If you could go back to the 1960s and 70s and tell people that they can play music, video movies, video games, and whatnot from a tiny 6-inch device, they would think you are crazy. The idea of small technology that can do almost everything, such as your mobile phone, was beyond most people’s understanding. It was simply too much information for someone from that period to handle, but whatever we once thought was impossible has come true in reality.
When it comes to future technologies, the things that come to our minds are Robots, Flying Cars, a City of Lights, Skyscrapers, and so on, and we are certainly headed that way. We must think about it first to make anything possible, but what if our thinking abilities limit us? That’s why scientists came up with Collective Intelligence to get past this limitation. To know more about this and various other technologies that can change the world, continue reading this content.
Artificial Intelligence (AI) is being used in the life sciences to cut costs and speed up the development of clinical applications in medication. The life sciences have made a big move into the digital age in the last few years.
Pharmaceutical corporations have generally resisted AI, but a growing number are working with smaller AI start-ups to gain experience in drug research, and others are aggressively recruiting for AI positions inside their organizations. The CB Insights report states that healthcare AI firms could have collected over $6 billion in the capital by 2020.
The need for clinical studies to evolve
Back in the days when we used to think about artificial intelligence, the first thing that came to our mind was Terminators, which is unrealistic, and that’s Hollywood for you. Artificial Intelligence isn’t like that at all, at least not yet. The most realistic forms of commercial Artificial Intelligence you can find are Google Assistance and Siri. These AI are the best example of what we can achieve with ML, Cloud Computing, and a large data pool. While such AI has come a long way since its first introduction, they are nowhere close to the level of Intelligence that we expect them to be, but that can soon change. The rapid advancement of quantum computing could boost AI technological development much beyond our imagination and finally bring the level of AI technology that we now see in Sci-fi movies.
The medical community still widely acknowledges this method of testing potential drugs for effectiveness and safety. Besides this, inefficient patient selection (including recruiting and retention issues) and challenges in treating and monitoring patients efficiently contribute to high trial failure rates and increased research and development expenditures. RCTs lack analytical capacity, adaptability, and quickness for the development of complicated new medicines aimed at smaller and sometimes varied patient groups.
The impact of AI on the process of clinical trials
AI algorithms and a strong digital infrastructure may allow for the cleaning, aggregation, coding, storage, and management of clinical trial data in real-time.
Additionally, enhanced electronic data capture (EDC) could help mitigate the effect of human error during data gathering and enable a seamless connection with other systems.
Choosing an investigator and a location: To ensure that a clinical study has the best possible outcomes, it is essential to choose investigators and venues carefully. The site’s administrative processes, resources, and doctors with in-depth expertise and knowledge of the condition may affect research duration and data quality and authenticity
Using available data to guide AI-enabled clinical trials: However, because of functional data silos and disjointed systems, clinical trial organizations may be unable to get a complete picture of their clinical trial portfolio across different worldwide locations.
Clinical trial design: Companies in the pharmaceutical industry are experimenting with various methods to improve the clinical trial design. Patient support programs, post-market monitoring data, and other scientific and research data sources have given new impetus to clinical trial design.
Using AI-enabled trial management tools may assist patients in staying involved in their tests. As a result of digital reporting applications and wearables, clinical trials may now be conducted with patients at the center of attention. As a result, patients are more likely to adhere and stick to their treatment regimens since they are less likely to have to travel to the study locations, which promotes patient compliance and adherence.