Dr. Google vs. Dr. ChatGPT: Exploring the Use of Artificial Intelligence in Ophthalmology by Comparing the Accuracy, Safety, and Readability of Responses to Frequently Asked Patient Questions Regarding Cataracts and Cataract Surgery.

PURPOSE: Patients are using online search modalities to learn about their eye health. While Google remains the most popular search engine, the use of large language models (LLMs) like ChatGPT has increased. Cataract surgery is the most common surgical procedure in the US, and there is limited data on the quality of online information that populates after searches related to cataract surgery on search engines such as Google and LLM platforms such as ChatGPT. We identified the most common patient frequently asked questions (FAQs) about cataracts and cataract surgery and evaluated the accuracy, safety, and readability of the answers to these questions provided by both Google and ChatGPT. We demonstrated the utility of ChatGPT in writing notes and creating patient education materials. METHODS: The top 20 FAQs related to cataracts and cataract surgery were recorded from Google. Responses to the questions provided by Google and ChatGPT were evaluated by a panel of ophthalmologists for accuracy and safety. Evaluators were also asked to distinguish between Google and LLM chatbot answers. Five validated readability indices were used to assess the readability of responses. ChatGPT was instructed to generate operative notes, post-operative instructions, and customizable patient education materials according to specific readability criteria. RESULTS: Responses to 20 patient FAQs generated by ChatGPT were significantly longer and written at a higher reading level than responses provided by Google (p < .001), with an average grade level of 14.8 (college level). Expert reviewers were correctly able to distinguish between a human-reviewed and chatbot generated response an average of 31% of the time. Google answers contained incorrect or inappropriate material 27% of the time, compared with 6% of LLM generated answers (p < .001). When expert reviewers were asked to compare the responses directly, chatbot responses were favored (66%). CONCLUSIONS: When comparing the responses to patients' cataract FAQs provided by ChatGPT and Google, practicing ophthalmologists overwhelming preferred ChatGPT responses. LLM chatbot responses were less likely to contain inaccurate information. ChatGPT represents a viable information source for eye health for patients with higher health literacy. ChatGPT may also be used by ophthalmologists to create customizable patient education materials for patients with varying health literacy.

Unilateral Retinopathy in a Preterm Infant With 4q Duplication: Description and Management.

A 33-5/7, 1570 g dichorionic diamniotic twin presented with cryptorchidism, failed hearing examination (both ears), poor feeding, profound hypoglycemia, coagulopathy, conjugated hyper-bilirubinemia, hydronephrosis, and hypotension. Microarray sent with results of whole genome SNP microgray analysis detected an interstitial duplication of the chromosomal segment 4q35 1q35.2. On this basis, telemedicine screening was performed to evaluate for ocular abnormalities in association with abnormal gene testing. Unilateral advanced retinopathy was noted affecting the right eye, with mature vascularization in the left eye. This infant was managed in concordance with retinopathy of prematurity guidelines, despite not making screening criteria. Off-label intravitreal bevacizumab injection (0.625 mg in 0.025 mL) resulted in full vascular maturation assessed by fluorescein angiography 6 months later. This represents the first description and management of retinopathy in 4q duplication syndrome. [Ophthalmic Surg Lasers Imaging Retina 2024;55:228-230.].

Evaluating Visual Acuity in the American Academy of Ophthalmology IRIS® Registry.

Objective: To describe visual acuity data representation in the American Academy of Ophthalmology Intelligent Research in Sight (IRIS) Registry and present a data-cleaning strategy. Design: Reliability and validity study. Participants: Patients with visual acuity records from 2018 in the IRIS Registry. Methods: Visual acuity measurements and metadata were identified and characterized from 2018 IRIS Registry records. Metadata, including laterality, assessment method (distance, near, and unspecified), correction (corrected, uncorrected, and unspecified), and flags for refraction or pinhole assessment were compared between Rome (frozen April 20, 2020) and Chicago (frozen December 24, 2021) versions. We developed a data-cleaning strategy to infer patients' corrected distance visual acuity in their better-seeing eye. Main Outcome Measures: Visual acuity data characteristics in the IRIS Registry. Results: The IRIS Registry Chicago data set contains 168 920 049 visual acuity records among 23 001 531 unique patients and 49 968 974 unique patient visit dates in 2018. Visual acuity records were associated with refraction in 5.3% of cases, and with pinhole in 11.0%. Mean (standard deviation) of all measurements was 0.26 (0.41) logarithm of the minimum angle of resolution (logMAR), with a range of - 0.3 to 4.0 A plurality of visual acuity records were labeled corrected (corrected visual acuity [CVA], 39.1%), followed by unspecified (37.6%) and uncorrected (uncorrected visual acuity [UCVA], 23.4%). Corrected visual acuity measurements were paradoxically worse than same day UCVA 15% of the time. In aggregate, mean and median values were similar for CVA and unspecified visual acuity. Most visual acuity measurements were at distance (59.8%, vs. 32.1% unspecified and 8.2% near). Rome contained more duplicate visual acuity records than Chicago (10.8% vs. 1.4%). Near visual acuity was classified with Jaeger notation and (in Chicago only) also assigned logMAR values by Verana Health. LogMAR values for hand motion and light perception visual acuity were lower in Chicago than in Rome. The impact of data entry errors or outliers on analyses may be reduced by filtering and averaging visual acuity per eye over time. Conclusions: The IRIS Registry includes similar visual acuity metadata in Rome and Chicago. Although fewer duplicate records were found in Chicago, both versions include duplicate and atypical measurements (i.e., CVA worse than UCVA on the same day). Analyses may benefit from using algorithms to filter outliers and average visual acuity measurements over time. Financial Disclosures: Proprietary or commercial disclosure may be found found in the Footnotes and Disclosures at the end of this article.

United States Population Disparities in Ophthalmic Care: Blindness and Visual Impairment in the IRIS® Registry (Intelligent Research in Sight).

PURPOSE: To evaluate associations of patient characteristics with United States eye care use and likelihood of blindness. DESIGN: Retrospective observational study. PARTICIPANTS: Patients (19 546 016) with 2018 visual acuity (VA) records in the American Academy of Ophthalmology's IRIS® Registry (Intelligent Research in Sight). METHODS: Legal blindness (20/200 or worse) and visual impairment (VI; worse than 20/40) were identified from corrected distance acuity in the better-seeing eye and stratified by patient characteristics. Multivariable logistic regressions evaluated associations with blindness and VI. Blindness was mapped by state and compared with population characteristics. Eye care use was analyzed by comparing population demographics with United States Census estimates and proportional demographic representation among blind patients versus a nationally representative US population sample (National Health and Nutritional Examination Survey [NHANES]). MAIN OUTCOME MEASURES: Prevalence and odds ratios for VI and blindness; proportional representation in the IRIS® Registry, Census, and NHANES by patient demographics. RESULTS: Visual impairment was present in 6.98% (n = 1 364 935) and blindness in 0.98% (n = 190 817) of IRIS patients. Adjusted odds of blindness were highest among patients ≥ 85 years old (odds ratio [OR], 11.85; 95% confidence interval [CI], 10.33-13.59 vs. those 0-17 years old). Blindness also was associated positively with rural location and Medicaid, Medicare, or no insurance vs. commercial insurance. Hispanic (OR, 1.59; 95% CI, 1.46-1.74) and Black (OR, 1.73; 95% CI, 1.63-1.84) patients showed a higher odds of blindness versus White non-Hispanic patients. Proportional representation in IRIS Registry relative to the Census was higher for White than Hispanic (2- to 4-fold) or Black (11%-85%) patients (P < 0.001). Blindness overall was less prevalent in NHANES than IRIS Registry; however, prevalence in adults aged 60+ was lowest among Black participants in the NHANES (0.54%) and second highest among comparable Black adults in IRIS (1.57%). CONCLUSIONS: Legal blindness from low VA was present in 0.98% of IRIS patients and associated with rural location, public or no insurance, and older age. Compared with US Census estimates, minorities may be underrepresented among ophthalmology patients, and compared with NHANES population estimates, Black individuals may be overrepresented among blind IRIS Registry patients. These findings provide a snapshot of US ophthalmic care and highlight the need for initiatives to address disparities in use and blindness. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

The Impact of COVID-19 on Missed Ophthalmology Clinic Visits.

PURPOSE: To measure the COVID-19 pandemic impact on missed ophthalmology clinic visits and the influence of patient and eye disease characteristics on likelihood of missing clinic visits before and during the pandemic. PATIENTS AND METHODS: A retrospective observational study analyzing eye clinic patients at a large tertiary care academic institution. We identified patients scheduled for eye care during pre-COVID-19 (January 1-February 29, 2020) and early COVID-19 (March 16-May 31, 2020) time periods. Missed appointment frequency and characteristics were evaluated during each time period. Multivariable logistic regression models were developed to examine adjusted odds of having at least one missed appointment during a given time period. Covariates included age, sex, race/ethnicity, marital status, preferred language (non-English vs English), insurance, distance from clinic, and diagnosis. RESULTS: Overall, 82.0% (n = 11,998) of pre-COVID-19 patients completed all scheduled visits, compared to only 59.3% (n = 9020) during COVID-19. Missed visits increased dramatically in late March 2020, then improved week by week through the end of May 2020. General ophthalmology/cataract and strabismus clinics had the highest rates of missed clinic visits during the COVID-19 period; neuro-ophthalmology, retina, cornea, oculoplastics and glaucoma had the lowest. Females, Blacks, Hispanics, Asians, ages 50+, and married patients had higher adjusted odds of missing clinic visits, both pre-COVID-19 and during COVID-19. Asian, elderly, and cataract patients had the highest adjusted odds of missing clinic visits during COVID-19 and had significant increases in odds compared to pre-COVID-19. Non-married, diabetic macular edema, and wet age-related macular degeneration patients had the lowest adjusted odds of missed visits during COVID-19. CONCLUSION: Missed clinic visits increased dramatically during the COVID-19 pandemic, particularly among elderly and nonwhite patients. These findings reflect differences in eye care delivery during the pandemic, and they indicate opportunities to target barriers to care, even during non-pandemic eras.

Toward a Mobile Platform for Real-world Digital Measurement of Depression: User-Centered Design, Data Quality, and Behavioral and Clinical Modeling.

BACKGROUND: Although effective mental health treatments exist, the ability to match individuals to optimal treatments is poor, and timely assessment of response is difficult. One reason for these challenges is the lack of objective measurement of psychiatric symptoms. Sensors and active tasks recorded by smartphones provide a low-burden, low-cost, and scalable way to capture real-world data from patients that could augment clinical decision-making and move the field of mental health closer to measurement-based care. OBJECTIVE: This study tests the feasibility of a fully remote study on individuals with self-reported depression using an Android-based smartphone app to collect subjective and objective measures associated with depression severity. The goals of this pilot study are to develop an engaging user interface for high task adherence through user-centered design; test the quality of collected data from passive sensors; start building clinically relevant behavioral measures (features) from passive sensors and active inputs; and preliminarily explore connections between these features and depression severity. METHODS: A total of 600 participants were asked to download the study app to join this fully remote, observational 12-week study. The app passively collected 20 sensor data streams (eg, ambient audio level, location, and inertial measurement units), and participants were asked to complete daily survey tasks, weekly voice diaries, and the clinically validated Patient Health Questionnaire (PHQ-9) self-survey. Pairwise correlations between derived behavioral features (eg, weekly minutes spent at home) and PHQ-9 were computed. Using these behavioral features, we also constructed an elastic net penalized multivariate logistic regression model predicting depressed versus nondepressed PHQ-9 scores (ie, dichotomized PHQ-9). RESULTS: A total of 415 individuals logged into the app. Over the course of the 12-week study, these participants completed 83.35% (4151/4980) of the PHQ-9s. Applying data sufficiency rules for minimally necessary daily and weekly data resulted in 3779 participant-weeks of data across 384 participants. Using a subset of 34 behavioral features, we found that 11 features showed a significant (P<.001 Benjamini-Hochberg adjusted) Spearman correlation with weekly PHQ-9, including voice diary-derived word sentiment and ambient audio levels. Restricting the data to those cases in which all 34 behavioral features were present, we had available 1013 participant-weeks from 186 participants. The logistic regression model predicting depression status resulted in a 10-fold cross-validated mean area under the curve of 0.656 (SD 0.079). CONCLUSIONS: This study finds a strong proof of concept for the use of a smartphone-based assessment of depression outcomes. Behavioral features derived from passive sensors and active tasks show promising correlations with a validated clinical measure of depression (PHQ-9). Future work is needed to increase scale that may permit the construction of more complex (eg, nonlinear) predictive models and better handle data missingness.

Artificial intelligence in global ophthalmology: using machine learning to improve cataract surgery outcomes at Ethiopian outreaches.

Differences between target and implanted intraocular lens (IOL) power in Ethiopian cataract outreach campaigns were evaluated, and machine learning (ML) was applied to optimize the IOL inventory and minimize avoidable refractive error. Patients from Ethiopian cataract campaigns with available target and implanted IOL records were identified, and the diopter difference between the two was measured. Gradient descent (an ML algorithm) was used to generate an optimal IOL inventory, and we measured the models performance across varying surplus levels. Only 45.6% of patients received their target IOL power and 23.6% received underpowered IOLs with current inventory (50% surplus). The ML-generated IOL inventory ensured that more than 99.5% of patients received their target IOL when using only 39% IOL surplus. In Ethiopian cataract campaigns, most patients have avoidable postoperative refractive error secondary to suboptimal IOL inventory. Optimizing the IOL inventory using this ML model might eliminate refractive error from insufficient inventory and reduce costs.

High-quality MS/MS spectrum prediction for data-dependent and data-independent acquisition data analysis.

Peptide fragmentation spectra are routinely predicted in the interpretation of mass-spectrometry-based proteomics data. However, the generation of fragment ions has not been understood well enough for scientists to estimate fragment ion intensities accurately. Here, we demonstrate that machine learning can predict peptide fragmentation patterns in mass spectrometers with accuracy within the uncertainty of measurement. Moreover, analysis of our models reveals that peptide fragmentation depends on long-range interactions within a peptide sequence. We illustrate the utility of our models by applying them to the analysis of both data-dependent and data-independent acquisition datasets. In the former case, we observe a q-value-dependent increase in the total number of peptide identifications. In the latter case, we confirm that the use of predicted tandem mass spectrometry spectra is nearly equivalent to the use of spectra from experimental libraries.

False News of a Cannabis Cancer Cure.

Background There is increasing concern among healthcare communities about the misinformation online about using cannabis to cure cancer. We have characterized this online interest in using cannabis as a cancer treatment and the propagation of this information on social media. Materials & methods We compared search activity over time for cannabis and cancer versus standard cancer therapies using Google Trends' relative search volume (RSV) tool and determined the impact of cannabis legalization. We classified news on social media about cannabis use in cancer as false, accurate, or irrelevant. We evaluated the cannabis-related social media activities of cancer organizations. Results The online search volume for cannabis and cancer increased at 10 times the rate of standard therapies (RSV 0.10/month versus 0.01/month, p<0.001), more so in states where medical or recreational cannabis is legal. The use of cannabis as a cancer cure represented the largest category (23.5%) of social media content on alternative cancer treatments. The top false news story claiming cannabis as a cancer cure generated 4.26 million engagements on social media, while the top accurate news story debunking this false news generated 0.036 million engagements. Cancer organizations infrequently addressed cannabis (average 0.7 Tweets; 0.4 Facebook posts), with low influence compared to false news (average 5.6 versus 527 Twitter retweets; 98 versus 452,050 Facebook engagements, p<0.001). Conclusions These findings reveal a growing interest in cannabis use as a cancer cure, and a crucial opportunity for physicians and medical organizations to communicate accurate information about the role of cannabis in cancer to patients, caregivers, and the general public.

Chronic retinal detachment with secondary retinal macrocyst and peripheral neovascularization.

PURPOSE: To report the association of a retinal macrocyst with peripheral retinal neovascularization that occurred secondary to a chronic, subclinical rhegmatogenous retinal detachment and to illustrate the utility of wide-field fundus photography. METHODS: Case Report. RESULTS: A 37 year-old male was diagnosed with chronic retinal detachment on routine eye examination. Further evaluation revealed intraretinal hemorrhages and a retinal macrocyst. Diagnosis was confirmed with wide-field fundus photography and fluorescein angiography. Surgery was recommended to repair the retinal detachment, to allow spontaneous resolution of the macrocyst. CONCLUSION: Chronic, subclinical retinal detachments may rarely be associated with retinal macrocysts and retinal neovascularization. This altered retinal morphology can be delineated on wide-field fundus imaging, which aids in diagnosis and management.