Vox Pop Pharma – where will AI take the industry in the next five years?

By Jonathan Smith

- Last updated on GMT

Vox Pop Pharma - where will AI be in five years?

Related tags AI Data management Artificial intelligence machine learning Research

Artificial intelligence (AI) is a hot topic in pharma with myriad uses such as supporting clinical research and empowering data analytics.

Industry leaders give their takes on what else is in store for AI-based tools in the pharma space.

AI-powered algorithms have taken many industries by storm and pharma is no exception. For example, many companies are using AI to empower drug discovery​ and the search for disease targets by crunching reams of literature, helping human researchers to find potential molecules in months instead of the years that they would take otherwise.

AI can also be deployed to help healthcare providers to better understand disease pathology and improve treatments such as in the case of tumors​. Yet other uses include speeding up data analysis​ and helping with the design of clinical trials

Where does the industry want to see AI making a difference next? And what are its challenges? Let’s hear what key opinion leaders have to say.

Humaira Qureshi, President of Qinecsa, says:

“Advancements in AI are causing positive disruption in efficacy around clinical trial research. The ability to process higher volumes of data more quickly to support efficient decision making is ultimately protecting patients. However, we still need to understand what the acceptable tolerance level for AI-only activities is and how much human intervention will still

Humaira Qureshi- President of Qinecsa Solutions- Headshot

be required.

“The need for a cultural transformative mindset is just as important as the AI itself. Teams and organizations need to embrace AI as an opportunity rather than a threat. The industry needs to come together to support acceleration in areas of data collection and processing which can ultimately benefit patient safety.

“I also want to see more education on AI and its impact on life sciences, patients, regulators and quality. This includes educating patients and consumers on not only AI data collection but also the outputs which have been processed through it, so they understand the wider benefits.”

Vincent Keunen, Founder and CEO of Andaman7, says:

“Patients need tools which help them to feed into research and understand clinical information. AI is already being used in practical ways to help empower patients in this way. For

Vincent Keunen- Founder and CEO Andaman7

example, by simplifying medical jargon, summarizing and translating complex medical documents. When patients understand better, they can contribute better to clinical research.

“The next step is to extract relevant health information from documents and other unstructured formats and codify it into largely adopted health standards with the help of AI. This would have a wide range of applications, including better data quality and facilitating participant recruitment by identifying candidates based on inclusion and exclusion criteria.

“AI can also play an important role in summarizing all the information available in the electronic health record of a specific patient, to feed into research. Finally, interoperability (data access) can also be significantly improved with advanced AI processes.”

Graham Clark, CEO of Phastar, says:

“In data management, we are already seeing how AI can be used to power data review, risk management, and enable automatic coding through interactive data monitoring platforms. Increasing access to user-friendly visualizations increases stakeholder engagement and frees up time to gain deeper insights and address anomalies.

Graham Clark- CEO Phastar

A further example is a medical monitoring app that provides a holistic view of each patient/site/region through consolidated data mapping, backed by intuitive visualizations, empowers medical monitors to make data-driven decisions, improving the likelihood of successful trial outcomes.

In the future, we expect AI to facilitate more optimized clinical trials such as automating the preparation of clinical trial reports for submission, enhancing patient recruitment processes, monitoring patient adherence and safety in real-time, analyzing large datasets for pattern recognition, and personalizing treatment plans based on individual patient data. These advancements will significantly improve efficiency, accuracy, and outcomes in clinical research.”

Sebastien Coppe, CEO of One2Treat, says:

“AI offers an opportunity to leverage diverse data sources to inform more patient-centric trial designs by eliciting patients' preferences regarding the most important outcomes and their priorities, which are then incorporated into the primary endpoint.

Sebastien Coppe - CEO One2Treat

“AI-driven trial design needs to combine patient preferences with transparent, easily understandable, and clinically meaningful study objectives. This straightforward approach facilitates approval by regulatory authorities.

“The Net Treatment Benefit, which integrates multiple criteria within a single primary endpoint, assesses treatment effects in a more holistic way than current approaches, often reducing sample sizes in clinical trials. This is critical for the Biopharma R&D industry as it shortens clinical research timelines and provides each patient with the treatment that best meets their needs.

“In the future, we expect this end-to-end, patient-centered approach (i.e., starting with patient preferences, developing and marketing the best new treatments, and allowing patients to identify the most suitable treatment for their individual needs) to be further adopted and enhanced as AI methodologies continue to improve.”

Patrick Hughes, Chief Commercial Officer & co-founder of CluePoints, says:

“AI is already helping to ensure quality management and compliance across clinical trials. Risk-Based Quality Management platforms now offer AI and machine learning methodologies integrated with advanced statistical algorithms to flag potential issues with quality in clinical trials and provide suggested resolution steps.


“Recently, CluePoints expanded this use to introduce applications that complement this unsupervised risk detection. For example, our AI and machine learning-driven medical coding module removes the need for first-line human intervention from medical reviewers and labor-intensive synonym library updates, guiding researchers to the correct term in seconds with up to 99% accuracy.

“In future, we predict AI will continue to contribute to quality management in clinical trials. However, it is vital that AI does not become just an umbrella term for anything ‘clever’ and also that we don’t take humans completely out of the loop. We need to make sure developments focus on areas where AI can make a real difference to quality by disrupting traditional, manual workflows and supporting human intervention and critical thinking. Our aim, at CluePoints, is to turn artificial intelligence into human intelligence.”

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