Technology

In this video interview, Krishna Cheriath, vice president and head of clinical research digital data and AI at Thermo Fisher Scientific, maps the highest-impact opportunities for AI across the trial lifecycle—from smarter protocol design and enrollment matching to data collection, cycle time compression, and the emerging potential of synthetic data in rare disease.

Risk-based monitoring requires integrated data systems, validated analytics, and strong governance to work effectively across global trials, but sponsors face significant technical and operational challenges that demand strategic solutions and organizational alignment.

In this video interview, Krishna Cheriath, vice president and head of clinical research digital data and AI at Thermo Fisher Scientific, argues that effective patient-centered AI must go beyond direct-to-patient tools to address social determinants of health and reduce the administrative burden on sites so that investigators can focus predominantly on the patient.

In this video interview, Krishna Cheriath, vice president and head of clinical research digital data and AI at Thermo Fisher Scientific, outlines the leadership priorities, team structures, and boundary-spanning capabilities that separate organizations that realize meaningful AI gains from those that struggle to move beyond the pilot stage.

In this video interview, Krishna Cheriath, vice president and head of clinical research digital data and AI at Thermo Fisher Scientific, explains how AI is being applied to case intake today and why successful adoption depends less on technology than on reimagining workflows and investing in workforce upskilling.

Digital twin technology can reliably simulate standard-of-care treatment outcomes using historical clinical trial data, offering a practical pathway to virtual control arms that reduce recruitment burden and address ethical concerns around placebo designs.

In this Q&A, Cheryl Kole, vice president of solution strategy and commercialization at Almac Clinical Technologies, examines what it takes to build and sustain a clinical trial technology infrastructure that can keep pace with increasingly complex study designs.

In part 2 of this three-part series, behavioral science and service design reveal how poor experiences with confusing information, unreliable technology, and inefficient processes drive site disengagement and patient dropout, and how measuring these experiences early enables practical, evidence-based solutions.

In this video interview, Cheryl Kole, vice president of solution strategy and commercialization at Almac Clinical Technologies, offers a framework for thinking about which emerging trial complexities deserve to be built into core technology platforms and which are better managed as bespoke, one-off configurations.

As clinical trials grow more complex, the technology infrastructure supporting them is under renewed scrutiny. Across data validation, AI adoption, and site-based systems, 2026 is shaping up as a year of implementation rather than experimentation.

In this video interview, Cheryl Kole, vice president of solution strategy and commercialization at Almac Clinical Technologies, discusses why sponsors must integrate their digital and physical supply chains as a single operational flow and how to build the in-house capabilities and partner relationships needed to manage that complexity effectively.

Sample integrity and traceability often fail not because of science but because clinical trials rely on manual processes and fragmented systems that obscure problems until samples are already compromised.

In this video interview, Cheryl Kole, vice president of solution strategy and commercialization at Almac Clinical Technologies, examines why current eClinical technology too often prioritizes data flow over the day-to-day needs of users and what a truly guided workflow experience should look like for busy site teams.

In this video interview, Cheryl Kole, vice president of solution strategy and commercialization at Almac Clinical Technologies, explains why an effective tech stack must be purpose built, fully integrated, and designed above all to simplify the execution of clinical research.

In this Q&A, Mohammed Saeed, MD, PhD, chief medical officer at Solera Health, explores how wearable devices and continuous remote monitoring are reshaping clinical oversight, from early intervention to AI-driven pattern detection.

In this video interview, Mohammed Saeed, MD, PhD, chief medical officer of Solera Health, explores how AI models capable of analyzing continuous wearable data streams alongside broader patient information could detect subtle warning signs of deterioration that no clinician could identify alone.

Why rigorous testing and validation matter more than ever.

In this video interview, Mohammed Saeed, MD, PhD, chief medical officer of Solera Health, discusses how regulatory trust-building and reimbursement reform are essential to making wearable devices a sustainable and widely adopted part of clinical care delivery.

In this video interview, Mohammed Saeed, MD, PhD, chief medical officer of Solera Health, examines the operational challenges of incorporating wearable data into clinical workflows, including information overload and false alarm fatigue, and what those obstacles mean for patient safety.

In this video interview, Mohammed Saeed, MD, PhD, chief medical officer of Solera Health, explains how continuous remote monitoring enables earlier detection of patient deterioration and allows care teams to intervene before conditions escalate to hospitalization.

In this video interview, Mohammed Saeed, MD, PhD, chief medical officer of Solera Health, discusses how wearable devices are giving clinicians a unique window into patient health outside the clinic and why that real-world visibility is becoming essential to modern care.

Older adults generate disproportionately high engagement in digital trial recruitment, suggesting they represent an overlooked opportunity when recruitment systems are designed around their actual behavior rather than outdated assumptions about digital capability.

Closing the gender gap in data science and tech requires tackling barriers at every stage, from early education through career advancement, while actively challenging the unconscious biases that continue to hold women back.