Dr. Abhishek Mandal, PhD

Scientific Advisor

An OCULAR Interface Exclusive

Keywords: Eyecare, Compassion, Artificial Intelligence (AI), Personalised treatment, Compassionate care, Early detection, AI in eyecare, Human-centered AI

AI’s Impact on Eyecare’s Compassion

Artificial Intelligence (AI) can enhance compassion and patient experience in eye care by personalising treatment plans, improving and making diagnoses more accurate, and minimising the workload of healthcare professionals, allowing them more time to interact and care for their patients.⁽¹⁾ Increased compassion and customer experience can be achieved by integrating AI technologies like machine learning and deep learning with human design principles.

The Importance of Compassion in Eyecare

Eyecare is a personal journey filled with emotional challenges and anxieties because of the fear of vision loss and eye-related conditions, hence, the need for a compassionate approach to diagnosis and treatment.⁽²⁾ Eyecare compassion relieves stress and anxiety, motivates patients to stick to their treatment plans, and improves the outcomes. Moreover, it helps patients deal with self-doubt and attachment traumas, as well as meeting the needs of individuals with a learning disability.^(3,4)

AI’s Role in Elevating Compassion

AI enables early screening and detection of eye conditions, improving the efficiency and accuracy of diagnosis and enhancing compassion.^(5,6) Early detection results in prompt intervention and treatment, improving patient outcomes and minimising their suffering. Moreover, AI can make diagnostic predictions that enhance clinical settings and reduce the burden on eyecare providers.⁽⁷⁾ AI can also enable eyecare providers to personalise patient interactions, allowing them to use a specific empathetic approach. Furthermore, machine learning algorithms can analyse patient data, enabling eyecare providers to resonate better with each patient by adapting to their tone, language, and delivery.

AI’s virtual assistants and chatbots also enhance eyecare compassion and promote emotional well-being by providing personalised and accessible support on preventive measures, home remedies, and interactive counseling.⁽⁸⁾ Yaghy et al⁽⁹⁾ emphasised that integrating communication, compassion, and competence into AI applications enhances patient care. Moreover, AI is integral in helping visually impaired individuals by providing personalised support for eyecare needs through intelligent voice recognition and text-to-speech capabilities.⁽¹⁰⁾

Additionally, a human-centered approach to AI can enhance eyecare compassion by including empathy, intelligence, and consciousness and involving patients, eye care professionals, and caregivers in designing AI systems.^(11,12) However, regulation, professional education, and training must be made to ensure AI enhances instead of replacing the human elements of eye care. Furthermore, eyecare providers and patients have raised data privacy and security concerns surrounding the use of AI in eyecare, which can be addressed by implementing data protection methods, including federated learning and blockchain technology.⁽¹³⁾

AI enhances Eyecare’s compassion and patient experience by ensuring the AI solutions in the industry incorporate human-centered design principles to boost the patient journey, create a deeper connection between patients and providers, relieve anxieties, and deliver compassionate care. The future of eyecare relies on balancing artificial intelligence and eyecare compassion to ensure every individual receives quality care.

References

1. Kerasidou A. (2020). Artificial intelligence and the ongoing need for empathy, compassion and trust in healthcare. Bulletin of the World Health Organization, 98(4), 245–250. https://doi.org/10.2471/BLT.19.237198
2. Dodge-Palomba S. (2008). Providing compassionate care to the pediatric patient undergoing enucleation of the eye. Insight (American Society of Ophthalmic Registered Nurses), 33(1), 10–12.
3. Kennedy, A. (2014). Compassion-Focused EMDR. Journal of EMDR Practice and Research, pp. 8, 135–146.
4. De Smit, E., Bunce, C., Duggan, R., & O’Sullivan, E. (2013). Importance of eye care in meeting the needs of patients with learning disabilities. BMJ (Clinical research ed.), p. 346, f4044. https://doi.org/10.1136/bmj.f4044
5. Akkara, J. D., & Kuriakose, A. (2019). Role of artificial intelligence and machine learning in ophthalmology. Kerala Journal of Ophthalmology, 31(2), 150-160.
6. Dutt, S., Sivaraman, A., Savoy, F., & Rajalakshmi, R. (2020). Insights into the growing popularity of artificial intelligence in ophthalmology. Indian journal of ophthalmology, 68(7), 1339–1346. https://doi.org/10.4103/ijo.IJO_1754_19
7. Keskinbora, K. H. (2023). Current roles of artificial intelligence in ophthalmology. Exploration of Medicine, 4(6), 1048-1067.
8. Wahal, A., Aggarwal, M., & Poongodi, T. (2022, April). Iot based chatbots using nlp and svm algorithms. In 2022 3rd International Conference on Intelligent Engineering and Management (ICIEM)(pp. 484–489). IEEE.
9. Yaghy, A., Shields, J. A., & Shields, C. L. (2019). Representing Communication, Compassion, and Competence in the Era of AI. AMA journal of ethics, 21(11), E1009–E1013. https://doi.org/10.1001/amajethics.2019.1009
10. Raghavan, R., Krishnan, V., Nishad, H., & Shaikh, B. (2021). Virtual ai assistant for person with partial vision impairment. In ITM Web of Conferences(Vol. 37, p. 01019). EDP Sciences.
11. Banerjee, S. (2020). A framework for designing compassionate and ethical artificial intelligence and artificial consciousness. Interdisciplinary Description of Complex Systems: INDECS, 18(2-A), pp. 85–95.
12. Chen, Y., Clayton, E. W., Novak, L. L., Anders, S., & Malin, B. (2023). Human-Centered Design to Address Biases in Artificial Intelligence. Journal of medical Internet research, p. 25, e43251. https://doi.org/10.2196/43251
13. Lim, J. S., Hong, M., Lam, W. S. T., Zhang, Z., Teo, Z. L., Liu, Y., Ng, W. Y., Foo, L. L., & Ting, D. S. W. (2022). Novel technical and privacy-preserving technology for artificial intelligence in ophthalmology. Current opinion in ophthalmology, 33(3), 174–187. https://doi.org/10.1097/ICU.0000000000000846