Jay Ferro is the Chief Information, Technology and Product Officer at Clario, he has over 25 years of experience leading Information Technology and Product teams, with a robust concentrate on data protection and a passion for creating technologies and products that make a meaningful impact.
Before joining Clario, Jay held senior leadership roles, including CIO, CTO, and CPO, at global organizations comparable to the Quikrete Corporations and the American Cancer Society. He can also be a member of the Board of Directors at Allata, LLC. His skilled accomplishments have been recognized multiple times, including awards from Atlanta Technology Professionals as Executive Leader of the 12 months and HMG Strategy as Mid-Cap CIO of the 12 months.
Clario is a pacesetter in clinical trial management, offering comprehensive endpoint technologies to remodel lives through reliable and precise evidence generation. Specializing in oncology trials, Clario emphasizes patient-reported outcomes (PROs) to reinforce efficacy, ensure safety, and improve quality of life, advocating for electronic PROs as a cheaper alternative to paper. With expertise spanning therapeutic areas and global regulatory compliance, Clario supports decentralized, hybrid, and site-based trials in over 100 countries, leveraging advanced technologies like artificial intelligence and connected devices. Their solutions streamline trial processes, ensuring compliance and retention through integrated support and training for patients and sponsors alike.
Clario has integrated over 30 AI models across various stages of clinical trials. Could you provide examples of how these models enhance specific facets of trials, comparable to oncology or cardiology?
We use our AI models to deliver speed, quality, precision and privacy to our customers in greater than 800 clinical trials. I’m proud that our tools aren’t just a part of the AI hype cycle – they’re delivering real value to our customers in those trials.
Today, our AI models largely fall into 4 categories: data privacy, quality control assistance, read assistance and skim evaluation. For instance, we have now tools in medical imaging that may routinely redact Personally Identifiable Information (PII) in static images, videos or PDFs. We also employ AI tools that deliver data with rapid quality assessments on the time of upload — so there’s a number of confidence in that data. We’ve developed a tool that monitors ECG data constantly for signal quality, and one other that confirms correct patient identifiers. We’ve developed a read-assist tool that allows slice prediction, lesion propagation and disease detection. Moreover, we’ve improved read evaluation by automating and standardizing data interpretation with tools like AI-supported quantitative ulcerative colitis Mayo scoring.
Those are only a couple of examples of the sorts of AI models we’ve been developing since 2018, and while we’ve made a number of progress, we’re just getting began.
How does Clario be sure that AI-driven insights maintain high accuracy and consistency across diverse trial environments?
We’re continually training our AI models on vast amounts of knowledge to know the difference between good data and data that will not be good or relevant. Consequently, our AI-driven data evaluation detects, pre-analyzes wealthy data histories, and ultimately results in higher quality results for our customers.
Our spirometry solutions nicely illustrate why we try this. Clinicians use spirometry to assist diagnose and monitor certain lung conditions by measuring how much air a patient can breathe out in a single forced breath. There are a selection of errors that may occur when a patient uses a spirometer. They may perform the test too slowly, cough during testing, or not have the opportunity to make a whole seal across the spirometer’s mouthpiece. Any of those variabilities could cause an error which may not be discovered until a human can analyze the outcomes. We’ve trained deep learning models on greater than 50,000 examples to learn the difference between a very good reading and a foul reading. With our devices and algorithms, clinicians can see the worth of the info in near real-time moderately than having to attend for human evaluation. That matters partly because some patients might need to drive several hours to take part in a clinical trial. Imagine driving that distance home from the positioning only to learn you’re going to must take one other spirometry test the next week because the primary one showed an error. Our AI models are delivering accurate overreads while the patient continues to be at the positioning. If there’s an error, it will possibly be rectified on the spot. It’s just one in all the ways we’re working to scale back the burden on sites and patients.
Could you elaborate on how Clario’s AI models reduce data collection times without compromising data quality?
Generating the very best quality data for clinical trials is all the time our focus, but the character of our AI algorithms means the capture and evaluation is sped up dramatically. As I discussed, our algorithms allow us to conduct quality control evaluation faster and at a better level of precision than human interpretation. Additionally they allow us to conduct quality checks as data are entered. Meaning we will discover missing, erroneous or poor-quality patient data while the patient continues to be on the trial site, moderately than letting them know days or even weeks later.
How does Clario address the challenges of decentralized and hybrid trials, especially by way of data privacy, patient engagement, and data quality?
Nowadays, a decentralized trial is basically only a trial with a hybrid component. I believe the concept of letting participants use their very own devices or connected devices at home really opens the door to greater possibilities in trials, especially by way of accessibility. Making trials easier to take part in is a key focus of our technology roadmap, which goals to develop solutions that improve patient diversity, streamline recruitment and retention, increase convenience for participants, and expand opportunities for more inclusive clinical trials. We provide at-home spirometry, home blood pressure, eCOA, and other solutions that deliver the identical data integrity as more traditional solutions, and we do it in concert with oversight from our endpoint and therapeutic area experts. The result’s a greater patient experience for higher endpoint data.
What unique benefits does Clario’s AI-driven approach offer to scale back trial timelines and costs for pharmaceutical, biotech, and medical device firms?
We’ve been developing AI tools since 2018, they usually’ve permeated every part we’re doing internally and positively across our product mix. And what has never left us is ensuring that we’re doing it in a responsible way: keeping humans within the loop, partnering with regulators, partnering with our customers, and including our legal, privacy, and science teams to be certain that we’re doing every part the best way.
Responsibly developing and deploying AI should affect our customers in quite a lot of positive ways. The muse of our AI program is built on what we consider to be the industry’s first Responsible Use Principles. Anyone at Clario who touches AI follows those five principles. Amongst them, we take every measure to make sure we’re using probably the most diverse data available to coach our algorithms. We monitor and test to detect and mitigate risks, and we only use anonymized data to coach models and algorithms. Once we apply those sorts of guidelines when developing a brand new AI tool, we’re in a position to rapidly deliver precise data – at scale – that reduces bias, increases diversity and protects patient privacy. The faster we will get sponsors accurate data, the more impact it has on their bottom line and, ultimately, patient outcomes.
AI models can sometimes reflect biases inherent in the info. What measures does Clario take to make sure fair and unbiased data evaluation in trials?
We all know bias occurs when the training data set is simply too limited for its intended use. Initially, the info set might sound sufficient, but when the tip user starts using the tool and pushes the AI beyond what it was trained to reply to, it will possibly result in errors. Clario’s Chief Medical Officer, Dr. Todd Rudo, sometimes uses this instance: We will train a model to find out proper lead placement in electrocardiograms (ECGs) so clinicians can tell if technicians have put the leads in the correct places on the patient’s body. We’ve got tons of great data so we will train that model on 100,000 ECGs. But what happens if we only train our AI model using data from adult tests? How will the model react if an ECG is finished on a 2-year-old patient? Clearly it could potentially miss errors that have an effect on treatment.
That’s why at Clario, our product, data, R&D, and science teams all work closely together to be sure that we’re using probably the most comprehensive training data to make sure accuracy and reliability in real-world applications. We use probably the most diverse data available to coach the algorithms incorporated into our products. It’s also why we insist on using human oversight to mitigate risks in the course of the development and use of AI.
How does Clario’s human oversight and monitoring process integrate with AI outputs to make sure regulatory compliance and ethical standards?
Human oversight means we have now teams of humans who know exactly how our models are developed, trained and validated. Each in development and after we’ve integrated a model right into a technology, our experts monitor outputs to detect potential bias and make sure the outputs are fair and reliable. I consider AI is about augmenting science and human brilliance. AI gives humans the flexibility to concentrate on a better level of challenge. We’re remarkably good at solving problems and still a lot better at intuition and nuance than machines. At Clario, we use AI to remove the burden on repeatable things. We use it to investigate broad data sets, whether it’s patient images or prior trials or some other thing that we wish to investigate. Generally, machines can try this faster, and in some cases, higher than humans can. But they cannot replace human intuition and the science and real-world experience that the wonderful people in our industry have.
How do you foresee AI impacting clinical trials over the following few years, particularly in fields like oncology, cardiology, and respiratory studies?
In oncology, I’m enthusiastic about advancing using applied AI in radiomics, which extracts quantitative metrics from medical images. Radiomics involves several steps, including image acquisition of tumors, image preprocessing, feature extraction, and model development, followed by validation and clinical application. Using increasingly advanced AI, we’ll have the opportunity to predict tumor behavior, tailor treatment response, and foresee patient outcomes based non-invasive imaging of tumors. We’ll have the opportunity to make use of it to detect early signs of disease and early detection of disease reoccurrence. As more advanced AI tools turn into more integrated into radiomics and clinical workflows, we’re going to see huge strides in oncology and patient care.
I’m equally excited in regards to the way forward for respiratory studies. This past 12 months, we acquired ArtiQ, a Belgian company that built AI models to enhance the gathering of respiratory data in clinical trials. Their founder is now my Chief AI Officer, and we’re expecting big things in respiratory solutions. Our approach to algorithm application has turn into a game-changer, not least since it’s helping reduce patient and site burden. When exhalation data is not analyzed in real time, and an anomaly is detected later, it forces the patient to return back to the clinic for an additional test. This not only adds stress for the patient, but it will possibly also create delays and extra costs for the trial sponsor, and that leads to numerous operational challenges. Our recent spirometry devices leverage the ArtiQ models to handle that burden by offering near real-time overreads. Meaning if any issues occur, they’re identified and resolved immediately while the patient continues to be on the clinic.
Finally, we’re developing tools that can have an impact across therapeutic areas. Soon, for instance, we’ll see AI deliver increasingly more value in electronic clinical outcomes assessments (eCOA). We’ll see AI models that capture and measure subtle changes experienced by the patient. This technology will help a large number of researchers, but for instance, Alzheimer’s researchers will have the opportunity to know where the patient is within the stage of the disease. With that sort of data, drug efficacy could be higher gauged while patients and their caretakers could be higher prepared for managing the disease.
What role do you suspect AI will play in expanding diversity inside clinical trials and improving health equity across patient populations?
For those who only take a look at AI through a tech lens, I believe you get into trouble. AI must be approached from all angles: tech, science, regulatory and so forth. In our industry, true excellence is achieved only through human collaboration, which expands the flexibility to ask the best questions, comparable to: “Are we training models that consider age, gender, sex, race and ethnicity?” If everyone else in our industry asks these kind of questions before developing tools, AI won’t just speed up drug development, it’s going to speed up it for all patient populations.
Could you share Clario’s plans or predictions for the evolution of AI within the clinical trials sector in 2025 and beyond?
In 2025, we’re set to see biopharma leverage AI and real-time analytics like never before. These advancements will streamline clinical trials and enhance decision-making. By speeding up study builds and implementing risk-based monitoring, we’ll have the opportunity to speed up timelines, ease the burden on patients, and enable sponsors to deliver life-saving treatments with greater precision and efficiency. That is an exciting time for all of us, as we work together to remodel healthcare.
