How Artificial Intelligence is Transforming Clinical Research

Artificial intelligence (AI) systems are powering rapid advancements in different sectors of healthcare, including clinical research and precision medicine. Researchers are relying on AI through the entire process of clinical research, from proposal development, subject recruitment, to the dissemination of results and the introduction of new drugs to the market. The global AI market size for healthcare has grown rapidly in the last decade. From an estimated value of close to $5 billion in 2020, the market is expected to reach $44.5 billion by 2026. AI increases efficiency, eliminates human errors, and therefore boosts the validity and reliability of clinical research. It is no surprise that huge investments are being made to support AI solutions for clinical research.

Technology has been a key driver for the latest developments in modern medicine. By increasing the efficiency and the accuracy of clinical research, AI has led to the discovery of novel treatment therapies for difficult-to-treat conditions. The combination of AI with machine learning and other tools used in precision medicine are proving indispensable in the fast-paced environment of cancer research.

Let us see how AI is transforming clinical research.

Clinical Researchers Are Turning to AI to Improve Patient Outcomes

Clinical researchers often encounter numerous challenges due to the nature of their work. For starters, they are often hard-pressed for time to deliver results that will lead to a better understanding of new diseases and the development of new and better drugs to treat diseases. At the same time, their work is guided by strict protocols and regulations to protect the safety and privacy of participants and to assure high-quality data for effective data-driven decision-making. AI bridges the gap between the rush against time and the elimination of human errors and adherence to Standard Operating Procedures (SOPs) and regulations.

AI is based on algorithms that are generated from large data sets and are used to make sense of imputed information. For example, when certain symptoms are keyed into an AI system, the output can be a diagnosis and treatment options. In clinical research, artificially intelligent systems can be used in the following ways:

1. Participant Recruitment

This is usually one of the most challenging aspects of clinical research, especially when a researcher is strained for time and resources. Researchers can leverage AI to select participants from patient medical records or pathology reports based on specified criteria. This is achieved through the mining of large datasets of patient information to identify potential candidates that qualify for research enrollment using mathematical modeling and neural networks. AI tools help identify which participants may respond better to drug treatment and also forecast dropout rates. This can increase enrollment, save on resources, and boost research accuracy and validity.

2. Speed up Drug Development

AI plays an important role in the discovery and introduction of new and improved drugs. The process of introducing new drugs to the market is long and involves stringent requirements. This is usually broken down into four distinct stages from the early drug discovery to the final approval stage. In the US, it may take up to fifteen years and about $12 billion before an experimental drug finds its way to your medicine cabinet at home. Worse still, you are battling with the odds of the drug getting rejected at the final stage. When you are thinking of cancer, this can be very devastating. That’s where AI comes in.

AI simply speeds up research and brings precision to each step of the process. By minimizing errors and increasing efficiency, AI improves the probability of drug approval and makes the discovery of newer drugs both cheaper and faster. AI also helps determine optimal dosing regimens of drugs for maximum effectiveness after administration in a patient’s body. However, deploying AI in clinical research can be a slow and labor-intensive process. AI can increase opportunities for drug manufacturers to bring more drugs to the market while incurring fewer costs.

Regulatory and Other Challenges in Deploying AI in Clinical Research

Regulatory hurdles can derail the adoption of AI tools in clinical research. There are issues related to AI being untested and therefore unproven in some spheres of clinical research. There are also issues that touch on privacy, oversight, and ethical dilemmas.

Another significant challenge is the curation of quality representative data sets to be used in the development of AI algorithms. Lastly, the lack of broad acceptance of AI systems and cost implications are some factors to be considered. As much as deploying AI systems can be costly and time-consuming, researchers can reap the results for a long time and therefore justify the initial costs incurred.

A Laboratory Software for Clinical Research Labs Bridges the Gap Between AI and Clinical Research

A laboratory software for clinical research labs, also known as Laboratory Information Management System (LIMS), plays an important role in facilitating the adoption of AI-powered clinical research. A LIMS serves as a repository of clinical research data and provides a unique opportunity for AI to mine high-quality data. Furthermore, AI enables researchers to use predictive modeling techniques to improve the statistical significance of data collected from research participants on a LIMS platform. A LIMS also supports regulatory compliance and therefore supports the adoption of AI systems. A laboratory software for clinical research labs automates workflows and can be integrated with predictive modeling tools to facilitate the recruitment of research participants, predict research outcomes, and zero in on the right drug candidates.

Conclusion

AI-powered technology and tools have the potential to redefine clinical research operations and are likely to govern the future of clinical research. AI has significantly transformed clinical research and hence patient management. It plays a key role in participant selection and the drug development process. Regulatory challenges, initial cost implications, and ethical considerations are some of the challenges that may derail AI adoption. However, a cloud-based laboratory software for clinical research labs minimizes some of these challenges and facilitates the rapid deployment of AI in clinical research.