The AI in clinical trial market is projected to reach a value of $4.8 billion by 2027, as AI and Machine Learning (ML) continue to advance and reshape clinical research. The current clinical trials landscape is influenced by macro trends such as the Covid-19 pandemic, geopolitical uncertainty, and climate pressures. At the same time, advancements in adaptive design, personalization, and novel treatments have made clinical trials more complex than ever before. Sponsors aim for greater agility and faster time to commercialization while maintaining quality and safety in a rapidly changing global market. AI presents optimization opportunities across every stage of clinical research, offering the potential to transform the clinical trials landscape.
Benefits of Leveraging AI in Clinical Trials
The pharma companies are exploring the power of AI in accelerating drug development. A significant part of drug development involves clinical trials and leveraging AI offers significant benefits from automating the trial workflow to identifying potential candidates for a trial. Following are the major benefits of AI in clinical trial:
Faster time to market
One of the biggest benefits of AI is its ability to automate many of the time-consuming and labor-intensive tasks associated with clinical trials. AI can automatically collect data from various sources, such as wearable devices and electronic medical records. This eliminates manual data entry, reduces errors, and speeds up the analysis process. AI algorithms can analyze vast amounts of data to identify patterns and trends that human researchers might miss. They can also reduce the time in sifting through data and identify a high potential candidate for a specific trial. All these makes AI a driving factor in reducing time to market of new drugs.
Reduced costs
AI doesn’t just save time; it saves money. Automating workflows like patient recruitment and safety monitoring frees up R&D teams, letting them focus on higher-value tasks. Predictive analytics also plays a role by helping companies allocate resources more efficiently. Furthermore, AI can accelerate crucial steps in drug development, like discovering new drug compounds and designing trials, ultimately leading to faster time to market and reduced overall costs.
More accurate data analysis
AI improves the efficiency and accuracy of data analysis. AI based algorithms can relate to historical patterns and predict the toxicity of potential drug candidates ruling out further research and effort on unsuitable compounds. AI models don’t miss out on any information while scanning vast number of datasets which is very likely for researchers. Also, the time taken to analyze such resources are very less compared to human interpretation. Accurate data analysis leads to saving a lot of time and money for the drug company.
Personalized medicine
By pinpointing patient groups responsive to specific drugs, AI facilitates personalized medicine.
Each patient’s unique need alters effectiveness in response to test drugs. AI simplifies this by identifying suitable patient populations based on genetic makeup and lifestyle. By integrating AI, personalized medicine becomes a possibility.
AI further personalizes treatment by optimizing dosing, calculating ideal dosage amounts and frequencies based on individual patient characteristics and medical history.
Improved patient outcomes
AI plays a pivotal role in crafting effective treatments with reduced side effects, ultimately improving patient outcomes.
From drug discovery to clinical trial design, AI enhances the R&D process, eliminating futile approaches and ensuring patient safety. Each discovery, be it new drugs or novel applications for existing ones, expands treatment options for patients.
AI’s pattern-recognition prowess is invaluable for identifying correlations in patient data and drug characteristics, making it a potent tool for uncovering new applications for existing treatments.
Prior to clinical trials, AI enhances patient recruitment by identifying high-potential candidates and predicting adverse reactions, thereby improving trial design and patient pool quality.
AI also elevates the patient experience during trials. Decentralized trials facilitate remote monitoring and data collection using wearables and mobile apps, transmitting real-time patient data, such as vital signs and medication adherence, to researchers. AI algorithms analyze data for early warning signals, enabling prompt intervention. Furthermore, AI-driven reinforcement learning methods boost participant adherence to trial protocols.
Regulatory compliance
Clinical trials are vital for developing life-saving treatments, but they also come with a big responsibility: ensuring patient safety and following strict regulations. Regulatory agencies need a clear picture of what’s happening in a trial. AI-powered systems keep a detailed record of every change made, ensuring complete transparency and smoother audits. This builds trust with regulators and ultimately benefits patients by speeding up the approval process for new treatments.
AI can automatically generate reports by pulling the right data and putting it into the format regulators need. This frees up researchers to focus on what matters most – the patients themselves. It can analyze data and identify potential problems with the trial, like missed steps or deviations from the protocol. This allows researchers to take proactive measures and minimize risks for participants. Early detection means early correction, keeping the trial on track and patients safe.
Innovative Use of AI in Clinical Trials
Employing AI-powered algorithms for diverse tasks, from data entry and analysis to enhanced medical diagnoses, empowers businesses to minimize the time and cost associated with drug development. Here are some ways organizations are harnessing AI in clinical trials:
Patient recruitment and screening
AI algorithms efficiently navigate vast datasets to pinpoint optimal trial candidates, expediting recruitment and cutting costs.
In the realm of patient recruitment and screening, AI technologies empower clinical researchers to streamline processes. Algorithms swiftly analyze extensive patient data to identify potential participants based on predefined criteria.
Moreover, AI-driven chatbots and virtual assistants engage with patients, offering trial details, addressing queries, and gathering initial data. This automation minimizes manual screening efforts, saving time and resources.
Trial design and optimization
The initial phase of clinical trials involves trial design, where AI plays a pivotal role in optimizing drug dosages, patient numbers, and data collection.
By identifying patterns in data, AI enhances trial design and optimization through predictions about patient behavior and drug efficacy. This knowledge allows researchers to design more efficient and effective trials, pinpointing the most suitable patient populations, treatment regimens, and dosages.
Intelligent Medical Objects, based in Rosemont, Illinois, has developed SEETrials, a method that leverages OpenAI’s large language model GPT-4 to extract safety and efficacy information from clinical trial abstracts. This approach enables trial designers to swiftly review how other researchers have designed trials and their outcomes.
A lab at University of Illinois Urbana-Champaign developed the HINT (hierarchical interaction network) algorithm. HINT predicts trial success based on the drug molecule, target disease, and patient eligibility criteria, further refining trial design and optimization.
Predictive modeling
AI employs predictive modeling to pinpoint patients most likely to benefit from treatments, thereby reducing trial failures and accelerating drug development.
By identifying patient populations best suited for specific treatments, predictive modeling enhances trial design, increasing success chances and minimizing risks of trial failure or patient harm. It also detects potential safety concerns earlier in the drug development process.
Leveraging patient characteristics and biomarkers, AI can create predictive models to assess individual patient responses to various interventions. This approach optimizes treatment efficiency and reduces risks, potentially revolutionizing personalized medicine. By detecting potential issues early, AI tailors therapies to each patient’s unique condition, enhancing care and outcomes.
Adverse event detection
Adverse events, or unexpected side effects, are a critical aspect of clinical trials, traditionally detected through manual reporting by participants and healthcare professionals. This method can be time-consuming and prone to errors.
AI enhances this process by identifying potential adverse events more rapidly and accurately than traditional methods. By employing machine learning algorithms to analyze data from multiple sources, including electronic health records, patient-reported outcomes, and social media, AI detects potential adverse events early on. The advantages include fewer severe incidents, saved time, and improved trial results.
Natural language processing
AI leverages natural language processing (NLP) to delve into unstructured data like medical records, unveiling hidden insights.
NLP, a subset of AI, centers on computer-human language interaction. In clinical trials, NLP extracts and analyzes unstructured data from diverse sources like electronic medical records and patient-reported outcomes. Automating data extraction and analysis, NLP streamlines processes, saving time and resources. Moreover, it aids researchers in uncovering patterns and relationships within data that might otherwise go unnoticed or require extensive time for identification.
Digital twins
AI-generated digital twins offer real-time insights into individual health, enabling personalized treatments.
These virtual representations of patients, crafted and monitored by AI, offer immediate insights into individual health conditions, supporting tailored treatments. Digital twins find applications in clinical trials and drug manufacturing, aiding in predicting biological responses based on biomarkers.
Unlearn, a start-up based in San Francisco, California, generates digital twins of patients involved in clinical trials. By analyzing an experimental patient’s data at the beginning of a trial, researchers can utilize the twin to predict how the same patient would have fared in the control group and compare outcomes. This approach typically decreases the need for control patients by 20% to 50%, according to Charles Fisher, Unlearn’s founder and CEO.
Generative AI
Generative AI is poised to accelerate clinical development by automating document generation, enhancing patient engagement and retention, and improving regulatory submissions, ultimately contributing to more efficient and effective therapies in the life sciences industry.
GenAI integrated chatbots can address patients’ queries, both during clinical trials and in regular practice. In one study, questions and answers from Reddit’s AskDocs forum were provided to ChatGPT, with healthcare professionals preferring its responses to doctors’ answers nearly 80% of the time. In another study, researchers developed a tool called ChatDoctor by refining a large language model (Meta’s LLaMA-7B) on patient-doctor dialogues and providing real-time access to online sources. ChatDoctor could provide answers to medical inquiries based on more recent information than ChatGPT’s training data.
Conclusion
Implementing AI in clinical trial workflow involves several key steps, including establishing a proof of concept, building a customized knowledge base, and training the model to address large-scale problems. Algorithms must be trained on extensive data to eliminate bias and ensure accuracy. Today, APIs enable the integration of cutting-edge advances into clinical trial applications.
By automating repetitive tasks, AI expedites the time to market for life-saving drugs and frees up human resources for more specialized tasks. By analyzing historical and current trial data, AI can inform future research, with machine learning suggesting more effective study designs. In the long term, AI has the potential to shift the focus from trial implementation to drug discovery, ultimately leading to improved treatments for patients in need.
