A deep dive into market leaders LinusBio and EarliTec Diagnostics
Artificial intelligence isn’t just “showing up” in behavioral health—it’s beginning to rewire how we detect, diagnose, and deliver care. Three shifts are worth watching: Predictive diagnostics that surface risk earlier—sometimes before symptoms are obvious. Objective, tech-enabled assessments that reduce subjectivity and support ongoing progress monitoring. Digital therapeutics and virtual platforms that expand reach—especially in communities that have historically been underserved. Below are two companies illustrating how this is unfolding, plus what it could mean for clinicians, families, and payors.
Image Copyright: LinusBio
Predictive Diagnostics: LinusBio and the “exposome” in a strand of hair
What they do:
New York–based LinusBio (a Mount Sinai spinout) is building StrandDx, a lab-developed test designed to estimate a child’s likelihood of autism by analyzing molecular “fingerprints” left by environmental exposures (the exposome) captured along a single strand of hair. One centimeter corresponds to roughly a month of exposure history; using laser-based analysis, the platform reconstructs a time-series map of biomarkers to power AI models.
Why it matters:
- Earlier signal, earlier support. LinusBio’s thesis is simple: if we can quantify risk earlier, we can route families to evaluation and intervention sooner—where timing often shapes outcomes.
- Beyond behavior-only screening. Autism has largely been identified through behavioral observation. StrandDx aims to augment, not replace, that process with biology-informed risk insights.
- Momentum: The company received FDA Breakthrough Device designation shortly after launch and raised $16M to expand R&D and clinical validation.
Reality check:
- This is an aid, not a stand-alone diagnosis. Clinical validation and real-world utility data are essential before wide adoption.
- Ethical guardrails matter—especially around communication of risk, consent, and follow-up pathways to evaluation and services.
Image Copyright: EarliPoint / EarlyTec
Objective, Earlier Identification & Ongoing Monitoring: EarliTec’s eye-tracking
What they do:
EarliTec Diagnostics uses patented eye-tracking and visual attention biomarkers to provide objective, quantifiable insights related to early autism indicators and to support ongoing assessment during treatment. The platform is designed to be fast, standardized, and friendly for young children.
Why it matters:
- Earlier identification. Objective tasks can help flag risk in toddlers, supporting referral for comprehensive evaluation sooner.
- Measure what matters. For clinicians, repeatable, standardized signals can help monitor progress and tailor care plans.
- Leadership update: In August 2025, Jamie Pagliaro—a longtime operator in autism services and ed-tech—became President & CEO, signaling a push into scaling access and commercialization.
Context:
EarliTec joins other FDA-cleared digital tools aimed at earlier autism detection (e.g., AI-enabled apps, eye-tracking approaches). The shared theme: reduce friction, reduce subjectivity, and shorten the path from concern to care.
Digital Therapeutics & Virtual Care: Expanding the “last mile”
AI is also showing up in care delivery—triaging families to the right level of care, supporting parent-mediated interventions, and assisting with documentation and outcomes tracking. For rural and underserved areas, this can mean fewer waitlists and more tailored supports (asynchronous coaching, shorter sessions, or stepped-care models). The key is pairing tech with clinician oversight and clear outcome measures.wa
What this could mean—for clinicians, families, and payors
Clinicians
- Faster routing. If predictive/early-signal tools reduce time to evaluation, clinicians can prioritize the highest-risk cases sooner.
- Personalized planning. Objective measures (like attention biomarkers) can inform data-driven treatment plans and adjust intensity/dosage.
- Less friction. AI can cut documentation burden and surface insights from the noisy stream of session data.
Families
- Earlier answers, clearer steps. When concerns arise, families need fast, reliable pathways—not just screenings but actionable next steps.
- Progress you can see. Objective repeat measures can help families understand what’s changing over time.
Payors & Systems
- Right care, right time. Earlier identification plus tiered, data-informed care can reduce long waitlists and optimize intensity.
- Outcomes & oversight. Standardized measures help distinguish signal from noise and align reimbursement with value.
What to watch (and what to ask)
- Validation and equity. Do models perform similarly across demographics, languages, and settings? Are false positives/negatives acceptable and clearly communicated?
- Clinical workflow fit. Can tools be run in minutes, with clean handoffs to evaluation and services? Do they reduce burden rather than add it?
- Data governance. Who owns the data? How are results stored, shared, and explained? What happens after a “positive” screen?
- Real-world outcomes. Do these tools actually shrink time to diagnosis, improve access, and enhance outcomes—not just produce impressive AUCs?
Bottom line
AI won’t replace the clinician—but clinicians who leverage AI may outperform those who don’t. LinusBio is pushing on the earliest signal—turning hair into a high-resolution exposure timeline that may guide faster evaluation. EarliTec is standardizing what we measure—offering objective, repeatable indicators to support early identification and track progress.
The opportunity is compelling: earlier insights, more personalized care, and broader reach. The responsibility is clear: validate rigorously, implement ethically, and keep humans in the loop.
