Artificial intelligence is making a foray into almost all industries and sectors of life. Healthcare is also enjoying the benefits of automation, and the impact is easy to see in clinical trials. The future of healthcare and the future of medical research will depend heavily on automation and artificial intelligence.
Looking at a basic example of the hospital databases, gone are the days of endless paperwork. With automation, there is a massive saving in terms of an increase in efficiency with fewer people running the back-office functions in hospitals. The impact of AI in clinical trials will be huge, resulting in greater efficiency and substantial savings.
Let’s explore the implications of AI in clinical trials below.
Automation of Processes
There are a lot of back-office functions that go on during clinical trials. It is, therefore, essential that there is the use of technology for efficiency. This technology will assist in tasks like data transfer and reading reports.
The use of cloud solutions will create efficiency in the infrastructure for a wide pool of players, including the hospitals, customers and suppliers. It will be easy to oversee their activities, thus ensuring better performance. Customers will also have real data on which they can base their decisions.
The precision with Data Analysis
Clinical research has some methodology that uses AI, thereby making the process of reaching a decision faster. The machines can make some decisions without needing the input of a doctor. Functions such as interpreting the results of CT, MRI, or X-rays can be left entirely to the machines. Think about the time-saving aspect, and the accuracy, because unlike the human eye, the machine is not likely to fail to notice some things.
Now with the application of the same in clinical trials, the failure rate will be significantly lower.
Any clinical trial depends on the design and execution of the protocols. If the structure is weak, the financial impact will be massive. There will also be inadequate data generation, all resulting in delays. Artificial intelligence will look at the past trials, see-through the enormous data, and highlight any areas that may need adjustment.
Patient Recruitment and Retention
Clinical trials depend a lot on the number of patients they can recruit. Finding the right participants can be difficult, and the pharmaceutical companies spent quite a bit of money looking for the right patients. Artificial intelligence can now sift through the patient data and come up with the right candidate. The result is efficiency, effective time management and ultimately saving for the company.
Real-Time Data Gathering and Sharing
Data collection and analysis is vital to clinical research. With artificial intelligence, it is possible to do away with the slow, labor-intensive manual processes. Further, the researchers can use digital applications to get and share information in real time. The data can then undergo immediate analysis, thus improving the response rate.
Impact on the Amount of Money Spent on Clinical Trial
Clinical trials are very expensive and time-consuming due to the research process. There are different phases, with each requiring a serious level of investment. Artificial intelligence will assist the researchers to spend less time and, therefore, resources in the clinical trials. Industry data estimates that up to 60% of research companies already using artificial intelligence in the clinical trials.
Insight into Data
A lot of data is generated during research, and AI will assist in detecting and interpreting it. Areas like cancer research will particularly benefit. With machine learning in clinical trials, AI will help in targeting therapy and ensuring the right dosage based on individual statistics. The intelligence it gathers will also assist in follow-up trials, thus a higher chance of success.
Industries are now fully embracing automation and the benefits it comes up. Artificial intelligence will double or Triple whatever advantages such Industries enjoy. In the future of clinical trials, we see AI assist with the detection of diseases, health services delivery and the discovery of new drugs.
Other benefits we can expect include better patient recruitment, processing of large amounts of data promptly, better trial designs, among others. In a nutshell, what we can expect is greater efficiency, huge savings concerning the financial aspects, and a better experience for patients and doctors.