Artificial intelligence (AI) is reshaping clinical trials, offering new efficiencies and insights. Rohit Nambisan, CEO of Lokavant, shares his expertise on AI’s role in optimizing clinical operations. This discussion explores AI’s potential, challenges, and future impact on the industry, providing valuable insights for researchers, sponsors, and patients.
Moe: What are the biggest AI opportunities in clinical operations today?
Rohit Nambisan: AI’s potential in clinical operations lies in automating laborious tasks, allowing experts to focus on strategic decision-making. A significant opportunity is in predictive and non-reactive clinical operations. For instance, in a global phase three study, AI identified enrollment risks as early as day sixty, allowing us to mitigate costly changes. This predictive capability reduced errors significantly, down to a 5% error rate compared to a 350% error with traditional methods. This example highlights AI’s ability to streamline operations, reduce costs, and enhance the accuracy of trial forecasts, ultimately leading to more efficient and successful clinical trials.
Moe: What do you think are the biggest barriers to achieving these AI-driven efficiencies?
Rohit Nambisan: The primary barrier is data quality. The fragmented data ecosystems in life sciences pose a challenge, as AI’s effectiveness depends on the quality of input data. We often say “garbage in, garbage out,” which holds true for AI. Additionally, there’s a cultural hesitancy in the industry to adopt new technologies, compounded by the proprietary nature of data that companies are reluctant to share. Overcoming these barriers requires a data-oriented approach and organizational realignment to build trust in AI outputs. This involves integrating diverse data sources and fostering a culture that embraces innovation and collaboration.
Moe: Why should organizations invest in AI, and what are its unique qualities?
Rohit Nambisan: AI’s value lies in its ability to detect patterns in complex datasets and adapt to new information. It enhances human judgment rather than replacing it, offering a productivity tool that unlocks higher quality outcomes. For example, AI can continuously adapt its forecasts based on real-time data, improving accuracy as a study progresses. While immediate ROI might be challenging to quantify, the long-term productivity and decision-making quality gains are substantial. AI allows professionals to focus on higher-level strategic tasks, leading to more innovative and effective clinical trials.
Moe: How can the industry overcome AI’s lack of trust and transparency?
Rohit Nambisan: Transparency is crucial, especially in our field. We pair complex models with explainable AI to provide causal explanations for decisions. This approach and a natural human interface for questioning AI outputs help build trust. For instance, when AI suggests a particular site or country for a trial, it can explain the criteria and data that led to that recommendation. Regular validation against historical data ensures the reliability of AI predictions, providing stakeholders with confidence in the technology’s outputs.
Moe: How do you balance innovation in AI with regulatory rigor in study planning?
Rohit Nambisan: We integrate regulatory compliance from the outset, ensuring our systems align with industry standards like 21 CFR part 11. This approach allows us to incorporate AI into study conduct without regulatory concerns, providing a seamless transition from planning to execution. By designing our systems to be compliant, we ensure that AI can be used confidently throughout the trial process, from feasibility planning to study conduct, without compromising on regulatory requirements.
Moe: Looking ahead, where do you see AI taking clinical trial planning in the next five years?
Rohit Nambisan: AI will make trial planning more continuous and adaptive in five years, with real-time forecasts guiding decisions throughout the study lifecycle. This will lead to faster studies, better alignment with real-world settings, and quicker patient access to therapies. For researchers, it means less guesswork and more strategic focus, ultimately increasing the probability of study success. AI will enable a more dynamic approach to trial management, allowing for adjustments based on real-time data and evolving study conditions, which is a win-win for all stakeholders involved.
Moe Alsumidaie is Chief Editor of The Clinical Trial Vanguard. Moe holds decades of experience in the clinical trials industry. Moe also serves as Head of Research at CliniBiz and Chief Data Scientist at Annex Clinical Corporation.
