Harmony between humankind and technology depends mainly on the way technology is used. How it affects the lives of targeted people or how it brings a positive change to the world. AI has proved its worth when it comes to assisting us humans in many ways if used the right way. 

The pandemic itself showed us the association of technology in healthcare. Medical science is progressive owing to technological researches and innovations. 

Clinical trials are a critical part of medical research and hence they have also evolved and are still getting better with a great scope of improvement at each stage.

Clinical trials have lives involved and hence any sort of uncertainty, discrepancy, and errors may have severe repercussions. Hence, pharma companies need to take advantage of disruptive technologies that are scalable and more efficient too.

AI/ML capabilities utilised in clinical trials help in:

•  Make Clinical Trials Intelligent
• Optimize Clinical trial processes
• Get Better decision-making insights 

This would lead to improved efficacy and safety of the treatment along with faster processing.

AI provides deeper level insights to detect trends and patterns from massive amounts of structured, unstructured, and binary data that leads to automation, efficiency, and cost reduction. 

The clinical trials and development market is becoming highly competitive owing to stricter regulatory standards and greater emphasis on patient safety. 

ERT (eResearchTechnology, Inc.) is a global company that specializes in clinical services and customizable medical devices to biopharmaceutical and healthcare organizations. It offers centralized cardiac safety and respiratory efficacy services in drug development and also collects, analyzes and distributes electronic patient-reported outcomes (ePRO) in multiple modalities across all phases of clinical research.

According to Dr. Prakriteswar Santikary, Vice President and Global Chief Data Officer at ERT - In charge of global data, cloud and AI strategy and execution, “Less than 10% of trials end on time and the costs to develop new drugs are sky-rocketing. But, AI can help to reverse these trends and enable sponsors to optimize clinical trials and accelerate new product development. From maximizing patient recruitment and retention to improving data collection and risk monitoring, Artificial intelligence has the potential to disrupt every stage of the clinical trials process. Researchers who embrace these new technologies can dramatically shorten time to market for life-saving drugs and deliver a huge win for the patients who need them most.”

Unlike traditional methods, the utilisation of AI also has the potential to reduce or even eliminate the need for patients to travel to various medical centres for trials. AI algorithms process the data collected from medical records, hospital data, mobile sensors or apps, or any other sources. 

AI-enabled voice assistant technologies to provide better engagement and interaction with patients to collect patient responses too.

AI/ML-enabled processes to improve clinical trials in several ways including:

• Measuring drug response
• Monitoring trial risks
• Centralising trial monitoring 
• Using voice-enabled responses for better patient response collection
• Monitoring drug consumption and pattern
• Data analysis

Many companies are coming forward and showing a keen interest in using AI-powered clinical decision support tools. Tempus is a technology company that has built the world's largest library of clinical and molecular data and an operating system to make that information accessible and useful for patients, physicians, and researchers.

Tempus is making precision medicine a reality through the power and promise of data and AI. With the world’s largest library of clinical and molecular data, and an operating system to make that data accessible and useful, Tempus is enabling physicians to make real-time, data-driven decisions to deliver personalized patient care, and in parallel, facilitate discovery, development, and delivery of optimized therapeutic options for patients through distinctive solution sets.

There are various challenges that clinical trials face such as inefficacy, lack of safety of an intervention, an imperfect design or protocol, money constraints, participant drop-outs, lack of volunteers, and many more. Many times there are issues around travelling from the patient’s end or there is a loophole in medicine administration or monitoring that might lead to flaws or delays which beat the whole idea and give incomplete results that do not add to the value of the trial.

Many times, the recruitment of the right resources becomes a challenge that can be taken care of by the early inclusion of AI and medicine as an aspect to be studied in the medical study curriculum.

AI has huge scope in these areas, we need a stronger collaboration among doctors, researchers, technicians, and AI experts to create a more robust AI ecosystem around drug discovery.