Updated October 2024
Artificial intelligence (AI) for medical affairs is creating disruption and opportunity. Medical affairs teams and leaders know they will need AI to remain competitive in their respective races to regulatory approval, market adoption, patient access and outcomes improvement.
“The playing field is poised to become a lot more competitive, and businesses that don’t deploy AI and data to help them innovate in everything they do will be at a disadvantage.” – Paul Daugherty, Chief Technology and Innovation Officer, Accenture
When it comes to the regulatory environment, AI is outpacing regulators’ ability to ensure patient safety, especially in the clinical realm. The US FDA has published Guiding Principles for Good Machine Learning Practices to which pharma and medtech innovators would be well advised to adhere. And the EU’s groundbreaking law governing “the way companies develop, use and apply AI, was given final approval by EU member states, lawmakers, and the European Commission — the executive body of the EU — in May.”
It officially entered into force on August 1st, 2024 according to CNBC. It will almost certainly impact pharma and medtech innovators worldwide.
A note of caution: Medcity News recently reported, large language models “hallucinate” at levels likely unacceptable to clinical care provision. “In the 50 summaries produced by GPT-4o, the researchers identified 327 instances of medical event inconsistencies, 114 instances of incorrect reasoning and three instances of chronological inconsistencies.
But while AI in pharma continues to generate headlines, how, where, and when should teams deploy AI for medical affairs? These nagging questions remain a barrier to adoption and value maximization. In this discussion, we’ll focus on current applications and impact opportunities around AI for medical affairs to accelerate milestone achievement, patient access, and improved outcomes.
“[AI is] not a replace everything, replace the entire department with AI and technology [proposition]. Instead, consider optimizing one part of the workflow in the life science industry [especially for medical affairs]. – Jason Smith, Chief Technology Officer, Within3
Where can medical affairs use AI right now?
Data aggregation
Big data became a buzzword as the 21st century began, and the life science sector produces more big data sets than almost any other. Everything from research data to electronic medical records (EMRs), clinical trial data, and the information captured by wearable technology means mountains of new data daily – a blessing and a curse for life science companies.
Pharma companies use AI to identify new drug possibilities, process data sets that could produce precision medicine treatment modalities, and repurpose existing drugs. AI and machine learning could also help the pharmaceutical industry improve its sourcing and manufacturing processes to make them more efficient and reduce costs.
Where does this leave the big data element of AI for medical affairs?
There is a clear opportunity for technology that integrates data from disparate sources and breaks down unnecessary silos enabling medical affairs to act more quickly. But there remain challenges, especially in data types, quality, cleanliness, and intentions for that data’s utility. Many life science companies base critical business decisions on old or incomplete data – a problem that costs billions of dollars and wastes years of diligent work.
Given all that AI can do for pharma, the current state of AI for medical affairs is a promising opportunity to augment human expertise by extracting and capturing the most relevant data from stakeholders, ensuring its integrity, and then deciding what action to take based on insights generated from that data or information.
“Attempting to implement an AI model before the data is ready wastes time and resources. Data challenges leading to poor or biased models can impact the industry’s confidence in the potential of AI to deliver business value,” said Jason Smith, Within3 CTO, in InsideBigData. “To succeed in training and deploying AI models, life science companies need to develop a clear data strategy and spend sufficient time cleaning and harmonizing their data.”
Team collaboration, meeting or event transcription and summarization
Natural language processing (NLP) and tools like ChatGPT have widespread applications in many industries, and pharma is no exception.
While not all chatbots are equipped with AI, modern versions will likely use NLP to understand user questions and automate responses. Chatbots may be used to communicate with patients on healthcare service providers’ behalf, improve communication between doctors and patients, and make healthcare more accessible. Applications for chatbots in these settings include mundane activities like scheduling appointments or ordering medical supplies to more complex operations like providing information about conditions or symptoms.
Medical affairs teams use increasingly ubiquitous, publicly available tools, such as ChatGPT and Google Gemini, to summarize meeting notes, create agendas and outlines, and perform other tasks that do not involve putting proprietary or confidential information into public large language models.
However, AI designed for life science and trained on life science language models can perform more advanced tasks in the medical affairs team task list, with applications in gathering and analyzing market intelligence, moderating pharmaceutical advisory boards, and insights reporting. AI should and can bring you the “so what” and “what’s next,” rather than just presenting the word clouds and pie charts.
Market intelligence
Medical affairs professionals speak with researchers, physicians, and thought leaders worldwide. They capture high volumes of data, including rich observations from those interactions, from the boots on the ground and the leading edge of a given disease state or in a broader therapeutic area. However, 76% of medical affairs leaders say they frequently or occasionally encounter the challenge of “acting too late” based on time-consuming data analysis.
“AI can understand patterns in data that are likely to go unnoticed by humans,” says Within3 CTO Jason Smith. “Those patterns can more quickly, more clearly show us how to respond to what the market is telling us right now, or to improve provider or stakeholder education and patient outcomes.” Smith goes on to allay concerns about AI “taking over” the medical affairs function – something it is fundamentally incapable of doing. “AI is a supplementary, complementary assistant to medical affairs, not its replacement,” he says. “You’re not going to have AI bots going out and meeting with the doctors, capturing the data and analyzing it then reporting it back.”
Session moderation and KOL engagement
KOL engagement to gathering patient input, pharma teams use these gathered insights to inform strategic decisions during the drug commercialization process. These key medical affairs activities include elements that increase expenses, add to inefficient workloads, and are time-consuming, diminishing the efforts needed for more strategic work.
In another survey of medical affairs professionals, 72% named activities related to executing HCP and KOL engagement – including moderation, analysis and reporting results, and creating and sharing a summary of the full session – as the most time-consuming or labor-intensive aspects for group meetings.
Within3’s Moderator Assistant and insights reporting capabilities were developed specifically to create efficiencies and reduce expenses for medical affairs teams. The technology reduces workloads by up to 90%, freeing medical affairs teams to focus on strategic decision-making. Now, you can actually be efficient in the time you spend trying to understand what’s happening in those conversations from advisory boards and the notes from the field, then acting upon and sharing the insights that drive strategic decisions and accelerate milestone achievement.
Clinical trial design
The current clinical trial process is long-winded, highly expensive, and often ends in failure. But AI is already helping to make clinical trials more efficient, bring new treatments to market more quickly, and maintain patient safety.
By parsing EMR data, AI improves trial recruitment by identifying populations with the best chance of responding to treatment. AI is being used to replace placebo control groups with ‘digital twins’ of subjects for randomized clinical trials, allowing clinical teams to reduce the size of their control groups and minimize patient disruption.
AI applications are also being used to clean, aggregate, store, and manage clinical big data – helping clinical teams inform better site selection, optimize study designs, and accelerate the informed consent process. Predictive AI models can be used to calculate the results of a given treatment on a subject or group of subjects – potentially removing the need for animal testing and accelerating the preclinical phase. And finally, AI-powered wearable technologies are invaluable to clinicians as patient monitoring tools, enabling automatic detection of physical states.
Insights reporting and analysis
Employing AI to information from integrated data sources can eliminate up to 90% of the workload associated with data analysis and insights reporting. Teams can spend more time on their highest-value work rather than taxing and labor-intensive number-crunching.
Within3’s industry-leading group engagement applications with the AI-powered Moderator Assistant provide 3-7x more feedback than typical methods like web conferences or in-person meetings, dramatically reducing the time and effort required to monitor and analyze insights.
“Our goal is to remove 90% of the work and cost from the process of gathering, analyzing, and integrating insights while at the same time improving insight quality and decision-making.” – Lance Hill, CEO, Within3
Medical Congress Insights
One data source where the use of AI can be incredibly helpful is in the collection and analysis of congress insights. Med affairs team member responsibilities surrounding and during major medical congresses are intensive. They leave little time to monitor or track potentially high value conversations in attendees and concerned parties’ social feeds. You can’t be two places at once and your task at hand almost always requires a high level of focus.
Within3’s AI powered congress monitor “…captures real-time insights during medical congresses as the scientific landscape changes with new therapy filings and data releases. It allows medical affairs teams to understand how trends change over time and provide added value to directly related insight-gathering activities.
Putting it all together for insights generation and management
The goal of AI for medical affairs should be to help experts in the field make faster, more informed decisions. However, a broad goal of “using AI” is not straightforward or particularly strategic. Internal teams may encounter roadblocks like company policies that forbid most uses of generative AI, or from in-house tech teams that will spend months, if not years, developing comprehensive proprietary platforms.
An incremental, experimental approach to adoption is more practical for most teams, with tailored solutions at a lower cost of entry and a shorter time to proof of concept.
Modern insights management platforms use AI-powered natural language processing and sentiment analysis to help global users discover key concepts, sentiments, and trends from interactions with HCPs. The most effective platforms are designed for life science and trained on life science language models capable of more advanced tasks.
Within3’s Insights Management Platform includes a completely redesigned group engagement experience focused on further maximizing physician and patient participation. The new system is based on behavioral data from 70,000+ HCP and patient advisor users and Within3’s 15 years of partnership with the world’s top 20 pharmaceutical companies.
Medical affairs and its role as “the broker of insights”
Unlike some projected uses of AI in life science like imaging analysis, diagnoses and personalized or precision medicine, AI-powered insights management is an application delivering real, tangible value.
As the brokers of insights within their companies, medical affairs teams have a unique opportunity to be strategic partners to their cohorts in clinical and commercial teams. By delivering, managing, and streamlining AI-driven actionable field insights, Within3’s AI-driven insights management platform helps pharma companies and medical affairs teams unlock new market opportunities and make better-informed business decisions.
Start the conversation to find the right AI balance for your medical affairs team when you book a demo.
