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Ethical Pathways: Key Considerations for AI in Eye Care

by | Aug 1, 2025 | Manuscripts | 0 comments

Varsha Singh , B.Optom.

M. Optom Student, The Sankara Nethralaya Academy, Chennai, India

 

The adoption of Artificial Intelligence (AI) in eye care can greatly improve diagnostic accuracy, treatment strategies, and patient outcomes. AI is transforming healthcare through advancements in patient care, diagnostics, and decision-making. (1) Technologies like Machine Learning (ML) and Natural Language Processing (NLP) are being utilised for personalised treatments and population health management. (2) However, the integration of AI into healthcare brings about significant ethical, privacy, and fairness concerns, requiring careful consideration of how these technologies are developed and deployed. (3,4) With the rise of AI in this sector, understanding its implications is crucial to ensure that it enhances healthcare outcomes without perpetuating biases, violating privacy, or compromising the integrity of medical practices. (5-7)

Key Consideration in AI in Healthcare

1. Data Privacy and Security: The use of AI in healthcare relies heavily on vast amounts of sensitive patient data, such as Electronic Health Records (EHRs), diagnostic images, and genetic information. This raises significant concerns regarding data privacy and security. Safeguarding patient information is crucial to prevent unauthorised access and data breaches, which could lead to identity theft or misuse of medical information. (1)

2. Bias and Fairness: A key ethical issue in AI development is the potential for bias in Machine Learning models. If AI algorithms are trained on biased data, they can dis-proportionately disadvantage minority groups and worsen healthcare disparities. To ensure fairness, AI models must be designed with inclusivity and diversity. (2,6)

3. Accountability and Transparency: Due to their “black-box” nature, AI systems in healthcare can make decisions that are hard to interpret. It is crucial to establish accountability and transparency to ensure trust, especially when AI decisions affect patient outcomes. Both patients and healthcare providers must understand how AI systems function and make decisions. (4,5)

4. Ethical Implications of AI Use in Diagnostics and Decision-Making: AI is increasingly used in diagnostic tools and decision-making, raising concerns about the role of human oversight in healthcare. While AI can assist healthcare professionals, there is a risk of over-reliance on these technologies, which may lead to missed diagnoses or errors in treatment. The ethical dilemma arises in balancing AI’s capabilities with the need for human involvement to ensure patient safety and well-being. (7,11)

5. Informed Consent and Patient Autonomy: As AI becomes more integrated into healthcare, patients must be informed about how their data will be used and how AI will affect their care. Ensuring informed consent before AI interventions is vital to uphold patient autonomy. Ethical concerns also arise in how the risks and benefits of AI are communicated to patients. (8)

6. Impact on Healthcare Professionals and Employment: The rise of AI in healthcare has sparked concerns about its potential to replace human workers, particularly in administrative and diagnostic roles. Ethical considerations focus on managing job displacement and ensuring AI is used to enhance, not replace, human capabilities. (10)

Figure 1: Use of AI in Healthcare

Conclusion

AI has great potential to transform healthcare by improving patient outcomes, efficiency, and diagnostic accuracy. However, its integration raises ethical concerns related to data privacy, bias, fairness, accountability, and patient autonomy. Addressing these issues through transparent governance, fair data use, and patient-centred care is crucial for ensuring AI benefits healthcare. With appropriate safeguards, AI can improve healthcare delivery while upholding ethical standards and equity. (1,2,6)

 

References

  1. Yadav, N., Pandey, S., Gupta, A., Dudani, P., Gupta, S., & Rangarajan, K. (2023). Data Privacy in healthcare: In the era of artificial intelligence. Indian Dermatology Online Journal, 14(6), 788–792.
  2. Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage health of populations. Science, 366(6464), 447-453
  3. Farhud, D. D., & Zokaei, S. (2021). Ethical issues of artificial intelligence in medicine and healthcare. Iranian Journal of Public Health, 50(11), i–v.
  4. Singh, B. P., & Joshi, A. (2024). Ethical considerations in AI development. In The Ethical Frontier of AI and Data Analysis (pp. 156–179).
  5. Keskinbora, K. H. (2019). Medical ethics considerations on artificial intelligence. Journal of Clinical Neuroscience: Official Journal of the Neurosurgical Society of Australasia, 64, 277–282.
  6. Sreerama, J., & Krishnamoorthy, G. (2022). Ethical considerations in AI addressing bias and fairness in machine learning models. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (Online), 1(1), 130–138.
  7. Hanna, M. G., Pantanowitz, L., Jackson, B., Palmer, O., Visweswaran, S., Pantanowitz, J., … Rashidi, H. H. (2024). Ethical and bias considerations in artificial intelligence/machine learning. Modern Pathology: An Official Journal of the United States and Canadian Academy of Pathology, Inc, 38(3), 100686.
  8. Wang, C., Liu, S., Yang, H., Guo, J., Wu, Y., & Liu, J. (2023). Ethical considerations of using ChatGPT in health care. Journal of Medical Internet Research, 25, e48009.
  9. Elendu, C., Amaechi, D. C., Elendu, T. C., Jingwa, K. A., Okoye, O. K., John Okah, M., … Alimi, H. A. (2023). Ethical implications of AI and robotics in healthcare: A review. Medicine, 102(50), e36671.
  10. Li, F., Ruijs, N., & Lu, Y. (2022). Ethics & AI: A systematic review on ethical concerns and related strategies for designing with AI in healthcare. AI (Basel, Switzerland), 4(1), 28–53.
  11. Guan, J. (2019). Artificial intelligence in healthcare and medicine: Promises, ethical challenges and governance. Chung-Kuo i Hsueh k’o Hsueh Tsa Chih [Chinese Medical Sciences Journal], 34(2), 76–83.

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