Artificial Intelligence/Machine Learning

Emerging applications of AI/ML, automation, and digitization are helping sponsors cut clinical trial start-up times to as little as four weeks, reduce data errors, and enhance patient engagement—demonstrating how tech-enabled processes are reshaping trial efficiency and experience across the study lifecycle.

Leveraging real-world data and AI-driven insights in clinical trial planning can reduce enrollment failures, improve retention, and prevent costly rescue studies by providing a more accurate view of patient populations and site feasibility.

In 2025, both big pharma and biotech are redefining decision-making as AI, real-world evidence, and flexible deal structures accelerate drug development, flatten organizational silos, and close the innovation gap across the life sciences ecosystem.

Novel trial designs are reshaping clinical development by improving efficiency, reducing redundancies, and accelerating timelines toward a new era of precision medicine.

In a decentralized, digitally enabled clinical trial environment, conventional approaches to data analysis are evolving as sponsors utilize technological tools in new ways to ensure compliance with global standards, mitigate risk, and bring life-changing therapies to patients faster.

Thermo Fisher will integrate OpenAI APIs into its Accelerator Drug Development platform and clinical research business, while Lundbeck deploys ChatGPT across its global workforce to drive R&D and commercial innovation.

Medidata deepens its collaboration with Sanofi to advance AI-enabled clinical development, while BioRender joins forces with Anthropic to power visual communication within Claude for Life Sciences.

How a unified approach to clinical data management, powered by artificial intelligence and advanced analytics, can elevate clinical trial monitoring and redefine how teams assess, act on and learn from data.

As pharma wrestles with whether to trust fully autonomous AI, semi-autonomous agents are emerging as a safer middle ground that reduces manual work, eliminates white space in clinical development, and accelerates trial timelines without compromising patient safety.

In this episode of the ACT Podcast, we highlight a recent Q&A featuring Ibrahim Kamstrup-Akkaoui, vice president of data systems innovation at Novo Nordisk; and a feature article by Chris Driver, senior director of product management, Patient Suite at IQVIA, in which they both highlight how sponsors are adopting automation to streamline operations.

Understand how adaptive human-in-the-loop frameworks can maintain safety and decision quality as AI becomes more embedded in trial monitoring and data review.

The convergence of AI, decentralized technologies, behavioral science, and real-world evidence opens the door to a new era in which the clinical trial industry proactively addresses participation barriers, integrates social determinants of health, and reimagines patient centricity.

Gain insight into how AI-powered agents can eliminate inefficiencies, shorten development timelines, and free clinical teams to focus on strategic decision-making.

Remote monitoring solution for cytokine release syndrome paves the way for more consistent patient care, a deeper understanding of immune responses, ultimately widening patient access to life-saving immunotherapy and reducing recruitment barriers in clinical trials.

Michel van Harten, MD, CEO, myTomorrows; and Kyle McAllister, co-founder, CEO, Trially, discuss how artificial intelligence can reduce barriers for underrepresented patients and streamline prescreening and outreach to support clinical research participation.

Maximizing AI’s potential in medical writing and regulatory submissions requires data standardization, objective content practices, and a streamlined document ecosystem that accelerates timelines while ensuring compliance.

In this Q&A, Randa Wahid of Indero, and Lyn Mursalo, a freelance clinical research professional, share how sponsors and CROs can build collaborative partnerships, navigate global trial complexities, and apply practical strategies to deliver studies on time and with quality.

In this video interview, Adrianne Rivard, senior community development manager at myTomorrows, explains why compliance with privacy standards and physician training are critical for safe and effective use of GenAI in clinical trial discovery.

Aligning artificial intelligence with patient needs, trial workflows, and employee experience enables adoption, builds trust, and ensures AI delivers measurable impact across clinical operations.

In this video interview, Adrianne Rivard, senior community development manager at myTomorrows, discusses how AI-powered pre-screening integrated into referral workflows can streamline eligibility checks and speed patient access to treatments.

In this Q&A from the 2025 Veeva R&D and Quality Summit, Ibrahim Kamstrup-Akkaoui, vice president of data systems innovation at Novo Nordisk, discusses simplifying system use through the company’s DataNow program and taking a measured, stepwise approach to applying AI and automation across the clinical development lifecycle.

In this Q&A from the 2025 Veeva R&D and Quality Summit, Ibrahim Kamstrup-Akkaoui, vice president of data systems innovation at Novo Nordisk, discusses simplifying system use through the company’s DataNow program and taking a measured, stepwise approach to applying AI and automation across the clinical development lifecycle.

In this video interview, Adrianne Rivard, senior community development manager at myTomorrows, explains how AI can reduce the time physicians spend pre-screening patients for clinical trial eligibility.

In this video interview, Adrianne Rivard, senior community development manager at myTomorrows, explains how AI can reduce the time physicians spend pre-screening patients for clinical trial eligibility.

In this video interview, Sunny Kumar, MD, partner at Informed Ventures, explains how organizational complexity, regulatory caution, and cultural risk aversion slow innovation in clinical trials, while tools like generative AI may help reduce operational barriers.