How Connected Diagnostics are Changing the Eye Exam

Written by: Dr Paul Watson
Medically reviewed by: Dr Han Simpson
Published: September 11, 2026
Medical review date: September 11, 2026
Evidence searched through: September 11, 2026
Next review due: September 11, 2027
Intended audience: General Readers & Patients
Geographic scope: Worldwide
Conflicts of interest: None declared.
Funding: Website Owner
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Medical disclaimer: Read the medical disclaimer

An eye exam is becoming less like a sequence of isolated measurements and more like a small clinical data pipeline.

A digital camera may capture the retina. Optical coherence tomography (OCT) can generate cross-sectional images of ocular structures. Biometry systems collect measurements used in surgical planning. Other instruments produce refraction, keratometry, pressure, or visual-function data. The significant change is not simply that these devices are digital. It is that their outputs can increasingly move into imaging systems, electronic records, analytical software, and other parts of the clinical workflow.

Jonathan M. Frantz, MD, FACS, from Frantz EyeCare, discusses cataract and refractive care and shows how each eye doctor in Lehigh Acres may work with digital imaging and biometry as part of a broader diagnostic environment [1].

That shift from instrument to connected workflow is where the eye exam starts to look like an Internet of Medical Things problem.

The exam room is becoming a connected data environment

A traditional mental model of an eye exam is fairly linear: take a measurement, look at the result, move to the next test.

Modern diagnostic environments can be more complicated. Different devices may collect different types of information, while software has to associate those results with the correct patient, preserve image quality and metadata, and make the information available where clinicians need it.

Ophthalmology already has a substantial standard foundation for this. The DICOM standard includes dedicated storage objects for ophthalmic photography, ophthalmic tomography, OCT analysis, autorefraction, keratometry, axial measurements, and even intraocular-lens calculations [2].

That matters because connectivity and interoperability are not the same thing.

A device can be attached to a network yet still require staff to export a PDF, type a measurement into another application, or open a proprietary viewer on a separate workstation. The device is connected, but the workflow remains fragmented.

A more integrated environment can look something like this:

diagnostic instrument → structured image or measurement → imaging or clinical platform → patient record → clinician review.

Some workflows add cloud storage, analytics, remote access, or surgical-planning software along the way. The exact architecture varies, but the operational goal is similar: keep clinical data attached to the right patient and usable beyond the machine that originally generated it.

Connectivity creates another responsibility as well. NIST notes that the IoMT environment involves interoperability across devices, reliance on cloud storage and software platforms, network communications, and cybersecurity considerations [3]. A diagnostic instrument that once behaved mostly like standalone equipment becomes part of the healthcare organization’s broader security boundary when it begins exchanging information with other systems.

For an IoT team, that means an ophthalmic instrument should not be evaluated only by sensor resolution or acquisition speed. Identity, access, integration, monitoring, software maintenance, and data portability can become part of the purchasing decision.

Digital imaging becomes more useful when data can be followed over time

Ophthalmology is particularly well suited to data-rich diagnostics because so much of the eye can be imaged and measured.

OCT is a good example. The technology generates cross-sectional images rather than a simple yes-or-no test result. In advanced implementations, imaging can produce volumetric information, segmentation data, and multiple analytical outputs. DICOM includes specific objects for storing ophthalmic tomography and OCT volume analysis, reflecting how structured these datasets can become [2].

A single scan can become more useful when it can be compared reliably with earlier studies.

It may be today’s scan compared with an earlier scan, a measurement associated with a treatment decision, or a series of images that allows a clinician to assess whether something appears stable or has changed.

For IoT teams, image acquisition is only the first part of the data problem.

Once high-resolution imaging becomes routine, practices need ways to organize, retrieve, compare, and present the resulting information without turning the clinician into a file manager. A powerful instrument loses some of its practical advantage if staff spend their time searching for the previous study or manually moving results between systems.

Artificial intelligence adds another layer. National Eye Institute researchers have demonstrated an AI method that dramatically accelerated processing in an advanced form of retinal imaging based on adaptive-optics OCT. The work was research rather than a claim that every clinical OCT system behaves this way, but it illustrates an important architectural point: analysis can increasingly become part of the imaging pipeline itself rather than something performed only after acquisition [4].

A connected workflow has to look beyond image quality. Where does the image go? Can earlier studies be retrieved reliably? Which software can process it? Can clinicians review the original data behind an automated result?

At that point, image management becomes an IoT platform problem as much as an imaging problem.

Cloud-connected records can make diagnostic data easier to follow over time

Getting information out of the device does not guarantee that it will follow the patient cleanly.

In January 2026, the Office of the National Coordinator for Health Information Technology requested input on standards and certification for diagnostic imaging interoperability [5]. The agency noted ongoing challenges in accessing, exchanging, and using diagnostic images across healthcare settings.

Eye care has many of the same architectural pressures.

A patient may have routine examinations, disease-specific imaging, surgical measurements, postoperative testing, and years of follow-up. If each system holds a separate piece of that history, longitudinal care depends on people rebuilding the context manually.

Connected records can reduce that fragmentation when integrations are designed well. Images do not necessarily need to live inside the electronic health record itself. A record might instead maintain a reliable link or reference to an imaging repository, while the clinician sees the report, measurements, and relevant history through the normal workflow.

Standards matter here because ophthalmic data is more diverse than a folder of JPEGs. Fundus photographs, OCT volumes, keratometry measurements, refraction data, axial measurements, and calculated values have different structures and clinical purposes [2].

Cloud connectivity can make storage and access more flexible, but it does not solve weak integration automatically. Moving a silo from a local server to the cloud still leaves a silo.

The stronger architecture preserves patient identity, provenance, timestamps, measurement context, and access controls while making the information available to the people and applications that legitimately need it.

This is also why cybersecurity cannot be added at the end. In its 2025 guidance for telehealth smart-home environments, NIST includes controls such as access management, authentication, continuous monitoring, data security, and network segmentation [6].

Connected diagnostic systems extend the security boundary beyond the individual instrument.

Automation helps most when clinicians remain in the loop

Once images and measurements are stored in machine-readable systems, automation becomes an obvious next step.

Software can organize information, calculate measurements, highlight areas for review, compare datasets, or generate analytical outputs. The FDA maintains a public list of AI-enabled medical devices authorized for marketing in the United States [7].

The harder question is how that automation fits into clinical work.

FDA transparency principles for machine-learning-enabled medical devices explicitlyemphasize the performance of the human-AI team, not merely model performance in isolation. The principles highlight intended use, inputs and outputs, workflow integration, limitations, performance, and the effect a device’s output is intended to have on professional judgment [8].

That distinction is particularly important for image-based systems.

The FDA’s current clinical decision support guidance treats software that analyzes medical images differently from simple software that merely presents existing medical information. It also places importance on whether healthcare professionals can independently understand and review the basis of recommendations in situations where the non-device CDS criteria apply [9].

For eye-care workflows, automation is therefore more useful when it reduces cognitive or operational friction without hiding clinically important context.

A clinician may benefit from software that makes an older scan easy to retrieve, aligns serial measurements, identifies an image requiring closer attention, or presents relevant trends. But the workflow still needs access to the underlying clinical information, appropriate quality controls, and a clear understanding of what the software was designed to do.

The practice provides a concrete example of device-to-device integration. Its published information describes pre- and postoperative testing with imaging and biometry platforms and an integrated cataract workflow in which VERION image-guided planning data can feed into other systems used during surgery, alongside ORA intraoperative aberrometry [1].

The useful integration is the handoff of information from preoperative measurement into planning and intraoperative guidance [1].

That is likely to be the more consequential change in diagnostic eye care. Better sensors will continue to matter, while AI-based image analysis remains an active area of development. But much of the practical value will depend on the less glamorous layer between them: standards, secure connectivity, longitudinal records, usable interfaces, and workflows that deliver the right information to a clinician at the point where a decision has to be made.

References

[1] Facts about Frantz EyeCare. (n.d.). Better Vision.

[2] Digital Imaging and Communications in Medicine. (n.d.). DICOM standard: Standard SOP classes and ophthalmic imaging objects.

[3] National Institute of Standards and Technology. (2020). Securing internet-connected medical devices.

[4] National Eye Institute. (Apr 10, 2024). AI makes retinal imaging 100 times faster, compared to manual method.

[5] Office of the National Coordinator for Health Information Technology. (Jan 29, 2026). Request for information: Diagnostic imaging interoperability standards and certification.

[6] National Institute of Standards and Technology. (2025). Mitigating cybersecurity and privacy risks in telehealth smart home integration.

[7] U.S. Food and Drug Administration. (n.d.). Artificial intelligence-enabled medical devices.

[8] U.S. Food and Drug Administration. (n.d.). Transparency for machine learning-enabled medical devices: Guiding principles.

[9] U.S. Food and Drug Administration. (2026, January). Clinical decision support software: Guidance for industry and Food and Drug Administration staff.