Industry Insights
Healthcare AI Has Crossed the Line From Experiment to Infrastructure
February 17, 2026
Healthcare AI adoption is no longer theoretical. It’s operational.
For years, artificial intelligence in healthcare lived in pilot programs, conference keynotes, and white papers promising future transformation. That phase is ending. What we are witnessing now is something fundamentally different: AI moving from optional innovation to embedded infrastructure inside everyday clinical workflows.
Few examples illustrate this shift more clearly than OpenEvidence.
Often described as “ChatGPT for clinicians,” OpenEvidence recently announced a $250 million Series D funding round at a $12 billion valuation, doubling its valuation in just two months. That headline is impressive, but the valuation is not the real story.
Adoption is.
According to reported metrics, OpenEvidence is now used by approximately 40% of U.S. physicians, supporting more than 18 million physician consultations per month, up from roughly 3 million a year ago. In 2025 alone, over 100 million Americans were treated by a clinician using the platform, with annualized revenue approaching $150 million and gross margins exceeding 90%.
Healthcare has never moved this fast.
When close to half of practicing physicians adopt a new workflow in fewer than three years, it signals that AI has moved beyond early adoption and into the realm of standard clinical practice. Once that shift takes hold upstream, expectations inevitably change downstream, including across laboratory operations.
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Why Physician AI Adoption Matters to Laboratories
At first glance, OpenEvidence may appear to be a physician-facing tool with limited relevance to laboratories. That would be a mistake.
Physicians are rapidly normalizing AI as part of daily clinical decision-making, not as a novelty, not as an experiment, but as infrastructure. Once clinicians internalize that AI can deliver fast, reliable, evidence-based answers at the point of care, they begin to expect the same level of intelligence, speed, and automation from all laboratory software systems they interact with.
That expectation inevitably flows to diagnostic services.
For clinical laboratories, reference labs, and pathology practices, this shift has profound implications:
- Faster answers become non-negotiable
- Manual review and data latency become unacceptable
- Fragmented systems become visible liabilities
- Legacy LIS systems alone are no longer sufficient
In an AI-native clinical environment, laboratories are no longer judged solely on analytical accuracy. They are judged on how effectively they integrate into intelligent, automated clinical workflows.
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OpenEvidence’s Model Signals Where Healthcare is Headed
OpenEvidence’s growth is not accidental. It reflects a deliberate, bottom-up adoption strategy that resonates with how modern healthcare professionals work.
Rather than selling into hospital IT departments through long procurement cycles, OpenEvidence built a doctor-first platform. Clinicians sign up directly. The platform is free. There is no enterprise gatekeeping. No implementation delays. No workflow disruption.
As the company’s CEO, Daniel Nadler, emphasized recently in MedCity News, OpenEvidence is designed for doctors, not hospital CIOs. It meets clinicians where they are: during rounds, between shifts, late at night reviewing cases, and provides answers sourced from trusted, peer-reviewed medical literature, including JAMA, the American Medical Association, and the New England Journal of Medicine.
That trust matters.
Physicians are savvy consumers of technology. They know when a system is built to serve them. OpenEvidence’s daily engagement, on average, one or more questions per clinician per day, reflects that trust and relevance.
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What This Means for Clinical Laboratories
As physicians grow accustomed to AI-assisted reasoning, laboratories must ask a critical question:
Are our diagnostic lab software systems ready to operate at the same speed and level as AI-driven clinical workflows?
In many environments, the answer today is no.
AI Expectations Will Reshape Laboratory Information Systems
The legacy laboratory information system (LIS) was designed as a system of record, a place to store results, manage accessioning, and support compliance. That foundation remains important, but it is no longer sufficient.
Modern LIS software must evolve into a system of action, capable of:
- Real-time decision support
- Intelligent routing and prioritization
- Automated quality assurance
- AI-assisted utilization management
- Closed-loop laboratory billing and documentation workflows
When a physician consults OpenEvidence and immediately refines a diagnostic plan, they expect laboratory workflows to keep pace, not slow them down.
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OpenEvidence Use Cases in Clinical and Reference Laboratories
While OpenEvidence itself is not a laboratory information system, its presence in clinical workflows creates clear opportunities for integration and operational alignment.
1. Test Selection and Utilization Guidance
Physicians frequently consult OpenEvidence when determining which tests are appropriate for a given clinical scenario. This creates an opportunity for laboratories to:
- Align LIS software decision rules with emerging evidence
- Reduce unnecessary or duplicative testing
- Support evidence-based utilization management
- Improve test appropriateness at the order-entry stage
Clinical laboratories that integrate AI-informed utilization logic into accessioning workflows will reduce downstream rework, improve turnaround times, and enhance clinician satisfaction.
2. Faster Clarification and Reduced Back-and-Forth
Medical lab technicians and client services teams spend significant time answering questions about test interpretation, reflex testing, and the context of the test result.
As clinicians become accustomed to AI-assisted answers, laboratories can mirror this experience by embedding AI-driven knowledge layers directly into diagnostic lab software, reducing phone calls, emails, and manual clarifications.
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Pathology Practices and Anatomic Pathology Software
Pathology stands to benefit even more directly from this AI-driven shift.
Pathologists already operate in a cognitively dense environment, synthesizing clinical history, imaging, histology, and molecular data. Tools like OpenEvidence normalize the idea that AI can augment, not replace, expert judgment.
AI-Aligned Pathology Lab Management
For pathology practices, this signals a future where anatomic pathology software must support:
- AI-assisted case triage and prioritization
- Intelligent workload balancing
- Context-aware clinical history aggregation
- Automated quality and concordance checks
- Faster, more consistent reporting workflows
As pathologists increasingly rely on AI-supported clinical insights upstream, LIS systems and other pathology lab management platforms must deliver the same level of intelligence internally.
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The Role of Medical Lab Technicians in an AI-Enabled Lab
AI adoption does not eliminate the need for skilled professionals; it elevates them.
Medical lab technicians will increasingly work alongside AI-driven tools that:
- Flag anomalies earlier
- Reduce manual verification steps
- Surface potential quality issues proactively
- Automate repetitive administrative tasks
This allows technicians to focus on higher-value work: exception handling, quality oversight, and process optimization.
Laboratories that embrace AI within diagnostic lab software will not only improve efficiency but also improve workforce satisfaction and retention, critical advantages in an environment of persistent staffing shortages.
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Lab Revenue Cycle Management and Billing Implications
One of the most overlooked aspects of healthcare AI adoption is its impact on lab billing and reimbursement.
As OpenEvidence normalizes AI-assisted clinical reasoning, documentation becomes more precise, structured, and evidence-based. Laboratories that integrate AI into LIS software and laboratory revenue cycle management (lab RCM) workflows can:
- Improve medical necessity documentation
- Reduce denials
- Accelerate clean claims
- Align test ordering with payer expectations
AI-aware clinical laboratory management is more than an operational advantage; it is a financial one.
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From Early Adoption to Table Stakes
Healthcare adoption has historically been slow. This is not slow.
When 40% of physicians adopt a new workflow in under three years, the message is clear: AI is becoming embedded infrastructure.
For laboratories, the question is no longer whether AI becomes table stakes.
The real question is how quickly you operationalize it before your customers expect it by default.
Intelligence Embedded Across the Entire Lab Operations Lifecycle
Artificial intelligence is rapidly reshaping healthcare, but pathology and clinical laboratories don’t need more disconnected AI point solutions layered on top of already complex workflows. What they need is intelligence built directly into the systems that run the lab.
That’s where LigoLab is different.
LigoLab isn’t building AI on top of pathology and clinical workflows.
We’re building a lab information system that orchestrates intelligence across them.
As laboratories move toward AI-driven operations, the LigoLab Informatics Platform is designed to serve as a true LIS system of action, aligning clinical, operational, and financial data in a single source of truth. This foundation allows intelligence to be applied consistently across the entire lab lifecycle, from order intake and accessioning to case management, lab billing, analytics, and reporting.
What “AI-Enabled” Means at LigoLab
At LigoLab, intelligence is not a bolt-on feature or experimental add-on. Every AI-driven capability is:
- Workflow-native, not layered on after the fact
- Auditable, explainable, and compliant by design
- Built to augment human expertise, not replace it
- Optimized across departments, not isolated in silos
Rather than introducing more tools to manage, LigoLab embeds intelligence directly into the workflows lab teams already use, supporting pathologists, technologists, administrators, and lab billing teams in real time.
Intelligence That Drives Measurable Outcomes
Every AI capability introduced into the LigoLab platform must deliver tangible operational or financial impact. If it doesn’t move a measurable needle, it doesn’t ship.
Specifically, intelligence must improve at least one of the following:
- Faster turnaround times
- Higher accuracy and fewer errors
- Greater labor efficiency
- Stronger revenue integrity
- Increased clinical confidence
By combining rule-driven automation, intelligent workflow orchestration, and AI-ready data architectures, LigoLab enables laboratories to adopt emerging AI tools with confidence, without disrupting operations or fragmenting data.
Contact us at LigoLab to learn how an all-in-one, AI-ready LIS system and lab billing platform can help future-proof your laboratory.
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