Artificial intelligence is increasingly becoming a part of our everyday lives—every time we pick up our phones and Google something, AI results now show up at the top of the page. Besides compiling lists of the best restaurants and cleaning tips for us, AI is showing promising applications for a wide range of industries from retail to finance to healthcare. 

Of course, the pharmaceutical industry is no exception. Researchers have been applying AI models since the start to advance their understanding of disease; however, its application across all aspects of development, production and marketing has exploded over the past year–the McKinsey Global Institute (MGI) estimates that Generative AI–which creates original content based on existing data–could generate $60 to $110 billion a year in economic value for the pharmaceutical and medical-product industries. 

MGI’s January 9, 2024 report, “Generative AI in the pharmaceutical industry: Moving from hype to reality,” which draws on data from 63 generative AI use cases in the life sciences, lays out the potential economic impact of generative AI on five pharmaceutical industry domains:

• Research and early discovery

• Clinical development

• Operations

• Commercial

• Medical affairs 

Beyond the financial payoff, the report breaks down how generative AI will accelerate discovery and improve patient outcomes, leading to “unquantifiable effects on human health and well-being.” However, while the benefits are seemingly endless, it admits that challenges still exist when it comes to implementing and scaling such fast-changing technology. 

Generative AI hype vs. reality

Many industry leaders who are tasked with making strategic decisions still have a steep learning curve, the report reveals. So it breaks down four generally held misconceptions about generative AI, offering up the “reality” to counteract the “hype.” Out of four, Revisto, which harnesses the power of AI to streamline the MLR (Medical, Legal, and Regulatory) review process, offers our take on two of them. 

1. The first is the belief that “Selecting the right large language model (LLM) will be a key strategic differentiator.” Given the complexity of the pharmaceutical industry and its data and regulations, generative AI models need to be adapted to a company’s internal knowledge base and use cases. To succeed, companies must integrate generative AI across complex workflows in order to encourage its adoption and maximize its impact.

Revisto agrees that this approach is most effective. Our model learns a company’s specific requirements and preferences, and using data from its brands, it’s able to quickly pair content with approved claims to identify discrepancies. The platform is also able to seamlessly integrate with a company’s existing workflows and CMS software to minimize disruptions. Furthermore, given the importance of accuracy in MLR, the model needs to be targeted and fine-tuned for this specific purpose to eliminate the risk of hallucination.

2. The report also debunks the myth that generative AI “will instantly affect every part of the organization.” It recommends that companies apply it only where it makes sense for overall business goals, using a “2x2 approach”–that means beginning with two cases that can generate excitement and have a more immediate impact, along with two cases that have the potential to be transformational in the long term.

Reviso agrees here as well. Given how time- and labor-intensive the MLR process is, implementing Revisto’s platform can quickly make an impact on a company’s bottom line. Not only that, given how easy it is to use and that it replaces tedious manual updates, it can make the process much more efficient and enjoyable for team members. 

Generative AI’s impact on commercial processes

As mentioned above, the report breaks down the economic impact of generative AI on five pharmaceutical industry domains. Commercial, which includes functions like content creation, MLR review and patient experience optimization, could alone have an expected value of $18 to 30 billion.

Specifically regarding MLR review, the report outlines how generative AI streamlines the process including: flagging potentially problematic language, reusing previously approved language and materials, and speeding up the approval process by keeping everyone in the loop. It concludes that automating these tasks could result in: A two to three times acceleration of the content approval process.

Revisto estimates our platform has the potential to speed up the MLR process by 5-10X, since it can immediately identify the majority of potential findings, while continuing to learn and improve from user actions and preferences.

How Revisto is leveraging the power of generative AI

Revisto is at the forefront of incorporating generative AI into MLR review, accelerating time-to-market without sacrificing accuracy and quality. It streamlines the entire process–flagging potential issues early on, providing timely content suggestions, and facilitating collaboration–which results in materials being approved and released more quickly. 

The results are significant. Companies can begin recouping their losses and generating revenue, putting them on a faster track to financial stability, while patients can get the care they need. 

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Learn how generative AI can streamline your MLR review process. Request a demo today.