According to research from the McKinsey Global Institute, generative AI is estimated to produce up to $110 billion a year in economic value for pharmaceutical and medical device industries. This profit will largely come from enhancements to innovation and efficiencies – everything from drug discovery and development to marketing them to HCPs to boosting employee productivity across the organization. With promises like this, it’s no wonder that Deloitte reports nearly 60% of life sciences executives plan to increase their investments in AI-enabled solutions.
These same executives cited pricing and access to drugs and medical devices as the most significant issues facing the life sciences industry. It’s safe to say that this innovative technology has arrived at the perfect time. So, what are the top AI use cases that can help organizations realize these benefits? Let’s discuss the top 8.
Drug Discovery & Development
The average R&D phase in life sciences can run up to 15 years and cost $1 to 2 billion for a single product to make it to market. AI’s predictive modeling capabilities can save organizations time and money, speeding up the processes and offering new levels of accuracy in the analysis of biological and chemical data to ensure the safest compounds are chosen. Machine learning, such as Deepmind’s AlphaFold, can then design drug molecules from scratch. This technology aids in analyzing protein structures, pinpointing potential binding sites and discovering viable drug candidates. AI can also assist in the design of medical devices by fine-tuning their features and functionality and forecasting performance to enhance the likelihood of success.
Clinical Trial Management
AI can streamline the critical trial process in a few ways. On the front end, it can quickly sift through data to identify the ideal patients and even predict possible drug interactions. AI technology even has the ability to help in the design of trials, determining protocols such as patient dosage, for the best outcomes possible. Throughout the duration of the trial, AI can help teams process and analyze a vast amount of data, identify key trends and insights as well as any adverse effects or potential compliance risks that need to be addressed. The likes of AI-backed chatbots and virtual assistants can work with patients and healthcare providers to maintain schedules and offer round-the-clock support to keep the process moving forward.
Pharmacovigilance
Similar to what is done during the clinical trial phase, but on an ongoing basis, this is the act of monitoring and assessing adverse side effects and other issues. It’s typically done manually which makes it prone to human error. AI can improve the accuracy of and automate this highly important oversight process by analyzing data from electronic health records, clinical trial data and even patient-led forums, including social media. It can then swiftly help make sense of all of these moving parts so teams can take action and predict potential issues to mitigate risks.
Manufacturing & Supply Chain Management
Ensuring the consistent and compliant production of therapeutic products is critical for manufacturing and supply chain management teams. AI will increase efficiency and accuracy in this area, offering real-time insights into the status of key processes such as procurement, logistics, inventory and distribution. This allows companies to deliver safe products to patients and healthcare providers in a timely manner and reduce the risk of recalls. More streamlined predictive analysis enables teams to forecast supply needs and coordination among multiple suppliers. AI can also help companies address another area of more recent focus, sustainability, by tracking their waste so they can make more environmentally friendly decisions.
Regulatory Compliance
Pharmaceutical and biotech companies face some of the strictest standards when it comes to regulatory compliance. To appease the FDA, EMA and MHRA and, most importantly, keep patients safe, they must implement a slew of constantly evolving strategies across the organization and provide proof that they are being meticulously followed. These folks can leverage AI to intercept global regulatory changes and new guidance as it becomes available. With access to real-time information and swift execution, organizations can practice better risk management, be more prepared for audits, deliver better patient outcomes and ultimately avoid legal, financial and reputational ruin.
Commercial
Those in the life sciences space understand the healthcare system’s push toward personalized care and have been restructuring their sales and customer engagement approaches to better support HCPs in this effort. Commercial teams, who once played the role of the traditional sales rep, must pivot to become expert educators in disease states, clinical trial data and other more nuanced areas. AI can aid in research (ie. each doctor’s patient populations and historical methods of practice) ahead of meetings. It can also be used in the development of individualized plans for each provider they consult with. And since reps are now responsible for knowing more (much more!) than just product knowledge, constant training is essential. Microlearning platforms allow you to quickly disseminate bits of information to reps in the field so they’re always up to date – and then reinforce that knowledge over time. Tools with real-time analytics even quickly call out knowledge gaps for swift remediation and risk prevention.
Medical Imaging & Diagnostics
AI can analyze CT scans, MRIs, and similar imaging data with impressive accuracy, significantly speeding up the processing of clinical trial results and helping bring products to market faster. During the R&D phase, AI imaging can highlight disease progression and identify novel biomarkers ahead of drug or product design. It can also aid in early disease detection, sometimes before it is clinically apparent, allowing for earlier interventions. Additionally, AI enhances the precision of imaging data interpretation, offering a deeper understanding of how diseases evolve and respond to treatments. This helps clinicians make more informed decisions, reducing human error and leading to better outcomes. Finally, AI automates the selection of the best-fit patient populations for clinical trials, ensuring a more targeted approach that boosts trial efficiency, improves results and accelerates regulatory approvals.
Change Management
It’s no question that AI can be used in a variety of ways across life sciences organizations to boost productivity, reduce costs, and drive more revenue. But with big change comes an even bigger responsibility: ensure the workforce is fully embracing that change and is educated on doing so responsibly. While ethical AI is crucial across all industries, it is especially vital in life sciences, where employees frequently handle sensitive client data and must adhere to rigorous compliance standards. Many organizations have brought in a team dedicated to AI. Whichever strategy and tools you decide to implement, company-wide training is essential.
Microlearning is scalable for large, global organizations. Short, engaging Q&A style challenges are delivered in the flow of work to increase knowledge retention and build job-critical skills. Training teams, AI leaders, or whoever is in charge of educating on AI gets access to real-time proficiency data, allowing them to intervene before costly mistakes are made.
In all honesty, microlearning and knowledge reinforcement solutions like Qstream can be used to train employees on all of the above. Each department in a med device or pharma organization requires a deep understanding of complex information and processes to effectively carry out their roles. Qstream effectively trains these employees in just minutes a day without compromising their productivity, ensuring they are up to date on job-pertinent information and have the skills needed to drive success.
In summary, AI has the potential to revolutionize multiple processes within the life sciences industry. This is an industry where time is of the essence, but also one where attention to detail is critical in bringing new therapies to market. AI offers the best of both worlds, boosting efficiencies across the lifecycle while maintaining a high level of accuracy in its outputs. As with any technology, however, human intervention is key and should be implemented at various stages as a catchall.
Did you know Qstream has its own AI capabilities? Watch this video to see how our AI Microlearning Content Generator helps you launch training in a matter of minutes.
Full Source List
- https://www.linical.com/articles-research/how-ai-is-revolutionizing-clinical-trials
- https://www.nature.com/articles/d41586-024-00753-x
- https://petrieflom.law.harvard.edu/2023/03/20/how-artificial-intelligence-is-revolutionizing-drug-discovery/
- https://www.sciencedirect.com/science/article/pii/S135964462400134X
- https://www.apexon.com/blog/transformative-power-of-ai-and-intelligent-automation-in-pharmacovigilance/
- https://www.qcsstaffing.com/blogs/2023-9/how-is-ai-revolutionising-pharmacovigilance
- https://www.pharmiweb.jobs/article/how-ai-is-transforming-regulatory-affairs
- https://www.qordata.com/using-artificial-intelligence-to-manage-life-sciences-compliance
- https://www.prp-us.com/quality-compliance-blog/ai
- https://pmc.ncbi.nlm.nih.gov/articles/PMC11120567/
- https://www.omdena.com/blog/top-biotech-companies
- https://www.medpace.com/wp-content/uploads/2023/03/Whitepaper-Artificial-Intelligence-Can-Boost-Reliability-and-Speed-of-Medical-Imaging-Analysis-in-Clinical-Trials.pdf
- https://www.iconplc.com/insights/blog/2024/06/14/rise-and-role-ai-medical-imaging
