Electronic Health Reporter

Future technology is changing the world of health. As a result, new ways on how health research is conducted and performed are beginning to emerge. Major Contract Research Organizations or CROs are starting to employ AI in pre-clinical tests, thus revolutionizing the role of technology in healthcare.

Artificial intelligence is a type of intelligence displayed by machines and computer systems. Nowadays, there are several ways how pre-clinical CROs use AI in their studies. But, first, what are pre-clinical CROs?

Pre-clinical CRO Defined

Pre-clinical CROs, otherwise known as Pre-clinical Contract Research Organizations, are companies that provide knowledge, skills, and experience needed to transform a medical or pharmaceutical idea concept into a final product. There are a lot of processes involved before the final product is revealed, which include the discovery and development stage, pre-clinical research stage, the clinical research stage, and, lastly, the FDA review.

The period between pre-clinical tryouts and the unveiling of the product is where the role of a pre-clinical CRO is most critical. Drug ideas and prospective products may fail within this period; hence modern pre-clinical CROs, like Ion Channel CRO, continue to dig deeper into the capacity of future technology to increase efficiency in health research.

Reasons Why Pre-clinical CROS Are Using Future Technology/AI To Conduct Health Research

1. Reduces uncertainty in pre-clinical experiments – AI is now being used to reduce the improbability that comes with pre-clinical trials. This will go a long way in reducing time spent on research, cutting down financial costs, and optimizing data gathering.
2. Gathers data and obtains actionable insights – Researchers now use AI to streamline data collection and selection of recipients of pre-clinical tests. Data collection and analysis are an integral part of health research, and keeping up with the zillions of data available is impossible for the human researcher. However, with the aid of AI tools, such as deep learning and machine learning, it is possible to analyze, select patterns, and connect relevant data that can lead to drug discovery.

Researchers also make use of reports generated by AI to gain actionable insights during their pre-clinical studies. AI tools can also improve recipients’ selection by choosing the most appropriate group capable of responding to pre-clinical research and tests.

1. Automates cell selection and analysis — With the aid of future technology, modern cell based assays allow researchers to identify problems with potential drug products at an early stage. This process reduces wastage and streamlines the development process. Data collected by AI during research can also be useful during clinical tests by matching the best possible patients.
2. Automates the process of pre-clinical image and sample analysis — There are also efforts to use future technology, such as machine-based learning, to automate sample analysis. This process includes using such technology to analyze patterns and identify molecular compounds for drug discovery. For instance, the Institute of Cancer Research uses AI technology to make predictions about new prospects for cancer drugs. Similarly, researchers have successfully developed an AI Robot, named Eve, specifically designed to make the process of drug discovery faster.
3. AI also performs repetitive tasks, such as updating research records and extracting data.

Limits of AI in Health Research

 The use of AI in health research is no longer a strange concept, but its total adoption is still far off. Some of the limitations of AI in health research include:

• The use of AI in health research raises ethical concerns, such as the inability of AI to have human empathy and characteristics, which include the ability to read social cues and contextual knowledge. There is also the potential for grave mistakes and the question of AI making decisions concerning life and death. Other ethical concerns include protection of sensitive data and the possibility of AI to be used for dubious purposes.
• There is also the possibility of AI making bias decisions based on the bias inherent in training programs of the AI.
• There are also concerns about the effects of the adoption of AI on the roles of health professionals.
• A primary limitation of the use of AI in health research is the level of data management currently available. The inconsistencies and quality of data available confine the potential of AI.
• Lastly, the question of accountability is often a concern to many. Owing to the complexity of AI methods, it is difficult to monitor and evaluate bias or error in AI outputs.

Conclusion

Despite making some head way in the health sector in recent times, AI technology still has a long way to go in terms of output and adoption. Fortunately, CROs are unrelenting in their efforts to incorporate AI technology in pre-clinical health research. While initial results have been slow, the future of AI in health research looks promising.