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Home EMJ GOLD From data to decision-making: How AI will transform medical
affairs



FROM DATA TO DECISION-MAKING: HOW AI WILL TRANSFORM MEDICAL AFFAIRS

7 Mins
EMJ GOLD

AI IS BECOMING LESS OF A BUZZWORD AND MORE OF A KEY CONSIDERATION FOR THOSE IN
THE PHARMACEUTICAL INDUSTRY. FOR MEDICAL AFFAIRS PROFESSIONALS, THE BENEFITS OF
AI ARE GROWING – BUT HOW CAN THEY BE HARNESSED? 

INTERVIEW BY JADE WILLIAMS

In the dynamic realm of medical affairs, teams are tasked with a dual challenge:
adapting to evolving industry needs while maintaining their core mission of
educating and engaging healthcare professionals. At EMJ GOLD, we believe that
medical affairs can be a transformative force, and EMJ offers innovative tools
to help them succeed along the way, but AI is a road less travelled. To explore
its potential, we asked three experts to share their insights on how AI will
reshape medical affairs. Where will it have the greatest impact, and what are
the potential challenges to watch out for?

CONTRIBUTORS:

 * Carlos Eid, Executive Director, International Medical Affairs, CV & ASCVD,
   Novartis 
 * Ankur Sharma, Vice President, Head of Medical Affairs, Medical Devices &
   Digital, Radiology, Bayer  
 * Ludmil Alexandrov, Chief Scientific Officer, io9 

WHAT DO YOU SEE AS THE MOST IMPORTANT CURRENT APPLICATIONS OF AI IN MEDICAL
AFFAIRS?

Carlos: One of the most important applications of AI in medical affairs is data
analysis and the generation of actionable insights. AI can process big datasets,
including clinical trial data, real-world evidence, medical science liaison
notes and scientific literature, uncovering insights that previously took weeks
or months to obtain. This capability allows medical affairs teams to lead
cross-functional discussions and make more informed (and timely) decisions
around strategy and tactics. 

Additionally, AI is increasingly being used in KOL identification and
engagement, where it not only helps to identify the most influential experts but
also analyses their sentiments and communication patterns. 

Ankur: It’s about large language models (LLMs), right? The role of medical
affairs, clinicians and their thought processes – how they analyse data sets and
different points and then process them to produce an output that might develop a
new product, shorten a development cycle, or engage with external partners or
HCPs – is tricky and time-intensive. 

LLMs can change how you analyse and process data, allowing you to do it in a
shorter time frame, which is a big deal. Having a tool such as an LLM and using
it to query a theoretical idea can help you look at extra data in the space
you’re examining. It can help you decide if all this thing will improve patient
outcomes or drive innovation for the population. 

Ludmil: AI holds the promise to revolutionise medical affairs by enhancing
diagnostic accuracy, personalising treatment plans and improving early detection
and screening. 

The most immediate and impactful application of AI will be in diagnostics,
particularly cancer diagnostics. By utilising AI to predict disease
characteristics directly from histopathological slides, we can eliminate
expensive and time-consuming molecular testing. This approach will expedite
diagnosis and enable more precise, timely and personalised treatment decisions,
significantly improving patient outcomes. 

ARE THERE ANY MAJOR LIMITATIONS TO THEIR IMPLEMENTATION, AND IF SO, WHAT ADVICE
WOULD YOU GIVE TO COMPANIES LOOKING TO IMPLEMENT THEM?

Carlos: There are several limitations that come to mind. Data privacy concerns,
integration challenges, inaccuracies, biases and lack of talent/expertise. 

My advice to companies looking to implement them is to invest in robust data
security measures and ensure compliance with regulations like GDPR or HIPAA – or
partner with providers that have mastered this step and have these measures in
place. Second, build the right workforce with the right talents. Make sure you
have teams that understand the power of AI and can leverage AI effectively. 

Third, AI in medical affairs is like a high-performance sports car – it’s
powerful, but if you fuel it with bad gas (or in this case, bad data), you’re
not going anywhere fast – AI’s conclusions could be inaccurate and biased.
Therefore, treat your data like gold, invest in clean, representative datasets
and keep good human oversight on all AI outputs. Finally, start with small,
pilot projects to help integrate AI into your broader medical affairs
operations. 

Ankur: The limitations here are significant, especially regarding data access
and data rights – these are well-known challenges in healthcare. Data access is
difficult because of patient information and rights, and now we’re talking about
giving that data to an AI, which introduces even more challenges. There are also
ethical considerations. When you feed data into LLMs, what are they going to
generate with it? How will they use it? It’s crucial to integrate LLMs into the
workflow in a way that carefully considers the data itself. 

Cybersecurity is crucial. How do you protect patient privacy and PHI when using
these AI tools? These aspects need to be taken into account when developing a
product, or when helping other organisations to implement new AI-enabled
healthcare applications, for example to assist radiologists in their daily work
with critical and often time-consuming tasks. We need to be aware of these
challenges before releasing any AI development tool, and this is something we
are taking seriously, and others will need to consider as well. 

The second aspect is getting users to maximise the potential of these tools,
which is known as prompt engineering. It’s about how you construct the prompt to
get the right answer for what you need. For instance, you could give an LLM 50
papers and ask it to summarise them, and it would do a fine job. But if you
don’t specifically ask for any common themes or recurring elements in those
papers, you might miss important connections, losing some of the LLM’s value.
So, how you build and use LLMs, and the transparency behind them, is crucial. 

Ludmil: Yes, there are major limitations to implementing AI in medical
diagnostics. These include data privacy concerns, the need for large and diverse
datasets for training, regulatory hurdles and integration challenges with
existing healthcare systems. 

My advice to companies looking to implement AI solutions is to prioritise robust
data security measures to protect patient privacy, invest in acquiring and
curating high-quality datasets and collaborate closely with regulatory bodies to
ensure compliance. Additionally, companies should focus on developing
interoperable systems that can integrate with current healthcare infrastructure
and provide comprehensive training to healthcare professionals to facilitate
smooth adoption and utilisation of AI technologies. 

Most importantly, [companies should] build better AI models that can be trained
with smaller amounts of data will provide unprecedented advantages. This
approach will make AI technology more accessible to institutions with limited
data resources, reduce the time and cost associated with data collection and
processing  and enable quicker iterations and updates to the models. 

HOW DO YOU SEE AI IMPACTING, POSITIVELY OR NEGATIVELY, THE COLLABORATION BETWEEN
MEDICAL AFFAIRS AND MARKETING DEPARTMENTS?

Carlos: AI helps both teams align by providing real-time insights into market
trends, latest scientific updates, KOL identification, sentiment analysis and
HCP preferences, etc. This fosters data-driven strategies and enhances
inter-departmental communication. 

By accessing and analysing these vast amounts of data, we will witness
better-targeted marketing campaigns and more accurate scientific communication
and data dissemination. This data-driven approach ensures that marketing efforts
align with the latest scientific evidence and regulatory requirements, improving
the credibility and effectiveness of campaigns. 

However, there could be challenges if AI tools are not fully understood or if
there is a lack of alignment between the two departments in how AI is utilised.
To keep things harmonious, we need to set clear boundaries and ensure both teams
understand AI’s role. AI can make these departments work like a well-oiled
machine – as long as they remember to keep the lines of communication open. 

Ankur: Overall, I think the outlook is positive, with a strong opportunity for
growth and collaboration between medical affairs, R&D and marketing. However,
it’s important to be careful about what you’re asking for and how it’s
interpreted. 

You need to consider how much of this process is driven by AI, how much is
influenced by LLMs and how much involves real-world human translation of ideas
into execution. This applies whether it’s R&D working on a new product or
marketing developing a campaign and crafting claims.  

This multidisciplinary cooperation is valuable, as different groups – medical
professionals, technical experts, or marketing teams – speak different
languages. Without transparent, collaborative communication across all these
groups, it becomes a challenge. However, LLMs can help by bringing these
elements together, enabling more effective collaboration. 

By focusing on the variations, rather than the broader picture which is already
distilled for you, LLMs can help develop better marketing strategies and
streamline data analysis. It’s a balance between these AI-generated outputs and
human decision-making, where people discuss the differences and nuances. But
overall, I think it’s positive. 

Ludmil: AI can positively impact collaboration between medical affairs and
marketing by enhancing data sharing and analysis, leading to more aligned
strategies and effective, data-driven marketing campaigns.  

However, challenges include ensuring data accuracy, avoiding biases, balancing
different departmental objectives and the risk of overselling AI capabilities.
To maximise benefits and avoid hype, organisations should promote collaboration,
transparency and clear guidelines to align goals, ensure compliance and set
realistic expectations about AI’s potential. 

DO YOU THINK THE WIDER USE OF AI IN MEDICAL AFFAIRS WILL BE BENEFICIAL OR
DETRIMENTAL TO HCP ENGAGEMENT, AND WHY?

Carlos: AI will be beneficial to HCP engagement if implemented thoughtfully. AI
can offer personalised, relevant and timely information, helping them stay
updated with the latest research, treatment options and guidelines, freeing up
time for them to focus more on their patients and making the more complex
decisions.  

From the pharma/HCP interaction lens, AI can facilitate better communication by
providing tailored content that meets the HCP’s specific needs and interests.
But here’s the catch: AI can sometimes be a little too perfect. Like that dinner
party where the conversation feels a bit… scripted. Sure, the topics are on
point, but something’s missing—the warmth, the spontaneity, the human touch. So,
while AI can whip up all the right ingredients—data, insights, content—it’s up
to us to add the flavour. Think of AI as your sous-chef. It does the prep work,
but you’re still the one crafting the dish. And let’s be honest, no one wants to
eat a meal that tastes like it was made by a robot. 

Ankur: I think it helps with HCP engagement, if done right. There’s so much
information out there, hundreds and thousands of papers, data sets and
real-world studies. How do you distil that, ingest it and provide feedback so
that [HCPs] can deliver the highest level of care?  

It’s about taking AI tools to help us reduce that manual work, save time and let
people provide strong collaboration with their colleagues but also, really
importantly, with HCPs. The practice of medicine is evidence-based, right?
Having an AI that can facilitate all the evidence and distil it in a way that
helps you make a more personalised, evidence-based decision for your patient
would be amazing.  

As doctors learn how to use these things, it’s going to be an unbelievable tool.
And it’s really a complementary tool, right? It’s just another tool in the bag
of physicians to augment their expertise. As professionals, we go to school for
a long time to learn a lot of things, and to have this in your pocket that can
remind you of things or collect real data in real time is such a huge positive.
I can’t wait for it to be adopted on a higher scale. 

Ludmil: The wider use of AI in medical affairs will likely be beneficial to HCP
engagement by providing more personalised and timely information, enhancing
decision-making and improving patient outcomes. However, it’s crucial to ensure
AI tools are user-friendly and transparent to maintain trust and avoid potential
disengagement due to perceived complexity or lack of understanding. 

WHAT INNOVATIVE OR UNEXPECTED WAYS DO YOU THINK AI COULD BE USED IN MEDICAL
AFFAIRS IN THE FUTURE, AND HOW SHOULD THE INDUSTRY PREPARE FOR THESE ADVANCES?

Carlos: This field is rapidly evolving and I am not sure how far away the
“future” is. I see great use of AI in predictive analytics for clinical trial
outcomes, where AI could analyse data from ongoing clinical trials to predict
outcomes and suggest modifications in real-time, leading to more efficient and
effective trials.  

We will also see AI generating and personalising scientific content in real time
and advanced virtual AI assistants that cover all your time-consuming day-to-day
tasks. Why not think of increased collaborations with societies and governments
in leveraging AI to monitor and improve patient outcomes? To be ready, the
industry needs to start preparing now. Invest in AI literacy, infrastructure,
data quality, ethical and regulatory measures and encourage a culture where
innovation isn’t just welcomed but celebrated. 

Ankur: People are thinking about interactivity with patients and doctors, and
how they can get real-time information about what disease they have and what
treatment they need, so that those treatment plans can be tailored. 

Think about how many times you call your doctor and you’re like, “Hey, I need to
talk to you”. And they’re like, “Okay, I’ll call you back in a day when I have a
free moment”. But now, if you have a smart language model that you can query and
get some basic responses that your doctor can then look through, you get a
real-time back and forth with this LLM in between to facilitate that. 

And then I think it’s going to be interesting to see how LLMs or AI tools can
help optimise the design of clinical trials, how you recruit patients and
monitor them – how you facilitate that engagement and education of patients in
the process. You could have a personalised patient pathway and give them the
custom support they need. And that goes back to the earlier example: how do
patients and HCPs interact? If you have this LLM that understands the real-time
interaction that’s been provided, it can be so powerful for patients to feel
acknowledged about the care of their health. 

Ludmil: In the future, AI could be used in innovative and unexpected ways in
medical affairs, such as predicting disease outbreaks, optimising clinical trial
designs and providing real-time treatment recommendations based on comprehensive
data analysis. To prepare for these advances, the industry should invest in
robust data infrastructure, ensure interoperability of AI systems, foster
interdisciplinary collaboration and prioritise ethical considerations and
transparency to build trust and facilitate the seamless integration of AI
technologies into healthcare practices.


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