Artificial intelligence is deemed to be an integral part of the fourth industrial revolution. This revolution, unlike previous industrial revolutions, is expected to bring in a system revolution rather than a product revolution as told by the founder of the World Economic Forum, Klaus Schwas. By a system revolution, what is inferred is that the future holds opportunities for melding cyber and physical systems via artificial intelligence-real world interfaces. An example of this is IBM’s Watson which is an artificial intelligence, cognitive computing program that is currently being tested by Mayo Clinic researchers, in collaboration with IBM on the difficult task of recruiting matching patients with the requirements of a clinical trial.
The following are ways in which artificial intelligence can be adopted to enhance clinical trials:
Active patient participation in clinical trials
In conventional clinical trials, healthcare providers like the consulting physicians and hospital staff are the people who collect data from patients and provide them to the investigators in the clinical trials. The “manual” mode of information collection and recording, results in time lapses, and also lack of direct involvement of patients in clinical trials. With artificial intelligence systems, patients can directly be involved. People in today’s world are defined by the variety of personal digital devices they use. Many of these are wearable devices that can provide a wealth of real-time information about the user. In clinical trials, if a patient uses these wearable devices, information about their conditions can be passed on to an investigator in real-time. Also, patients’ suggestions and complaints can be factored into clinical trial protocol in real-time which lessens the probability of a drug causing adverse reactions and getting rejected by regulators.
Creation of consistent standards for clinical trials
Adoption of electronic health records is already an established norm in clinical trials record keeping in regions like North America, Scandinavia, and Western Europe. But it is found wanting in other regions like Asia, Africa, and Southern Europe. Paper-based assessments in clinical trials are still widely prevalent and the reason was a lack of knowledge on how to operate artificial intelligence systems to streamline clinical trial records and other data. A few companies have begun to adopt artificial intelligence systems in their processes such as MedRespond. Such adoptions have resulted in better patient recruitment and patient retention for clinical trials.
Precision medicine through artificial intelligence
Treating patients precisely involve getting the right treatment plan to the right patient at the right time. Since a lot of criteria are involved in finding precise treatment plans, artificial intelligence can provide a means to enable precise treatment plans during clinical trials using new drugs.
Cloud-based clinical trial systems
Participation in clinical trials is always a struggle in the current scenario because pharmaceutical companies need to find the required investigators and patients and sign them up. Also, investigators and patients cannot connect to each other easily due to lack of information on where and what clinical trials are being conducted and their requirements.
Crowd-sourcing is a phenomenon usually seen at social events, but artificial intelligence systems like could based computing can employ crowdsourcing to enable investigators and prospective patients to meet with pharmaceutical companies more conveniently and at lower costs.
HeroLinx is a crowd-sourcing clinical created by Scott Ballenger of the Trial Acceleration Institute. His system aims at organizing clinical trials centered around patients by actively involving them in the trials by suggesting protocol design tips and collaborating with the study plan. Due to this, patients feel more included and patient retention in clinical trials is highly probable.
These ideas and innovative systems are the future of artificial intelligence in clinical trials. Although they are small in number presently, they are bound to grow exponentially when cost-benefit ratios and patient happiness are taken into account.
Nayak, V. S., Khan, M. S., Shukla, B. K., & Chaturvedi, P. R. (2016). Artificial intelligence in clinical research. International Journal of Clinical Trials, 3(4), 187-193.