Automation is no stranger to clinical research, but the need for it has never been greater with post-pandemic demand, labor shortages, administrative and regulatory burdens, shrinking revenues, and evolving competition. These trends have compelled healthcare organizations to strive for greater agility, efficiency, and scalability.
Historically, the objective of automation has been to make manual, repetitive, and error-prone tasks faster, more efficient, and more accurate by transferring them from burned-out medical personnel – who are in short supply – to software robots that perform those tasks quickly, accurately, 24/7.
However, artificial intelligence (AI) has also been advancing at breakneck speed, led by generative (Gen) AI. Human amplification has gone beyond rules-based automation, the simplest form of AI, to intelligent automation. ‘Thinking’ has been added to ‘doing.’
In clinical research, the integration of AI and automation promises to reshape the field. This revolution touches upon every phase of a clinical trial, from planning and conduct to closure and archival. What are the potential benefits?
STUDY PLANNING
Study protocols. With Gen AI, researchers can now design study protocols by analyzing massive historical clinical trial data and predicting potential pitfalls or identifying opportunities for improvement. Trials can become more efficient and less likely to need later amendments.
Subject recruitment. Difficulties include inaccurate patient selection and prolonged timelines. AI-driven algorithms can sift through vast patient databases, identifying eligible participants based on study criteria and accelerating recruitment.
Prediction. Gen AI can help through predictive modeling, helping researchers understand different trial sites and forecast patient enrollment rates, potential dropouts, or response rates based on past data. This facilitates better resource allocation and more accurate timelines.
STUDY CONDUCT
Many areas have been automated, including study management, site monitoring, informed consent, issue and query management, safety and vigilance, subject management, risk management, remote data monitoring, collection of subject data, compliance and fraud management, and logistics. Gen AI has again added new possibilities.
Data collection and management. In an age of wearable tech and remote monitoring, patient-generated health data continues to explode. AI-driven platforms can collect, process, and analyze this data in real time, flagging any anomalies. This not only reduces the burden on human monitors but also ensures a more timely and accurate evaluation of the data.
Synthetic data generation. Where it’s prohibitive to gather enough real-world data, Gen AI can generate synthetic data, e.g., for rare diseases where patient samples are limited. Such datasets can help with initial modeling prior to the actual clinical study but must undergo rigorous validation to avoid errors and biases.
Adverse event prediction. Trained on private and/or public datasets, AI can predict potential adverse events or patient reactions that humans might miss. Early prediction leads to timely regulatory reporting and interventions to safeguard participants.
Personalized treatment pathways. Gen AI can simulate potential treatment pathways for individual patients, enabling personalized medicine approaches by predicting how a patient might respond to various interventions.
Enhanced image analysis. For trials involving medical imaging, AI-powered algorithms can detect tiny changes or patterns that might be overlooked by the human eye. Fatigue, boredom, and distraction are nonfactors. Radiology, pathology, and ophthalmology are just a few fields where AI diagnosis has made inroads.
STUDY COMPLETION
Closure and archival tasks have been automated, including final reporting, regulatory submission and trial registration, and final trial master file (TMF) reconciliation and archival. AI has added to the possibilities.
Data analysis. The primary post-study challenge is to make sense of potential massive trial data. Traditional statistical methods can be time-consuming and may not always capture complex non-linear relationships. AI-driven algorithms, especially deep learning models, can quickly analyze vast datasets, extracting meaningful patterns and insights.
Data interpretation. Gen AI can model complex relationships within trial data, generating insights from multidimensional datasets that may not be apparent using traditional analytical methods.
Report generation. AI can generate initial drafts of clinical study reports. While human oversight is still essential, such tools can significantly speed up the documentation process. Again, reducing e-paperwork saves time.
Archival. The growth of clinical trial data has made digital archival systems a necessity. AI can assist in curating, categorizing, tagging, and storing vast amounts of data for easy retrieval later. This is crucial for future meta-analyses or where data needs to be reinterpreted in light of new knowledge.
Archival enhancement. Gen AI can simulate retrieval scenarios, informing the design of optimal archival infrastructure.
GENERAL TASKS
Gen AI can also play a role in general clinical research tasks.
Communication. Chatbots powered by AI can handle routine questions from study participants or sites, such as clarification on procedures or scheduling matters. This reduces the burden on study coordinators and ensures 24/7 responsiveness.
Enhanced communication. Gen AI can be used to produce communication materials like informative brochures, study updates, and summary reports, all personalized to the specific audience’s needs.
Regulatory compliance. Staying compliant with evolving regulations can be challenging. AI can monitor regulatory changes to make sure trials stay compliant. Additionally, AI-driven systems can assist in the timely submission of regulatory documents, verifying format and content compliance.
Regulatory documents. Gen AI can produce initial drafts of regulatory documents for submission. By analyzing prior submissions and current requirements, AI can align content with applicable standards and best practices.
Training. AI-driven virtual reality (VR) and augmented reality (AR) platforms are being used to train clinical research staff, offering immersive and interactive experiences that can significantly enhance understanding and retention.
Training simulations. A diverse range of scenarios based on real-world data can help research staff prepare to deal with a broad range of situations, from subject dropouts to adverse events.
Finally, an intriguing phenomena associated with Gen AI that we have not fully leveraged is its ability to think ‘outside the box.’ Not bound by human cognitive constraints, it can often produce solutions or pathways that might be counterintuitive, yet useful.
The role of AI and automation in clinical research is undeniable. While AI won’t replace human intuition, judgment, and oversight, it will make more room for these unique human traits to develop, grow, and flourish.
