To Transform Healthcare and Life Sciences, AI Must Be Trustworthy

Artificial intelligence (AI) is rapidly becoming embedded across healthcare and life science organizations. Yet most organizations are using it in pockets rather than scaling it to materially improve performance across the enterprise. Among the challenges: AI in these industries must meet the highest standards of quality, privacy and reliability, and it must be trustworthy.

Large language model (LLM)-based AI tools are powerful, but most LLMs are not designed for the demands of healthcare and life science operations. They can produce inconsistent outputs, and their performance can vary as information and context change. General-purpose AI in particular is trained on broad, public data – with limited medical curation – and not built to meet medical, scientific or regulatory requirements.

These issues are unacceptable in operations where decisions have not only financial but also clinical, scientific, legal and ultimately human consequences.

The bottom line: A higher standard of AI is needed.

If healthcare and life science organizations want to use AI to transform their commercial and regulated operations, they need AI that is trustworthy.

What’s needed to create trustworthy AI

Trustworthy AI produces reliable results, performs consistently as data changes, and is compliant and defensible.

Achieving this requires both scientific and technical expertise, as well as a rigorous approach that considers every facet of responsible AI design, use and monitoring. What does this look like in practice?

The first step is to understand the end goal: What is the end user requirement that the AI solution must address, and what does success look like? This involves understanding the roles of those who will use the AI solution, their needs and workflows, and either the commercial goals they want to achieve or the regulatory requirements they must comply with.

These details will help inform key technical decisions, such as choosing the appropriate models for the AI solution, designing validation frameworks and establishing the metrics that the solution will be measured against.

Trustworthy systems also consider the expert in the loop right from the beginning of the design process, not as an afterthought. This involves using human experts – including clinical, scientific, regulatory and commercial experts – to help make sure the AI solution is designed and deployed correctly and to consider how the solution will impact an end user’s work.

Of course, trust isn’t just earned at the design stage – it must be maintained throughout the life of the AI solution. Mechanisms like AI data flywheels, or learning loops that continuously update models with new data to keep them current, help AI solutions remain relevant, accurate and trustworthy. Reinforcement learning and guardrails programmed into AI solutions can also help keep their performance on track within a defined set of rules.

Real-world applications

AI is already being embraced and trusted and making an impact in real-world use cases for some of the world’s largest life science companies.

In one case, a leading pharma company sought to improve how it engaged healthcare professionals (HCPs) across multiple brands and markets. The company’s ability to engage HCPs and optimize marketing strategies was hindered by challenges such as data-management issues, a lack of customer-level insights and adaptation difficulties.

The company implemented an omnichannel engagement solution. It combined predictive signals for HCP engagements with “next best action” recommendations that helped teams decide how to pace outreach and what follow-up actions to take. The company saw a fourfold improvement in its ability to identify high-value patients, along with 20% and 36% increases in new patient initiation for two of its brands.

Another example is in literature reviews required for drug development. Conducting these reviews can take months and require deep domain expertise, meticulous planning, significant manual effort and more. They can also be difficult to scale and susceptible to errors.

AI solutions can automate major portions of literature reviews, from protocol development to searching and screening, data extraction, and analysis and reporting. For whatever work the AI solution takes on, researchers or others can review the logic behind every decision.

Now with AI, reviews that once took months can be completed in just days and with fewer errors. In one case, an AI solution helped a large pharmaceutical company achieve an initial screen for a scientific-literature-review use case seven times faster than the traditional manual process. This condensed the estimated screening time from 20 days to less than three days.

AI is also creating new possibilities in this field. For instance, it has allowed companies to create “living” reviews that can be continuously updated with the latest published data.

Collaboration is essential

Creating trustworthy AI solutions for healthcare and life sciences requires a blend of expertise that no one organization can provide on their own. This is why like-minded companies are collaborating, bringing together the technical and domain know-how and capabilities needed to create complete, validated AI systems that can scale across both regulated and commercial workflows.

The right technical partner, for instance, brings engineering depth and extensive experience to deploy and run AI at enterprise scale. They can deliver open models to provide the transparency that trustworthy AI needs and software components that enable faster AI solution building. And their experience creating trustworthy enterprise AI solutions for other industries can help them anticipate challenges and strengthen designs.

On the domain side, an effective collaborator brings not only deep clinical-development and commercialization expertise, but also a proven track record of developing trustworthy AI solutions. They have the essential ingredients needed to create these solutions, like data science expertise, regulatory knowledge and a history of safe and responsible data use. But they can also offer more to support AI deployments, from a willingness to challenge public benchmarks to help make sure an AI solution performs as expected, to resources like forward-deployed engineers who can help integrate AI solutions into end users’ workflows, taking into account the end user’s unique IT system configurations and policies.

Changing how work gets done

AI isn’t just another tool for healthcare and life science organizations. Done right, it changes how work is performed and how problems are solved. Trustworthy AI in particular is already proving it can shorten timelines, improve accuracy and help teams more nimbly tackle complex challenges, reimagining workflows for the AI era.

As AI shifts from generating insights to making decisions and executing complex workflows, organizations that embrace this evolution will be able to unleash new operating models that make them more efficient, more informed and more responsive to rapidly changing demands in healthcare and life sciences.