseases. If artificial intelligence (AI) evaluations of macular images estimated this VA from a fundus image, AI might provide spectacle-corrected VA without technician costs, reduce visit time, or facilitate home monitoring of VA from fundus images obtained outside of the clinic.
Objective: To estimate spectacle-corrected VA measured on a standard eye chart among patients with diabetic macular edema (DME) in clinical practice settings using previously validated AI algorithms evaluating best-corrected VA from fundus photographs in eyes with DME.
Ophthalmol Ther
January 2025
International Health Policy Program (IHPP), Ministry of Public Health, Nonthaburi, Thailand.
Introduction: Screening diabetic retinopathy (DR) for timely management can reduce global blindness. Many existing DR screening programs worldwide are non-digital, standalone, and deployed with grading retinal photographs by trained personnel. To integrate the screening programs, with or without artificial intelligence (AI), into hospital information systems to improve their effectiveness, the non-digital workflow must be transformed into digital.
View Article and Find Full Text PDFWorld J Urol
January 2025
Department of Urology, Renmin Hospital of Wuhan University, 99 Zhang Zhi-dong Road, Wuhan, Hubei, 430060, P.R. China.
Purpose: To develop a deep learning (DL) model based on primary tumor tissue to predict the lymph node metastasis (LNM) status of muscle invasive bladder cancer (MIBC), while validating the prognostic value of the predicted aiN score in MIBC patients.
Methods: A total of 323 patients from The Cancer Genome Atlas (TCGA) were used as the training and internal validation set, with image features extracted using a visual encoder called UNI. We investigated the ability to predict LNM status while assessing the prognostic value of aiN score.
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