TL;DR:
- AI is set to play a significant role in the realm of drug development due to its ability to unlock advanced analytics, automate processes, and increase speed.
- The clinical trials landscape is undergoing a transformation, influenced by the ongoing Covid-19 pandemic, geopolitical uncertainty, and environmental pressures.
- Sponsors are looking for solutions that maintain quality and safety while optimizing every stage of the clinical research process.
- AI offers a solution to optimize clinical trial patient recruitment and management by gathering subject information, screening and filtering potential participants, and analyzing data sources.
- AI offers a solution to accelerate clinical trial study build by streamlining the study build process, handling structured and unstructured native documents, and automating querying and medical coding.
- The implementation of AI in clinical research has the potential to revolutionize the drug development process and shape future research by suggesting better study designs.
Main AI News:
As the pharmaceutical industry continues to evolve and face new challenges, Artificial Intelligence (AI) is poised to play a significant role in the realm of drug development. With its ability to unlock advanced analytics, automate processes, and increase speed, AI is set to disrupt the clinical trial value chain in 2023.
The clinical trials landscape is undergoing a transformation, influenced by the ongoing Covid-19 pandemic, geopolitical uncertainty, and environmental pressures. At the same time, the introduction of innovative treatments and the increasing complexity of trials, driven by adaptive design and personalization, has heightened the need for greater agility and faster time to commercialization. To meet these demands, sponsors are looking for solutions that maintain quality and safety while optimizing every stage of the clinical research process.
In this whitepaper, digital technology solutions provider Taimei examines the impact of AI on the clinical trials of today and delves into its potential to shape the future. This comprehensive analysis provides valuable insights into the role of AI in improving the efficiency and effectiveness of the clinical trial process. Get ready to discover the full potential of AI in revolutionizing the pharmaceutical industry.
Optimizing Clinical Trial Patient Recruitment and Management with AI
Clinical trials are a crucial component of the drug development process, and patient recruitment and management are two key areas that can significantly impact the success of a trial. According to Scott Clark, Chief Commercial Officer at Taimei, the biggest delays in clinical trials often occur during patient recruitment, site start-up, querying, data review, and data cleaning.
Patient recruitment, in particular, is a time-consuming and crucial stage of clinical trials. Sponsors must identify and select a suitable set of participants, using a range of inclusion and exclusion criteria to ensure that the trial is conducted with high-quality results. However, patient recruitment is not just about finding the right participants. Effective patient management is also vital, as patient retention can directly impact the quality of the trial’s results.
Fortunately, AI offers a solution to these challenges, providing a more efficient and effective approach to patient recruitment and management. AI algorithms can be used to gather subject information, screen and filter potential participants, and analyze data sources such as medical records and social media content to detect relevant subgroups and geographies. Additionally, AI can alert medical staff and patients to clinical trial opportunities, leading to faster and more efficient patient recruitment and increased quality and retention.
Accelerating Clinical Trial Study Build with AI
Clinical trials require a significant amount of time and resources to build, and the process can be laborious and repetitive. Data managers are typically tasked with reading the study protocol and generating a large number of case report forms (CRFs), each of which has different requirements. This process can take weeks, affecting the accuracy and quality of the trial.
However, AI offers a solution to these challenges, streamlining the study build process and freeing up data managers’ time. Automated text reading enables the AI to parse, categorize, and stratify corpora of words, automatically generating eCRFs and the data capture matrix. AI can then use the data points from the CRFs to build the study base and create the entire database in a matter of minutes rather than weeks.
Additionally, optical character recognition (OCR) allows AI to handle structured and unstructured native documents, reducing the study build a timeline from ten weeks to just one. The use of AI also facilitates the analysis of data, develops all required tables, listings, and figures (TLFs), and even provides conclusions pending review. With the ability to perform up to 168% more edit checks than the manual process, AI can also automate remote monitoring to identify outliers and suggest the best course of action.
Once the trial is launched, AI can be used to automate querying and medical coding, flagging any data that doesn’t make sense and providing suggestions for correction. This eliminates the need for manual data cleaning at the end of the trial and saves time for data managers, who simply review what the AI has corrected.
From Concept to Reality: Navigating the Implementation of AI in Clinical Research
The implementation of AI in clinical research has the potential to revolutionize the drug development process, but getting there requires careful planning and execution. From establishing the proof of concept to building a custom knowledge base and training the model on large amounts of data, each step must be taken with precision to ensure accuracy and eliminate bias.
One of the key advantages of AI in clinical research is its ability to automate repetitive tasks, freeing up human personnel for more specialized work. This acceleration of the time to market for life-saving drugs is made possible through the use of APIs that integrate best-in-class advances into clinical trial applications.
Moreover, AI can also play a critical role in shaping future research, using machine learning to suggest better study designs based on the analysis of past and present trial data. In the long term, the implementation of AI in clinical research has the potential to shift the focus from trial implementation to drug discovery, leading to improved treatments for patients in need.
Conlcusion:
The integration of AI in clinical trials has the potential to significantly impact the pharmaceutical industry. By automating processes, increasing efficiency, and optimizing patient recruitment and management, AI has the ability to revolutionize the clinical trial value chain. The implementation of AI in clinical research has the potential to accelerate the time to market for life-saving drugs, improve the quality of results, and shift the focus from trial implementation to drug discovery.
This presents a significant opportunity for companies in the AI and digital technology solutions space to capitalize on the growing demand for AI-powered solutions in clinical trials. Companies that can offer innovative and effective AI solutions will be well-positioned to capture a significant share of the market and drive growth in the coming years.
