In the high-stakes world of oncology, the distance between a breakthrough discovery in a laboratory and the bedside of a patient can often be measured in years. For decades, the American Society of Clinical Oncology (ASCO) sought to bridge this chasm through CancerLinQ, a pioneering platform designed to aggregate electronic health record (EHR) data from practices nationwide. What began as a tool to turn routine patient encounters into a “learning system” has now entered its most ambitious phase yet, following its December 2023 acquisition by ConcertAI.
By layering advanced artificial intelligence, agentic workflows, and real-world data synthesis over the existing CancerLinQ framework, ConcertAI is not merely updating software—it is fundamentally redefining the role of intelligence at the point of care.
The Foundation: From Quality Reporting to Clinical Intelligence
When ASCO first launched CancerLinQ more than a decade ago, its primary utility was operational. It provided oncology practices with the digital infrastructure necessary to automate the measurement of quality standards required for ASCO certification. While vital for administrative compliance, the platform was largely reactive.
“Before we bought it, CancerLinQ largely just delivered software that automated the measurement of ASCO certification quality measures, in order to get your ASCO certification,” explains Eron Kelly, CEO of ConcertAI.
Following the 2023 acquisition, the mandate shifted. ConcertAI recognized that the vast, dormant reservoirs of EHR data within CancerLinQ represented an untapped goldmine for clinical decision support. The company began a rapid transformation, integrating trial-matching capabilities, sophisticated research datasets, and, most importantly, AI-driven insights that inject themselves directly into the physician’s live workflow.
Closing the Care-Research Gap: A Chronology of Innovation
The integration of ConcertAI’s technology into CancerLinQ marks a significant pivot in how oncology practices interact with data. The chronology of this transformation highlights a move from static data storage to dynamic, actionable intelligence:
- Pre-2023: CancerLinQ functions as a compliance and quality-reporting tool for oncology practices, primarily serving the needs of administrators and regulatory bodies.
- December 2023: ConcertAI completes its acquisition of CancerLinQ, signaling a move toward integrating the platform into a broader AI-focused ecosystem.
- 2024-2025: ConcertAI begins the deployment of "chain-of-thought" AI models. These agents are trained to parse unstructured clinical notes, pathology reports, and genomic data to identify longitudinal patient journeys.
- 2026 (Present): The platform is now fully operational as an "efficiency layer." It provides real-time "nudges" to clinicians regarding care gaps, missing molecular testing, and highly specific clinical trial matches, effectively embedding clinical research into the daily standard of care.
The "Lane Assist" Model: AI as a Clinical Co-Pilot
Perhaps the most compelling aspect of this transformation is how ConcertAI conceptualizes the physician-AI relationship. Dr. Shaalan Beg, ConcertAI’s chief medical officer for oncology, utilizes a "self-driving" analogy to describe the platform’s impact on modern practice.
“For most of the clinical care doctors are asking for right now, it’s more like lane assist,” Dr. Beg says. “It’s about keeping the car in its lane and giving nudges along the way.”
These nudges are not trivial; they address the chronic capacity crisis facing modern oncology. In an era where a single patient’s care team may include an oncologist, a nurse practitioner, a geneticist, a nutritionist, and a physical therapist, the cognitive load on the primary physician has become unsustainable. By automating routine documentation and alerting clinicians to critical gaps—such as a missing genomic test for a colon cancer patient—the platform restores precious minutes to the physician, allowing them to focus on high-level decision-making.
Furthermore, the trial-matching capabilities are a force multiplier. Traditional trial screening is a manual, labor-intensive process. ConcertAI’s system compresses this into a fraction of the time, automatically walking through 20 to 30 eligibility criteria against a patient’s record and providing a clear rationale for every determination. This capability is especially transformative for satellite clinics in hub-and-spoke networks, which may lack the research staff typically found in major academic centers.
Data Integrity and the "Chain of Models"
The efficacy of these tools hinges entirely on trust. If a clinician does not trust the AI’s recommendation, the tool becomes a liability. To ensure accuracy, ConcertAI employs a "chain of models" architecture.
"We use a chain of AI models and agents to parse all that information, understand really challenging concepts like progression, or timelines within a diagnosis journey, and summarize that into a structured data model that can be queried from our SaaS software stack," Kelly explains.
The system relies on a layered validation process. One set of agents assesses "abstraction accuracy"—ensuring the model is correctly interpreting the source material. A second layer evaluates "clinical coherence"—checking the sequence of events to ensure, for example, that a treatment plan follows a logical medical progression. This internal cross-checking ensures that the AI’s outputs are not just technically correct, but clinically sound.
The result is a system that can, for instance, re-evaluate historical pathology reports to identify patients who were previously classified as "HER2-negative" but would, under current guidelines and new antibody-drug conjugate therapies, be considered candidates for life-extending treatment.
Supporting Data: The Surge in Physician Adoption
The shift toward AI in oncology is not merely a product of vendor marketing; it is a reflection of a broader, rapid adoption of technology by the medical community. According to 2026 data from the American Medical Association (AMA), physician engagement with AI has skyrocketed.
- Adoption Rates: In 2023, 38% of physicians reported incorporating AI into their practice. By 2026, that number climbed to 72%.
- Awareness: Physician awareness of AI capabilities increased from 66% in 2025 to 81% in 2026.
- Functional Preference: The most popular use cases remain documentation and summarization. Nearly 40% of physicians now use AI tools to summarize research and standards of care, a 26-point increase since 2024.
This data underscores a vital shift in the culture of medicine: the transition from viewing AI as a "black box" to viewing it as a necessary instrument for managing the overwhelming complexity of modern data-driven oncology.
Implications: The "Ground Shot" Philosophy
While the healthcare industry has long been fixated on the concept of a "moonshot" cure, Dr. Beg advocates for what he calls "ground shots."
"I think we should focus on ground shots first, disseminating the treatments we already know work to the people who need them right now," Dr. Beg asserts.
This philosophy is the cornerstone of the new CancerLinQ. By leveraging real-world data to identify eligible patients for clinical trials and ensuring that the latest standards of care are accessible regardless of the facility’s size, ConcertAI is aiming to solve the "last mile" problem of medical research.
The implications for the industry are profound. As ConcertAI continues to scale its platform, the boundary between clinical care and clinical research will likely continue to blur. If successful, the result will not just be a more efficient oncology practice, but a more equitable one, where the latest scientific advancements—whether they are groundbreaking immunotherapies or refined biomarker testing—are seamlessly delivered to the patients who need them most.
In the final analysis, ConcertAI has successfully turned a repository of retrospective data into a forward-looking engine for patient survival. By empowering the clinician rather than replacing them, the platform provides a roadmap for the future of specialized medicine: one where the burden of data is managed by machines, leaving the art of healing to the doctors.
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