Jeff Elton, Ph.D., is CEO of ConcertAI, an AI SaaS solutions company providing research solutions and patient-centric solutions for all times sciences innovators and the world’s leading providers. ConcertAI is concentrated on accelerating and improving the precision of retrospective and prospective clinical studies using provider EMRs, LISs, and PACSs systems because the source for all study data. It’s a long-term partner partner with the American Society of Clinical Oncology and its CancerLinQ program, US FDA, NCI Health Equity initiatives, and almost 100 healthcare providers across the US.
Prior to ConcertAI, Jeff was Managing Director, Accenture Strategy/Patient Health; Global Chief Operating Officer and SVP Strategy at Novartis Institutes of BioMedical Research, Inc.; and partner at McKinsey & Company. He can also be a founding board member and senior advisor to several early-stage firms. Jeff is currently a board member of the Massachusetts Biotechnology Council. He’s the co-author of the widely cited book, Healthcare Disrupted (Wiley, 2016). Jeff has a Ph.D. and M.B.A. from The University of Chicago.
Because the founding CEO of ConcertAI, are you able to share your vision for the corporate at its inception? How has that vision evolved since 2018?
We began with the concept improved patient outcomes come from deep and actionable insights. Gaining those insights requires data completeness, data scale, data representativeness and advanced AI intelligence. So, we created a Data-as-a-Service and AI Software-as-a-Service company. We targeted AI that permits inferencing and prediction. This included predicting events to avoid, corresponding to patients’ non-adherence to their therapy or discontinuation of care due to a scarcity of positive response, which informed when clinical trials is perhaps the subsequent option.
Our vision has remained steadfast, and we proceed to expect more out of our solutions. With the most recent generation of LLMs, agentic AI and other generative AI solutions, we will operate at scale (and almost in real-time—something we didn’t expect or anticipate in 2018). With partners like NVIDIA, we will advance our solutions to perform higher than expected, recognize limitations and unique characteristics, and move on the pace of the complete market’s innovations—the journey up to now has been extraordinarily productive and exhilarating.
Now we have opened up previously unimaginable performance in clinical trial automation solutions, automating the location of patients on evidence-based clinical pathways, advanced workflows in radiological interpretation, and the usage of digital twins as a decision-enhancing tool for care and research.
Today, we serve almost 50 biopharma innovators and a couple of,000 healthcare providers—so while not at quite the dimensions of the complete market, we’re the broadest-reaching AI solutions for oncology within the industry.
What inspired you to deal with oncology and hematology datasets specifically, and the way did you see ConcertAI making a difference in these fields?
The USA began the “War on Cancer” in 1971 with the National Cancer Act. This catalyzed large-scale government funding, which generated insights into the mutations that drive cancers, latest modalities for therapies, expanded National Cancer Institute-designated treatment centers, and more. Under the Obama administration, funding increased again by $10 billion in electronic stimulus going to the NIH and, in turn, to the NCI. Under Biden, the Cancer Moonshot 2.0 program was launched in 2022, again catalyzing a completely latest generation of research and seed funding investment for tutorial, community, and private-public partnerships.
I give this history because few diseases or areas of healthcare have the extent of knowledge: genomic, transcriptomic, digital pathology, digital radiology, detailed electronic medical records, etc., and the extent of research that contextualizes these data with validated insights through rigorous, multi-center, peer-review studies. As further evidence, the American Society of Clinical Oncology annual meeting is the most important medical meeting on the planet, with the best number of latest publications, posters and abstracts of any scientific forum on any topic.
So, when you are going to be data and AI-centric, there are few higher areas to advance solutions with confidence and at scale than oncology. ConcertAI has the most important collection of research-grade data of anyone on the planet. It includes tons of of peer-reviewed publications enabled by that data, significant evidence resulting from those publications changing how patients are treated and assuring essentially the most positive possible outcomes, and now AI SaaS technologies which can be integral to the processes of care and research that bring the facility of that data and evidence to bear in any respect points and for all decisions along a patient’s care journey. What is basically vital here is that we don’t do that unilaterally. It is completed transparently with our healthcare provider and biopharma innovator partners to engender the best confidence and use. So, we’re evolving toward real-time, advanced, AI intelligence-enabled decision augmentation.
ConcertAI has grow to be a number one player in real-world evidence (RWE) and AI technology for healthcare. What were among the early challenges you faced in positioning the corporate as a frontrunner on this space?
You will have to be trusted and evolve towards being the reference source. That’s earned. The trust comes out of your provider partners, believing that the info you might be accessing is in the most effective interests of their patients. Trust comes out of your academic and industry partners, who see the evidence of and consider that your data is derived as an ideal reflection of the unique patient records and that the concepts you advance are ‘true’ and reflective of current clinical and scientific practice. You furthermore mght have to attain a scale that your data solutions represent not only the complete population but in addition produce conclusions which can be confidently generalizable back to the total population being treated with a specific medicine. Technology is analogous. Scientists and clinicians are inherently skeptical—as they needs to be—and don’t trust black boxes or algorithms they don’t understand. So we would have liked to ascertain trust there, too, through publications and being open about how our solutions work.
ConcertAI holds the world’s largest oncology and hematology dataset. What unique opportunities does this data create for transforming cancer research and treatment?
I like that query. We’re working on this daily! The opportunities to supply value to providers, patients and innovators are almost limitless. In early-phase trials, we’re evolving study simulation approaches with digital twins that can change the programs we take into clinical trials. Our data and AI optimizations will lower the time required to go from finalized protocol to finalized submission to regulators by 30 to 40%—meaning latest medicines get to patients faster. Our decision augmentation AI solutions will recommend pathways for treatment which can be evidence-based and specifically tailored to those pathways, monitor responses according to the expected response, and search for potentially helpful clinical trials when response or profit is below expectations. Our imaging clinical interpretation solutions operate at the extent of operational processes, clinical interpretation, and longer-term view of latest interpretations or latest interventions that needs to be considered based on insights and evidence in the longer term. Not is an motion “once and done” but somewhat it becomes “once, and however and again” such that helpful reassessments and future decisions are an ongoing process! What’s different here is that the view is the complete patient journey—it is a horizontal view versus a series of narrow, deep, vertical views that should be stitched together. That is an innovation enabled by AI and a profound process change that gives latest ways of working to the expert humans involved.
Are you able to explain how ConcertAI’s Digital Trial Solution works to match cancer patients with life-saving clinical trials? What impact have you ever seen up to now when it comes to patient outcomes?
Clinical trials are very complex and require hours of effort by a big selection of highly expert individuals. For many organizations, clinical trials are offered as a responsibility and commitment to patients where the present standard of care may not represent a viable alternative. Trials have probably not been very available to patients in community treatment centers, where 80% of patients receive their care. Yet, these are the patients who will ultimately be receiving newly approved medicines. This creates a double dilemma: the vast majority of patients who need access to trials are limited, and people who are reflective of the last word standard of care population will not be within the trial dataset. We set a path to resolve these problems.
The outcomes have been great—so positive that we’re going to be expanding our variety of studies underway by 10x in 2025. We published this for the last American Society of Clinical Oncology meetings and in other areas. Our approach is how we expect AI needs to be implemented—as an augmentation of expert humans where there are large capability and talent constraints and where lives are at stake. Now we have developed a set of orchestrated and tuned large language models that access patient records, synthesize characteristics, and match patients to potentially helpful trials, doing exactly what the expert humans would do—with a totally documented approach to creating recommendations and assessments. Within the sites where our technologies are deployed, we perform at the extent of essentially the most expert humans and accrue patients at 5x or more relative to sites where our technologies will not be deployed—the research teams and biopharma innovators are each pleased, and patients profit most.
How does ConcertAI’s AI-driven approach to trial design and patient recruitment address among the current limitations in clinical research, corresponding to patient diversity and trial efficiency?
I’m happy with my team—they told me three to 4 years ago that achieving diversity is an obligation and the best thing to do scientifically. Additionally they maintained that it is tough to do whether it is manual but requires zero incremental effort if automated. So, we decided then that each dataset and AI SaaS solution would integrate diversity and social determinants of health characteristics as our standard approach. It’s not an option. It’s just what we do. Subsequently, our CARAai™ supported clinical trial design and optimization solutions can assess what ethnic, racial or economic subpopulations could also be most adversely impacted by a disease, integrate those considerations into the trial design, make sure that these populations will not be unwittingly excluded, and define the clinical sites almost definitely to guarantee participation and representativeness. That is where AI could be “AI for Good” and where technology doesn’t introduce a bias but assures that biases don’t enter the method, the last word design, or the operational processes across the clinical trial.
What role does ConcertAI play in reducing the burden on healthcare providers and optimizing site selection in clinical trials?
We integrate the work burden into all features of our clinical trial solutions. First, there may be a burden on the patient. This could be where the positioning is situated, the variety of visits required for a study versus the usual of care, or the clinical intensity of a study versus the usual of care, as within the case of additional biopsies. These items can determine whether the patient—or the patient in consultation with their provider—can afford to participate or tolerate and complete participation.
There’s also a burden on the provider. If we will automate the identification of patients for clinical trial eligibility, minimize false positives that create work, and supply what we call “AI leverage” to the work of the Clinical Research Associated, Study Nurses, and Physicians, then the burden is lowered. The identical is true of our AI Automation Solution, which allows the research team to avoid doing manual data entry—typically 2 to 4 hours at the top of the day, and sometimes accomplished at home. Early on we checked out the info within the EMR—digital—being manually entered right into a portal for the sponsor’s EDC. So digital data is being read after which rekeyed to grow to be digital data again! Here, too, we’re using our multi-tuned large language models—this was an actual focus of the NVIDIA partnership from the start. We’re at 55% full automation today, with a really fast path to over 80% in the approaching few months. As these elements come together, we’ll get the staff time all the way down to 10% of legacy requirements and make these studies more accessible to more patients.
Precision medicine is a key area where AI is making significant strides. How does ConcertAI’s technology contribute to more precise and personalized cancer treatments?
We’ve not discussed this an excessive amount of since last yr. In December 2023, we assumed responsibility for the American Society of Clinical Oncology’s (ASCO) CancerLinQ program. It’s the world’s largest intelligent health network, comprising academic centers, regional hospital systems and community providers. A key a part of this network is implementing the ASCO Certified® quality and clinical pathway solutions. Since CancerLinQ is a ConcertAI initiative, we have now been growing the network, automating precision oncology pathways, creating latest digital twin approaches for augmenting treatment selection for the providers, identifying and messaging critical diagnostic tests that would inform treatment decisions, and doing the identical for newly approved medicines that represent one other or higher treatment alternative. All of that is underpinned by our CARAai™ architecture, again a set of vision LLM and tuned oncology LLMs done in collaboration with NVIDIA. It’s amazing to see the progress being made, and we’re enthusiastic about what we’ll be publishing and presenting at next yr’s ASCO 2025.
How do you see AI imaging solutions benefiting fields like oncology and radiology, especially as these fields face clinician shortages?
Great query! It’s true that each the number of latest oncologists and radiologists entering the sphere is lower than the number retiring. Nonetheless, patient demand is ever-increasing. So, it’s the perfect area for providing AI SaaS solutions that support physician and allied care professionals in each workflow optimization and clinical decision augmentation. Radiologists and oncologists will each cite the importance of those latest intelligent solutions coming into their fields specifically. Imaging is a superb area for AI, and its performance is outstanding. Non-inferiority studies reflect that AI models could be near or comparable to expert humans in narrow areas. Orchestrated workflows can bring this all together. The identical is true in oncology, where we’re bringing together molecular test results with immune response data, predictive algorithms for resistance and other elements that can all inform the treatment decision and enable response monitoring. I’ve been in the sphere for years and on different sides of latest innovations—what we will do now’s well beyond anything we were ever in a position to do before, and the pace of change is amazing.
As an experienced leader in healthcare technology, what advice would you offer to latest firms trying to make a meaningful impact in healthcare through AI?
You’ll be able to’t be an AI company without access to data at scale. Data is the substrate for constructing training and monitoring models. Also, constructing AI solutions is a team sport. You would like domain knowledge at an exceptional depth matched with a brand new generation of AI model development capabilities that recognizes the behaviors of various classes of AI solutions and might bring them to bear against narrow objectives, specifically tuned for human or above performance. Then, these approaches could be orchestrated in various ways to represent a brand new system for operating—that’s where the changes occur, and the worth gets delivered. Practice “AI Humility” as every part is amazing and exhibits things we couldn’t do even six months before. Yet, ‘amazing’ shouldn’t be necessarily a product or a brand new way of working—it’s just that, technology doing something latest. It’s the responsibility of the AI company to make it a brand new way of working and a brand new approach for delivering an astonishing level of value that was never accessible before. Finally, assume that you must reveal trust in business practices, AI models, and solution transparency. We’re still early in our societal journey, and we’re those who should earn the trust to bring concerning the changes we’re able to delivering.
