Expanding the Perspective: Eye Tracking in Ophthalmology and Neurology

Eye tracking technology is widely used in psychology, human factors, and usability research, but its role in ophthalmology and neurology is equally promising. In reading ‘The Fundamentals of Eye Tracking Part 3: How to Choose an Eye Tracker ¹, we were inspired by its insights and saw an opportunity to extend the discussion into medical and rehabilitation applications, particularly for ophthalmic and neurological diseases.

We wanted to explore key considerations for selecting the right eye tracker for clinical use. Inspired by the original article, we expand on their set of personas by introducing Dr. Ray, who is an early-career researcher looking for a device to use for developing a novel visual field assessment. His considerations illustrate the challenges and decisions involved in integrating eye tracking into clinical practice. Before diving into this, we first explain why eye trackers are important in ophthalmology, neurology, and vision rehabilitation.

The Clinical Case for Eye Tracking

Many traditional clinical vision tests rely on subjective patient responses, which can be inconsistent and are often qualitative rather than quantitative, such as simple observational methods like tracking a clinician’s finger with the eyes. Assessing visual function often requires manual input, making it challenging for individuals with motor impairments or cognitive decline ^2–4. These tests also demand extensive patient participation and are not always adaptable for individuals with limited mobility, intellectual disabilities, or communication difficulties ^5–8.

Eye tracking offers several potential advantages. Eye tracking automates data collection and analysis, making assessments more efficient and standardized ⁹. Unlike traditional diagnostic approaches that depend on lengthy procedures or manual input, eye tracking enables rapid assessments with minimal effort ^{3,5,7,10–12}. This is beneficial in busy clinical settings, where streamlined workflows and efficient evaluations contribute to improved patient throughput.

Choosing the Right Eye Tracker for Clinical Use

While eye tracking holds great potential for clinical research and patient care, choosing the right eye tracker is just as crucial as recognizing its benefits. Different clinical applications demand varying levels of precision, usability, and adaptability. The effectiveness of an eye tracker depends not only on its technical specifications but also on how well it integrates into clinical workflows and meets regulatory requirements. Consider Dr. Ray, who wants to develop a device that utilizes eye tracking for visual field assessments. Visual field assessments are a standard part of ophthalmic evaluations, performed routinely in clinical practice to assess and monitor vision loss. However, standard visual field tests rely on subjective patient responses, which can be inconsistent and challenging for certain populations ^6,12. His approach, which relies on tracking gaze responses instead of manual inputs, potentially allows for greater precision and efficiency in detecting areas of vision loss, making the testing process more inclusive and accessible to a wider range of patients. What considerations should he be aware of when choosing an eye tracker for this task?

Ease of Use – Unlike in a controlled lab, clinical settings require rapid, robust, and intuitive setups. Calibration should be quick (preferably instantaneous), robust, and adaptable to diverse patient groups. Compared to most tabletop models, many mobile eye trackers, in particular, offer flexibility¹³ and ease of use while maintaining sufficient precision for many clinical applications. Systems that integrate well into existing workflows without requiring extensive training of clinical staff are particularly valuable. VR systems with integrated eye tracking present another compelling option, providing a controlled, immersive testing environment while eliminating the need for external display setups ¹⁴.

Reliability for Diagnostic Use – Data accuracy and precision must be sufficient for clinical decision-making, as even minor discrepancies can affect diagnosis and treatment. While stationary systems usually offer high spatial precision, mobile systems allow for more naturalistic assessments with a different set of advantages ^3,6,15.

Robustness to Eye and Head Movements – Many patients struggle with fixation stability, and an ideal eye tracker should accommodate these variations. Mobile systems provide more tolerance to movement, making them well-suited for dynamic clinical environments ¹³. Additionally, systems that employ advanced gaze-tracking algorithms to compensate for head and involuntary eye movement may enhance accuracy in certain patient populations ^15,16.

Industry Collaboration and Regulatory Compliance – A crucial factor in the adoption of eye-tracking technology in clinical settings is the willingness of companies to cooperate with necessary modifications and compliance requirements. Ensuring that eye trackers meet clinical regulations, such as CE marking and FDA approval, is a cumbersome, lengthy and expensive process but essential for real-world medical applications. ¹⁷ Additionally, supporting diverse patient populations, including those with mobility or cognitive challenges, requires manufacturers to develop inclusive and adaptable solutions that align with healthcare standards.

Who can be assessed? – Most off-the-shelf eye trackers are designed for adults. However, children are not simply smaller adults; their eye morphology differs, with smaller facial structures, different interpupillary distances, and evolving ocular anatomy, all of which can affect calibration and tracking accuracy. Additionally, their behavior during testing—such as shorter attention spans, increased movement, and difficulty maintaining fixation—poses further challenges ^18–20. Ethnic variations in orbital structure, interpupillary distance, and eyelid anatomy can also impact eye tracking performance, requiring careful validation across diverse populations ²¹. These issues are particularly notable in the design of mobile eye tracker frames. To meet real-world medical needs, manufacturers must collaborate closely with clinicians and researchers to refine their technology accordingly.

Data analysis and reporting – most researchers are used to either writing their own tools while making use of general purpose programming languages such as Matlab, R, or Python. But in clinical situations, such software may not be suitable. Clinical applications require software that is robust, user-friendly, and capable of generating clear, interpretable reports suitable for medical decision-making. Additionally, conformity to existing standards and interoperability with electronic health record (EHR) systems are essential for seamless integration into healthcare environments.

Ultimately, the choice of an eye tracker should align with its intended purpose. High-precision systems may be necessary for research applications requiring fine spatial resolution, whereas mobile systems provide a balance of usability and adaptability for clinical environments. In more specialized settings, such as fMRI studies, eye tracking can play a crucial role in understanding neural activity related to vision and cognition. However, using eye tracking in an fMRI environment introduces a host of additional requirements, including MR-compatibility, minimized interference with imaging, and the ability to function within the constraints of a confined space. These factors significantly narrow the range of suitable devices and often come with a considerably higher price tag.

The Future of Eye Tracking in Ophthalmology and Neurology

Beyond its role in diagnosis and monitoring, eye tracking is being explored for its potential in rehabilitation and disease management. Research suggests that eye movements can help individuals with visual impairments adapt to their condition, regain functional independence, and optimize gaze strategies for daily activities ^3,12,22,23. Emerging applications include:

Rehabilitation Training – Patients with stroke-induced visual field deficits can use eye-tracking-based training programs to compensate for lost visual function. By guiding patients to improve their scanning behavior, these programs enhance mobility and reading ability. ^22,23

Predictive Analytics for Disease Progression – Eye tracking is being integrated into longitudinal studies to monitor the progression of ophthalmic and neurodegenerative diseases. These insights can inform treatment adjustments and help researchers develop more personalized interventions ^{3,5,7,11,12,14}.

Assistive Technology for Low Vision Patients – Devices using eye tracking are being incorporated into wearable technologies that allow patients with severe visual impairments to interact not only with digital interfaces through gaze-based control but also with their real-world surroundings. Such advancements include smart navigation systems that provide real-time feedback on obstacles and pathways, enabling greater mobility and independence. Eye tracking is also being integrated into assistive tools that help patients with low vision and blind individuals enhance their interaction with objects, signs, and people in everyday environments, as well as in prosthetic visual implants, where gaze-based interaction can optimize visual perception and navigation. ^24–26

Looking Back: How Eye Tracking Options Have Evolved

The number of options to choose from has increased dramatically over the past years. When author FWC started out with doing eye tracking with clinical applications in mind (^27–29), nearly all of the above issues applied, yet much fewer options to choose from were available. The system that turned out to be most robust, participant-friendly, and accurate following an on-site demonstration by the manufacturer was chosen. Now, given all the new options, that same system would most likely be considered less attractive for the same purpose.

Figure 1. Author FWC testing a participant with an at-the-time-state-of-the-art 250 Hz head-mounted video-based eye tracker in 2009. This was the first eye-tracking system he acquired, marking the beginning of preclinical eye-tracking research in his lab. (Photo by Reyer Boxem, reyerboxem.nl (with permission).

As eye tracking technology advances and integrates more seamlessly into clinical practice, its role in patient care and rehabilitation continues to grow, moving beyond traditionally specialized applications that require expert operation. No longer confined to research labs, eye tracking is now a valuable tool in clinics, hospitals, and rehabilitation centers, supporting both diagnostic and therapeutic applications. Its expanding use underscores the importance of selecting the right device for specific medical tasks, ensuring compliance with regulatory standards, and fostering innovation through industry collaboration. As more clinicians adopt eye tracking in their practice, its full potential will continue to unfold, shaping the future of ophthalmology, neurology, and assistive technology.

Inspired by Nyström et al.¹, we extend this discussion—exploring how eye tracking is not just a research tool but an increasingly practical option for clinical and rehabilitation settings. Beyond improving diagnostic accuracy and accessibility ^{3,6,10–12,18}, it is becoming integral to monitoring and treatment strategies. By analyzing abnormal eye movements or tracking how patients visually explore their environment, clinicians can refine early detection, track disease progression, and tailor treatments more effectively ^15,22,24,30. The ability to observe real-time gaze behavior also allows for individualized rehabilitation programs, optimizing interventions for a range of visual impairments ^15,23,31. As a result, eye tracking is no longer just a tool for early diagnosis but a key component of long-term disease management.

Featured Psychonomic Society article

Nyström M, Hooge ITC, Hessels RS, et al. The fundamentals of eye tracking part 3: How to choose an eye tracker. Behav Res Methods. 2025;57(2):67. doi:10.3758/s13428-024-02587-x

Acknowledgements

Author DT was funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 955590 (OptiVisT), and a grant from the Graduate School for Medical Sciences (GSMS).

Authors

Dilce Tanriverdi

Dilce Tanriverdi is a postdoc at the Laboratory of Experimental Ophthalmology, University Medical Center Groningen, Netherlands. Her research focuses on visual crowding and the development of functional vision assessment tools using eye movements and psychophysics to improve the evaluation of visual deficits.
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Frans W. Cornelissen

Frans W. Cornelissen is a professor of Visual Neuroscience and Ophthalmology at the University Medical Center Groningen, the Netherlands. His research interest is in understanding the behavioural, neurological and computational basis of functional vision in health and disease, thereby contributing to improving vision rehabilitation. He has been using eye tracking in his research for over 30 years. Amongst others, he’s the first author of the Eyelink Toolbox.
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