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    Clinical trial concept

    With hundreds of thousands of patients entering clinical trials each year, more research and life-saving treatments are being developed. This rapid pace of innovation is being augmented by using artificial intelligence (AI) to accelerate clinical timelines, enroll a more diverse pool of patients, monitor and retain patients, and use data to drive AI-enabled analytics. On average, a drug development process lasts nine years and costs $1.3 billion, yet COVID-19 highlighted an urgent need for faster, more agile clinical trials. Now, the $52 billion industry of clinical trials is getting a much-needed makeover thanks to artificial intelligence.

    More Automated, Diverse Patient Selection

    Between 80% and 85% of trials are delayed due to patient selection, making filling a clinical trial one of the most arduous and complex aspects. From bias in representation to difficulty sourcing, patient selection is one area that AI could improve. For example, what if a clinical trial could quickly source ideal participants — those who wouldn’t be inconvenienced by travel because they’re not human?

    Data and AI are making that possible with the concept of “digital twins,” virtual representations of patients built on historical data. These digital twins are followed in real time to simulate the biological processes and outcomes if a human patient with the same characteristics went through the clinical trial. This process saves trials time and money and creates a more diverse pool of patients, which can eliminate biased outcomes.

    Doctors discussing clinical trials

    For example, Unlearn.AI developed TwinRCTs, a proprietary technology that combines AI, digital twins, and novel statistical methods to enable smaller, more efficient trials, helping make trials more patient-centric, shortening enrollment times, and increases confidence around trial outcomes.

    Curebase, an AI-based software, is another innovation that allows patients to participate in trials from the comfort of their homes, eliminating the need for travel. With wearables, home visits, and telemedicine, patients from more diverse backgrounds can participate without traveling in person or taking time off from work.

    More Meaningful Data and Analysis

    Clinical trials generate millions of data points and can spread across locations and patients, making it hard for researchers to analyze and draw conclusions quickly. AI automates data collection, reporting, and processing to share analytics, increase collaboration, accessibility, and more.

    For example, Elluminate AI uses AI framework and machine learning to automate processes and deliver faster, more comprehensive insights. More meaningful data is created, and researchers can collaborate and share results with other practitioners.

    Also, with AI, researchers can enhance their data analysis across the entire trial lifecycle — not just at the conclusion. AI can analyze every step of the process, from recruitment to monitoring, and it can also integrate data from multiple sources to create a comprehensive picture. Additionally, when utilizing wearable technology or other monitoring devices, AI can aggregate data to develop predictive analysis, analyze the patient’s response to treatment, and identify when a patient could be moved to a sub-cohort of the trial — all in real time.

    Streamlined Trial Processes

    Day-to-day management of patients and trial processes is time-consuming and often decentralized. AI-powered platforms consolidate information, streamline the management of patients, and create a centralized solution for all trial data. For example, Deep 6 AI brings together all research stakeholders in an AI-powered, real-time, data-driven, collaborative ecosystem. Another innovative technology, Deep Lens AI, creates easy-to-use dashboards to ensure enrollment goals stay on track. These tools allow companies to digitize clinical-trial processes and complete studies faster, providing life-saving treatments and medicines to reach the public market in less time.

    Increased Cost Savings

    76% of Deloitte survey respondents are investing in AI for clinical development, leaning on digital transformation to revolutionize how trials are performed. One of the most significant benefits is cost savings. Trials can perform less rework, use standard materials and resources, automate processes, increase efficiency, and reduce overall time and cost invested. AI can cut costs by an estimated $28 billion per year while reducing the clinical research phase by more than half, according to ITIF.

    Biotechnology concept

    How Ambiq Contributes

    Startups and big tech are actively revolutionizing every aspect of clinical trials, and it’s estimated the AI clinical trials market could be worth $4.8 billion by 2027. Ambiq contributes with energy-efficient solutions, extending battery life on devices, so researchers can utilize innovative technology without sacrificing performance or user experience.

    Indeed, through the advanced Subthreshold Power Optimized Technology (SPOT®) platform, Ambiq has helped many leading manufacturers worldwide create products that can operate for days, months, and sometimes years on a lithium battery or a single charge. Visit for more information. 

    Jan 18. 23
    Written by

    Charlene Wan