Work, Vision, and Risk: What AI Is Teaching Us

Occupational health has moved from a box‑ticking exercise to an essential management function. One area, however, still hides in plain sight: visual health. In screen‑centric workplaces with aging workforces and persistent chronic disease, vision problems accumulate quietly until they surface as quality issues, safety incidents, or avoidable absences. This article connects the broader logic of workplace risk with the specific role of visual health, and describes — without hype — how modern imaging and AI are being used in Mexico to detect problems early enough to change outcomes.

The Problem

A practical way to think about risk in a typical company is to layer three influences. The first is the intrinsic profile of the workforce — age distribution and the burden of diabetes, hypertension, and other cardiometabolic conditions that set a baseline. The second is workplace exposure — ergonomics, lighting, sustained screen time, and shift patterns that can strain vision even in healthy eyes. The third is a convergence zone where these forces reinforce each other. A pre‑diabetic employee under sustained stress will struggle to maintain glycemic control; a 40‑year‑old professional with uncorrected presbyopia working in low‑contrast environments will experience fatigue, headaches, and more frequent errors.

Visual health sits precisely at this intersection. Yet, it often goes unmeasured because impairment develops gradually and because many programs still equate “eye screening” with refraction — how well a person reads letters on a chart. That is useful but incomplete. The leading causes of irreversible vision loss in working‑age adults — diabetic or hypertensive retinopathy, glaucoma, macular and myopic degeneration — are structural diseases of the retina and optic nerve. They can be asymptomatic for long periods. By the time employees notice, damage may already be significant. Bringing structural assessment into occupational health widens the prevention window and links day‑to‑day performance with long‑term protection from avoidable disability.

Technology and Practice

Over the past few years, portable non‑mydriatic cameras have made it possible to capture high‑quality retinal images on site in minutes. AI systems can then analyze those images for patterns associated with retinal disease and flag cases that warrant follow‑up. What matters in practice is not the novelty of the algorithm but the fit with clinic and operations. In a well‑run program, an employee sits for imaging, receives a brief explanation in plain language, and the occupational physician gets an interpretable note that states urgency and a suggested pathway. The work can be scheduled alongside periodic exams, and the typical time per person remains short — under 10 minutes — so production schedules are not disrupted.

The roles are clear. AI supports screening and prioritization, diagnostic and treatment decisions remain with clinicians. Trust depends on consent, role‑based access, and traceability, and on being candid about limitations. A minority of images will be ungradable, some employees will require dilation or repeat imaging, and not every flagged case will convert into confirmed disease. These realities are manageable if performance is audited periodically, including subgroup analyses, and if referral networks are in place so that people who need care can actually get it. When programs close the loop — communication the same day, workable referrals, and simple follow‑up prompts — the benefits accrue on two fronts: slower progression to severe disease and better day‑to‑day function (fewer errors, less fatigue, steadier output).

What We’re Seeing and the Economics

Between September 2024 and September 2025, workplace campaigns in Mexico that used AI‑enabled retinal screening assessed 20,377 employees. The signal was consistent across sites: 43.8% had at least one clinically relevant finding (an average of 1.36 findings per person), and 12.7% had AI‑detected retinal findings compatible with retinopathies, glaucoma suspicion, macular changes or other retinal damage requiring follow‑up. In other words, visual health is not a marginal issue, it is a measurable component of workforce risk that often goes unseen until it is measured.

The financial picture points in the same direction. Using observed workplace prevalence and standard cost assumptions (an all‑in daily labor cost around MX$800 (US$43) and an average age near 40), a representative 100‑employee site shows savings on the order of MX$2.1 million against an on‑site campaign cost of roughly MX$31,300 — a return close to 67‑to‑1. The precise value will vary by sector and geography, but the mechanism is stable: early identification and timely referral prevent expensive outcomes and reduce productivity drag from unaddressed visual problems.

Conclusion

Occupational health strategies that ignore visual health leave value — and human well‑being — on the table. Adding structural retinal assessment with AI‑enabled triage to routine workplace exams allows companies to detect silent disease earlier, protect safety and productivity, and target budgets where they matter most. In our recent campaigns, about one in eight employees had a retinal finding detected by AI, and each assessment took under 10 minutes — a modest operational ask with outsized clinical and economic implications.