Maryam Moharib AI in Eye Care Six Tools Influencing Clinical Practice- 2026

Artificial intelligence (AI) has been widely discussed in recent years, but its impact on Canadian eye care is now tangible. From personalized dry eye management to automated retinal screening and advanced OCT biomarker analysis, AI-driven platforms are helping clinics improve efficiency, standardize documentation, and strengthen diagnostic support.

Not all AI tools serve the same function. Some operate as screening and triage systems, others deliver quantitative imaging analytics, and some provide broader clinical decision-support resources.

Below is an overview of six AI-enabled tools currently shaping clinical conversations in Canadian eye care.

 

  1. CSI Dry Eye Software – Standardizing Dry Eye Care

Dry eye disease remains one of the most common conditions encountered in primary eye care. CSI Dry Eye Software is a cloud-based clinical management platform designed to structure and streamline dry eye assessment and management.

Using AI-driven algorithms, the platform synthesizes patient questionnaires, diagnostic testing, and clinical findings. It identifies likely contributing mechanisms—such as evaporative or aqueous-deficient components—and generates structured, individualized treatment plans.

Key capabilities include:

  • Analysis of multiple dry eye tests, patient questionnaires, and clinical data to identify underlying causes and contributing factors.
  • Support for personalized, protocol-driven treatment plans by comparing patient data against databases of OTC products, prescription therapies, and in-office procedures.
  • Longitudinal tracking of patient progress across multiple visits.
  • Cloud-based access (SaaS) with structured dashboards, automated reminders, customized reporting, and follow-up tools to streamline workflow.
  • Reduced chair time and decreased subjective variability through standardized analysis and treatment guidance.

Integration considerations: CSI operates as a browser-based SaaS platform and does not require proprietary hardware. It works with most dry eye data already collected in clinic. EMR integration varies by system; summaries can typically be exported or incorporated into records.

Best suited for: Clinics seeking structured, protocol-driven dry eye management.

 

  1. OphtAI – Automated Retinal Screening

OphtAI is designed for automated analysis of colour fundus photography. It screens for diabetic retinopathy, glaucoma, and age-related macular degeneration (AMD), generating real-time reports with lesion mapping, severity grading, and confidence scoring.

Key features include:

  • Detection of major retinal diseases from fundus photographs.
  • Real-time analysis with downloadable PDF reports detailing lesion mapping and severity grading.
  • Flexible access via web portal, mobile application, or API.
  • Designed for rapid, automated screening in tele-ophthalmology or population-based programs.

Integration considerations: The platform is accessible via web, mobile, or API. In Canada, it is also currently integrated with Tecksoft EyeVu EMR. It does not require proprietary imaging hardware beyond a compatible fundus camera.

Best suited for: Primary care environments and large-scale screening programs.

 

  1. Altris AI – OCT Biomarker Quantification

Altris AI focuses on AI-enhanced interpretation of OCT scans. The platform detects and quantifies more than 70 retinal pathologies and biomarkers, including drusen subtypes, fluid, atrophy, and edema. It is particularly positioned for monitoring AMD, in both dry and neovascular forms.

Core features include:

  • Automated detection and quantification of 70+ retinal biomarkers and pathologies.
  • Highlighting and longitudinal tracking of AMD-related features to support monitoring of progression and treatment response.
  • Conversion of raw OCT data into structured, reproducible outputs, including heatmaps, Early Treatment Diabetic Retinopathy Study (ETDRS) grid visualizations, graphs, and quantitative measurements.
  • Support for research workflows through longitudinal comparisons and advanced analytics.

Integration considerations: Altris AI offers cloud-based access as standard, with on-premise options available. It is vendor-neutral and designed to integrate into existing OCT workflows, compatible with devices from multiple major manufacturers.

Best suited for: Retina-focused practices and imaging centres requiring detailed OCT analytics.

 

  1. AI4Eyes – Ocular Surface Test Consolidation

AI4Eyes is a Canadian-developed platform that combines proprietary hardware with AI-driven diagnostic software. The tool consolidates approximately 10 anterior segment tests into a streamlined, AI-guided imaging workflow. Clinicians receive AI-supported diagnostic suggestions and treatment recommendations for review.

Notable capabilities:

  • Consolidation of multiple anterior segment tests into a single AI-guided workflow.
  • Machine learning–based analysis of ocular surface parameters.
  • Personalized treatment suggestions that clinicians can validate and refine.

Integration considerations: AI4Eyes integrates into the pre-test workflow and supplements existing diagnostic data collection. Unlike pure SaaS platforms, it requires acquisition of dedicated hardware.

Best suited for: Practices investing in integrated anterior segment and dry eye diagnostics.

 

  1. VisualDx – Broad Clinical Decision Support

VisualDx is a clinical decision-support platform rather than a dedicated eyecare AI tool. It contains a database of more than 45,000 medical images and peer-reviewed clinical content.

Clinicians can input signs and symptoms to generate differential diagnoses—particularly valuable when ocular findings intersect with dermatologic or systemic disease.

Core benefits include:

  • Searchable content extending beyond ophthalmology, supporting multi-system evaluation.
  • Access to treatment guidance, ICD coding support, and complication alerts.
  • Patient education tools to facilitate communication and shared decision-making.

Integration considerations: VisualDx is web-based and accessible via browser or app, with API integration options available.

Best suited for: Clinicians managing complex or multi-system presentations.

 

  1. OCTolyzer – Open-Source OCT Research

OCTolyzer occupies a distinct niche as an open-source OCT analysis toolkit developed for research use. In Canada, it may be used in research environments but is not intended for direct patient diagnosis.

The platform performs automated retinal and choroidal segmentation and extracts quantitative metrics such as thickness measurements and vascular indices.

Integration considerations: OCTolyzer runs on standard computing systems and is platform-agnostic. It is not a regulated clinical diagnostic device and is intended for academic research, validation studies, and AI development.

Best suited for: Universities, research laboratories, and AI development teams.

 

In Summary

AI adoption in Canadian eye care continues to accelerate, with tools serving diverse clinical objectives:

  • CSI Dry Eye Software and AI4Eyes focus on structured ocular surface management.
  • OphtAI supports retinal screening.
  • Altris AI enhances OCT biomarker analysis.
  • VisualDx strengthens cross-disciplinary differential diagnosis.
  • OCTolyzer advances academic research and innovation.

Selecting the appropriate platform depends on clinical focus, practice size, patient volume, regulatory considerations, and readiness to integrate new technologies into established workflows. As AI continues to evolve, its role in Canadian optometry is shifting from experimental to operational—reshaping how optometrists assess, document, and manage ocular disease.

 

 

Maryam Moharib

Maryam Moharib, BOptom, BHSc, CSPO, CAPM

Maryam holds degrees in Health Sciences from the University of Ottawa and in Optometry from Anglia Ruskin University in Cambridge, England. She has dedicated many years to working alongside ophthalmologists in refractive surgical clinics, where she gained significant experience in clinical training and in EMR implementation for various software platforms.

Maryam has also worked as a certified product owner with an EMR software company where she played a key role in effectively bridging the gap between clinical needs and technology. Additionally, her certification in project management from the Project Management Institute has equipped her with the skills to lead implementation and transformative clinic projects successfully.


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Diana Monea article AI and EMR Integrated into an Optometric Practice

Optometry has evolved from paper files to digital records, from phone calls to virtual communication, and in the future, perhaps even to robots (bots) assisting humans. Every day, innovation takes on a new dimension as artificial intelligence (AI) is integrated into electronic medical record (EMR) systems. The future of communication and diagnostics in eye care remains uncertain: will AI replace parts of what we do, or simply help us work more efficiently, more accurately, and at lower cost while improving the patient experience?

  1. AI Reduces Office Time, Improves Documentation, and Enhances the Patient Experience

AI can capture the doctor–patient conversation and generate clinical notes automatically. Unlike basic dictation tools, more advanced systems can organize information into the appropriate sections such as chief complaint, history, exam findings, assessment, and plan.

AI can also support tasks such as:

  • Billing and coding support
  • Documenting diagnostic impressions and recommended next steps

Used well, these tools can improve the patient–doctor experience. The clinician can stay more present with the patient, while reducing clerical workload and documentation interruptions. This can also help reduce clinician burnout by making charting more seamless and efficient at the point of care, reducing the need for after-hours documentation.

2. Integrating AI and EMR

When AI outputs can be structured and integrated into an optometry EMR, the system can help identify patterns and automate repetitive, but necessary, tasks. Depending on the platform and workflow, AI-enabled EMR integration may support:

  • Prescriptions and documentation support
  • Referrals and communication templates
  • Treatment plan tracking and management
  • Patient education materials based on clinical findings
  • Interpretation support and clinical prompts
  • Revenue cycle support
  • Analytics and reporting

With thoughtful integration, the EMR can begin to function less like a passive record and more like an active clinical assistant.

3. AI Imaging as Clinical Support

OCT, fundus photography, corneal topography, and visual field data can be analyzed using AI models trained on large datasets. Applications may include:

  • Diabetic retinopathy screening
  • Glaucoma risk and progression monitoring
  • AMD risk stratification and progression tracking
  • Corneal irregularity detection
  • Automated comparison of serial scans

AI-powered support tools may also combine imaging with clinical measurements such as intraocular pressure, pachymetry, and refraction. These tools are not a replacement for clinical judgment, but they can add a layer of insight and help flag subtle changes that are easy to miss in busy practice.

4. AI’s Impact on Practice Management

Administrative inefficiencies are a major source of stress and cost in many practices. AI-enabled systems can assist with:

  • Insurance claim submission, verification, and follow-up
  • Coding support, rejection handling, and resubmissions
  • Appointment scheduling, reminders, and recall workflows

By reducing errors and repetitive front-desk work, AI can free staff to focus on higher-value patient service and practice operations; helping reduce administrative burden and improve consistency.

5. Patient Communication

AI can improve patient understanding and satisfaction by:

  • Providing personalized summaries of findings
  • Explaining recommendations and outcomes in plain language
  • Generating visuals or simplified graphics that help patients understand their results
  • Supporting appointment booking and capturing patient concerns ahead of visits
  • Providing relevant pre-visit or post-visit information for recommended procedures

Practice bonus: when patients are better informed, they are often more confident and more compliant, leading to smoother visits, fewer misunderstandings, and a stronger overall experience for patients, staff, and clinicians.

6. Standardization Through AI and EMR Connectivity

A major advantage of AI is its potential to integrate with EMR systems more seamlessly than older workflows. Historically, many EMRs operated in silos, requiring manual entry and increasing the risk of transcription errors.

With tighter device-to-EMR integration, key data, such as refractions, visual acuities, intraocular pressure measurements, and imaging results, can populate charts automatically. This reduces redundancy, improves accuracy, and supports standardization across the clinical team.

7. The Advantage of an AI-Enhanced Practice

When documentation, scheduling, coding, and routine analysis are partially automated, optometrists gain time to do what matters most: be human. That means having more capacity to educate, reassure, and build trust without sacrificing the operational demands of modern practice.

The future of optometry will be shaped by clinicians who use AI strategically to enhance care, reduce burnout, and elevate the patient experience. Practices that adopt AI, and integrate it effectively into their EMR, will be better positioned to improve efficiency, strengthen resilience, and pursue clinical excellence in the face of tomorrow’s practice challenges.

 

 

Dr. Diana Mae Monea, OD, FAAO, MHRM

Dr. Diana M. Monea, OD

Dr. Diana M. Monea is an award-winning optometrist, author, and keynote speaker with more than four decades of leadership in clinical practice, business ownership, and professional education. Founder and former CEO of Eye Health Centres, she now focuses on consulting, mentorship, patient care, and public speaking.


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