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Through the AI Lens: Ethical Clarity in Ophthalmology’s Future

by | Jul 1, 2025 | Manuscripts | 0 comments

Aakanksha Pathania, B Optom.

Editorial Assistant and Tutor, Vision Science Academy,Delhi,India

 

Artificial Intelligence (AI) is making waves in the world of medicine and healthcare, and ophthalmology is leading the charge. This field relies heavily on imaging, which produces massive datasets that are perfect for training AI algorithms. The potential uses for AI are vast, ranging from disease screening and diagnosis to monitoring and even managing healthcare systems. However, bringing AI into medicine is not without its challenges, especially when it comes to ethical considerations that need to be tackled to ensure it’s used safely and effectively.(1)

One of the biggest ethical issues revolves around the principle of “do no harm.” This core idea is the foundation of all medical AI guidelines and standards regarding responsibility and liability. The origins of AI ethics can be traced back to Isaac Asimov’s robotic laws from the 1950s, particularly the first law that states robots must not harm humans, whether through action or inaction.(2) These principles continue to shape modern bioethical frameworks that guide AI development in healthcare today.

In the realm of healthcare, the integration of artificial intelligence (AI) systems calls for a meticulous evaluation of associated risks, a thorough assessment of the benefits against potential drawbacks, and an unwavering commitment to prioritising patient safety above all else. Achieving this delicate balance requires not only the harnessing of advanced machine learning capabilities but also an adherence to ethical principles that guide the responsible use of technology in patient care.(3)

One of the significant challenges facing AI in healthcare is its tendency to function as a “black box.” This terminology refers to the complexity of AI algorithms, which often operate in ways that are not easily interpretable by humans.(2,3) As a result, the decision-making processes employed by these systems can be opaque, creating challenges for healthcare professionals who need to understand how conclusions are reached. This lack of transparency can undermine confidence in AI tools and hinder their acceptance in clinical environments, where trust between practitioners and technology is essential for effective patient treatment and outcomes.(4)

Figure 1: AI in Optometry: Ethical Workflow from Data to Diagnosis
(Self Created by Author)

Furthermore, ensuring that AI systems are designed with a clear framework for accountability and transparency is crucial. This includes establishing guidelines for explainability, where AI-generated recommendations or decisions can be understood and justified. By addressing these challenges, the healthcare industry can not only harness the full potential of AI technologies but also foster an environment where both patients and practitioners feel secure and informed in their use of such innovations. (4,5)

To tackle this challenge, researchers are pushing for the creation of AI systems that provide clear and interpretable outputs. This could involve natural language explanations, quantifying uncertainty, and offering context-aware insights that fit into a clinician’s decision-making process. Additionally, interactive interfaces that let clinicians control the level of detail in explanations can enhance collaboration between humans and AI. By promoting transparency and understanding, these advancements can boost both trust and effectiveness in clinical environments. (5)

Conclusion:

In practical applications, a significant challenge lies in ensuring that AI models sustain their performance as time progresses. Various factors can influence the effectiveness of these models, including shifts in demographic trends, such as aging populations or changes in population health metrics, as well as advancements and modifications in medical practices that can alter treatment protocols. These evolving conditions necessitate a proactive approach to continuously monitor, adapt, and refine AI models. (3,4)

This may involve regularly updating the training data to reflect current realities, retraining algorithms to address new variables, and validating the outputs against contemporary clinical standards to ensure that the models remain relevant and accurate in their predictions and recommendations. Ignoring these factors could lead to a decline in performance, ultimately undermining the reliability of AI in supporting clinical decision-making and patient care. (3-5)

 

References

  1. Abdullah, Y. I., Schuman, J. S., Shabsigh, R., Caplan, A., & Al-Aswad, L. A. (2021). Ethics of artificial intelligence in medicine and ophthalmology. The Asia-Pacific Journal of Ophthalmology, 10(3), 289-298.
  2. Wallach, W., & Asaro, P. (Eds.). (2020). Machine ethics and robot ethics. Routledge.
  3. Chen, S., & Bai, W. (2025). Artificial intelligence technology in ophthalmology public health: current applications and future directions. Frontiers in Cell and Developmental Biology, 13, 1576465. https://doi.org/10.3389/fcell.2025.1576465
  4. Evans, N. G., Wenner, D. M., Cohen, I. G., Purves, D., Chiang, M. F., Ting, D. S. W., & Lee, A. Y. (2022). Emerging Ethical Considerations for the Use of Artificial Intelligence in Ophthalmology. Ophthalmology science, 2(2), 100141. https://doi.org/10.1016/j.xops.2022.100141
  5. Gichoya, J. W., et al. (2022). AI recognition of patient race in medical imaging. The Lancet Digital Health, 4(7), e406–e414. DOI
  6. Morley, J., et al. (2020). Ethics of AI in health care: A mapping review. Social Science & Medicine, 260, 113172. DOI

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