As clinical trials have evolved over time, by the use of new technologies and modern equipment, medical investigators and laboratories have managed to lower the study cost, decrease the time for research, and supervise the supply of resources in a study and optimize the study design for the trial.
According to clinicaltrials.gov, 418,021 studies have been registered globally, and in the last seven years, there has been a 49% hike in this number. The cost of phase I of a trial alone generally costs US$3.4 million, involving patients, and the numbers increases as the study advances each stage due to the increasing number of volunteers and other complications.
While budgeting is a significant limitation faced by the companies conducting trials, the other challenges faced by them include:
Less number of patient enrollments
Improper/ misguided study design
Artificial intelligence is referred to as a system or an algorithm that mimics the intelligence of human beings, providing similar or more advanced results with lesser human intervention and reducing the processing time.
Significance of Artificial Intelligence:
Companies have integrated AI-powered tools to transform clinical developments. Branches of AI such as machine learning and neural networks have direct applications in clinical trials.
Today, artificial intelligence has restructured the dynamics of clinical trials and has increased efficiency throughout the study. The challenges medical investigators face are seamlessly resolved by AI systems such as OCR (optical character recognition) and NLP (natural learning process).
AI-powered solutions to aid Clinical trials:
For any clinical trial, the most complex and time-consuming step is identifying and enrolling relevant patients. Between 2000 and 2011, the US National Cancer Institute NCTN could not find even half the number of patients required for almost 18% of cancer trials conducted.
The major reasons behind this are the lesser reach to proper patients, unavailability of algorithms to collect sufficient data for a patient database, and language barrier to identify the viable patient for a particular trial. Currently, in the US alone, 18000 clinical trials are recruiting patients actively.
AI primarily identifies patients eligible for types of trials in an area using NLP (natural learning process), an analytical tool, and creates a database of relevant patients for a particular sort of trial. This cuts down the unnecessary manual inputs required in discovering patients and reaching out to them in a traditional, time-consuming way for any clinical trial.
Clinical Trial Designs:
For a clinical trial, the study design must be optimized and corrected frequently by more than one consulting medical professional. This measure is taken to eliminate even minor errors in the design, as they can cost hundreds of thousands of dollars and cause an incalculable delay in results.
AI-powered tools process the vast amount of data that needs to be studied for a trial, analyze the information and provide outputs much faster than an individual manually reading the databases.
Clinical trial-based start-ups are evolving, such as Unlearn.ai and AiCure providing analytical solutions and Hawkins with collaborative solutions combining data scientists, clinicians, and pharmaceutical companies. Apart from them, VeriSIM Life, a start-up offering bio-simulation tools to lower the risk for any clinical trial, has been significantly adopted by many trial designs today.
Pharmacovigilance detects, monitors, and prevents adverse effects of pharma products, ensuring a safe and assessed clinical trial result. With the use of big data analytics, a patient's health state is assessed, which helps in better studying the risks associated with using a particular drug.
Artificial Intelligence and machine learning help automatically determine the risks and other adverse effects. Both, raw and structured data collected by AI are further analyzed by deep learing and neural networks. OCR (optical character recognition) and NLP (natural learning process) are other tools used to analyze the data collected and are used to increase the efficiency and safety of the trial.
It is observed that even after following every necessary protocol from the clinical end, errors occur, and incorrect data is recorded. This may lead to a flawed database and can significantly alter the desired results, increasing the risk of adverse effects on a drug’s usage.
This happens due to negligence from the volunteer/patient, who often forget to take medicine/drugs on time and, on several occasions, skip one or two doses. To overcome this issue, companies use automated applications of AI-powered monitoring systems which integrate an app to the patient’s fitness device or mobile phone.
The monitoring system sends the patient a text notification on their preferred device, ensuring the patients don’t skip any dose of medicine. These monitoring systems also help collect real-time insights from patients, such as their weekly blood pressure data and a drop or increase in their heart rate at a given period.
How powerful is AI-powered solution?
The clinical trial industry loses about $80 billion yearly on drug development due to trial delays. Iqvia, a US-based company, successfully increased the precision rate for predictive algorithms to 79% and increased the enrollment rate by 20.6% by collectively utilizing advanced technologies such as AI and ML and integrating them into clinical research.
Artificial intelligence has the power to transform clinical research into a much more automated and efficient sector that is less prone to manual errors.
Clinical research organizations across the world have already adopted AI-powered solutions and are being benefited in more than one aspect. Efficiency, cost-effectiveness, and decreased error rate are a few advantages among many that are appreciated by the clinical research industry.
Aviskaran provides services to help your company set up an AI-powered ecosystem, so your company can benefit from these high-yield tools.
Content Writer, Aviskaran Technologies