Life science companies are fast finding new and innovative ways to harness the power of generative AI (Gen AI) and Natural Language Protocols (NLPs). This is especially true in the development and testing of potential new treatments. From finding ways to save time on laborious processes to homing in on important therapeutic insights, data science teams are already using Gen AI to enhance research and development (R&D) efforts, streamline clinical trial operations, improve clinical trial patient recruitment and retention, and simplify regulatory compliance tasks—all in the name of getting important new therapies to patients faster.
Enhanced R&D Efforts
Developing new drugs and treatments takes years of research. We may, however, be on the verge of a new era. Today, Gen AI is assisting in research and significantly speeding up drug development processes by ingesting vast amounts of biomedical data and quickly identifying patterns, correlations, and potential biomarkers for diseases and treatments. This makes developing novel molecular structures, predicting potential side effects, and repurposing existing drugs for potential new uses so much easier.
Streamlined Clinical Trial Operations
Once a potential treatment is identified and created, it needs to be meticulously tested. Designing clinical trial protocols to test new treatments is a complex task that ensures trial standards are met and sets the stage for ease of administration, troubleshooting, and ease of participation. Because it is difficult to predict all the roadblocks a given trial might encounter, protocols often must change once a trial has begun, a process that slows trial progression. But with Gen AI, protocol designers can better predict potential clinical trial issues from the beginning and build protocols around those predictions.
For example, they can use Gen AI to analyze vast amounts of medical literature, patient records and historical trial data to identify statistical patterns that might not be immediately apparent to the human eye. Discovering trends in patient responses, adverse events and treatment outcome would inform trial protocols with a greater likelihood of success.
From there, Gen AI can create predictive models that simulate the behavior of clinical trial participants and predict possible outcomes under different scenarios. This could help researchers anticipate challenges, identify potential bottlenecks and adjust the protocol to mitigate risks or enhance the trial’s efficiency.
Improved Recruitment and Retention
Patient recruitment for clinical trials has always been a challenge that delays trials. Even when recruitment is successful, patient retention can be difficult when patients believe a trial no longer accommodates or suits their needs, causing even more delays.
To mitigate these issues, Gen AI is helping data scientists locate possible candidates for clinical trials by analyzing the data available in Electronic Health Records (EHRs) and health care providers’ NPI numbers. Using that data, they can pinpoint HCPs with specific patient populations and treatment portfolios. They can even discover patterns in diagnoses and information like disease stage, treatment types and responses to treatment. This method can also be used to understand patient needs and build study protocols to meet those needs.
For patient retention purposes, Gen AI could serve as a virtual assistant for trial participants, providing them with information, reminders and support throughout the trial to enhance participant engagement and ensure that they stay informed and motivated. It can also assist trial designers in crafting patient-centric assessment schedules, minimizing invasiveness, and optimizing sequences to enhance enrollment and retention.
Simplified Regulatory Compliance
Ensuring that healthcare’s everchanging, complex compliance regulations are being met is arduous and detailed work, exactly the kind of thing Gen AI is built for. From document generation to translation of complex healthcare legalese to risk assessment reports, Gen AI could put a huge dent in the regulatory pile.
Compliance teams are using Gen AI up to monitor regulatory changes and updates in real-time, alerting companies when there are modifications to the relevant regulations that may impact their operations. They could also use AI to generate customized guidance on how to achieve compliance, help businesses prepare for regulatory audits, and act as chatbots to provide real time answers to regulatory questions.
It’s just a matter of a short time before all these efficiencies are in place, and it’s hard not to imagine the possibilities—new life-changing therapies, possible cures for diseases, and increased speed to market are likely just the start.
