Industry News
Strong Support for AI in Eye Care, But Trust and Infrastructure Gaps Remain
A recent study published in Eye examined how artificial intelligence (AI) is perceived within the ophthalmic community. The research aimed to assess awareness, acceptance, and concerns surrounding AI, with the goal of guiding its future integration into eye care.
Researchers conducted a systematic literature review, analyzing 16 relevant studies to evaluate AI acceptance among key stakeholders—patients with eye conditions, ophthalmologists, allied eye-care professionals, and the general public. Using the Unified Theory of Acceptance and Use of Technology (UTAUT) model, the team focused on four key factors: performance expectancy, effort expectancy, social influence, and facilitating conditions. They also considered how variables like age, gender, experience, and voluntariness of use might affect acceptance.
Key Findings
- Widespread Acceptance: Most groups showed strong support for ophthalmic AI, particularly due to its potential to improve diagnostic accuracy and streamline workflows.
- Top Drivers of Acceptance: Perceived usefulness and ease of use were the most frequently studied and influential factors.
- Less Attention to Social and Environmental Factors: Peer influence, institutional backing, infrastructure, and training were less commonly explored.
- Demographic Gaps: Little data exists on how age, gender, or previous exposure to AI affect acceptance.
Stakeholder Insights
- Ophthalmologists welcomed AI as a support tool for diagnosis and decision-making, assuming it remains reliable and interpretable.
- Optometrists and technicians appreciated its efficiency benefits but were concerned about changing roles and responsibilities.
- Patients and the public were generally open to AI, particularly when it complements rather than replaces human clinicians.
Major Concerns
- Data Privacy: Patients and professionals voiced strong concerns over how personal data is handled.
- Trust and Transparency: Many called for greater clarity in how AI systems make decisions.
- Regulatory Uncertainty: Key questions remain around liability, validation standards, and reimbursement policies.
- Financial Barriers: High costs of AI implementation, maintenance, and integration pose significant hurdles.
Recommendations
To support the successful adoption of AI in ophthalmology, the authors recommend:
- Expanding research, especially in diverse and underrepresented settings.
- Launching large-scale, controlled trials to evaluate AI’s clinical and financial impact.
- Enhancing clinician and patient education to build trust and understanding.
- Strengthening technical and regulatory infrastructure.
- Incorporating economic evaluations into planning and policy-making.
Conclusion
The researchers found that AI is generally well-received in the field of ophthalmology, mainly due to its performance and usability benefits. However, they caution that issues around trust, transparency, regulation, and cost must be addressed. The authors call for collaborative efforts, rigorous evidence, and strategic system design to ensure AI’s responsible and sustainable role in eye care.
