Dear Editor, Ophthalmology has experienced a rapid increase in the use of big data analytics, primarily due to the growing access to electronic health records, population-based screening initiatives, imaging databases, and genomic and biobank data.1 Furthermore, the availability of wearable and mobile eye health technologies enables the real-time collection of ocular and behavioral data outside of hospitals.2 These datasets open new avenues for comprehensive disease surveillance and trend analysis in various eye conditions and to evaluate the relationship between different sociodemographic and patient-related factors in heterogeneous settings.1,2 The integration of clinical and imaging data has also fostered the development of predictive analytics and machine learning algorithms for the early detection of eye diseases such as diabetic retinopathy and glaucoma.3 The objectives of the article are to identify the ethical concerns in big data in ophthalmology and ascertain the utility of community engagement and public trust in data health research. These large population-based datasets can be used to improve epidemiological mapping, identify risk factors, promote the evaluation of health service utilization, and help program managers identify inequalities in eye health outcomes.1,2,4 The analysis of available big data in ophthalmology is often performed using artificial intelligence systems.5 However, if these systems are fed with unrepresentative datasets, the results can be biased and exacerbate inequalities in the detection and treatment of various eye diseases.5 Moreover, there is an ethical obligation to ensure fair usage of data, as this will prevent underrepresentation of marginalized populations.4 Furthermore, it is critical that we strictly adhere to ethical standards when conducting large-scale data analysis (e.g., the right of each individual to decide on the use of their personal health data, the prevention of data misuse, the use of this data for the public good, etc.).1 Hence, traditional consents might not be sufficient in the current digital era; rather, we should give the option to subjects to change their preferences on how they want the data obtained from them would be used.6 In India, according to the Digital Personal Data Protection Act, 2023, definitive emphasis is given toward strengthening the consent framework in data-driven research.7 This act mandates that consent must be free, informed, specific, and unambiguous, with each person having the right to withdraw their consent at any stage.8 In other words, the concept of a dynamic consent mechanism is advocated, due to which participants can modify their data-sharing preferences over time, and this will not only improve trust but also make the entire process transparent.7,8 From the Indian perspective, the ethical conduct of big data research must be in adherence with the guidelines advocated by the Indian Council of Medical Research, which emphasize respect for autonomy, privacy, confidentiality, and accountability in the use of human data.9,10 Further, due emphasis is given to oversight by the institutional ethics committee, data anonymization, and justification for waiver of consent in secondary data research, as these large-scale datasets are regularly used for secondary analysis in big data ophthalmology.9,10 Community engagement and building public trust have emerged as critical for the success of data health research.11 In other words, researchers must obtain permission from the community members informally, which goes beyond legal compliance. This kind of permission is generally granted when communities develop trust that their data use aligns with their values, expectations, and will ultimately benefit the masses.11 It is vital that the investigator/s communicate about the purpose, involved potential risks, strategies involved in data collection and analysis, etc., to the community members in a transparent manner, and this will serve the dual purpose of accountability of researchers and building trust.12 At the same time, the team of researchers must listen to the concerns of participants empathetically and address them with compassion.13 This approach is bound to clear any myths and also creates an enabling environment where the community realizes their voices are being heard.13 In conclusion, big data in the field of ophthalmology has immense potential to transform the way eye healthcare services are delivered to the masses. However, in this digital era, we must adopt an approach to engage with community members and build public trust, as this can eventually aid in delivering better patient outcomes. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.
Shrivastava et al. (Fri,) studied this question.
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