Interview with Berkan Aysan, Global Medical Engagement Excellence and Digital Health NPP Lead, Merck
Unlocking the full potential of AI will likely involve combining its strengths with a full spectrum of other intelligence types, from human to biological. This is the symphony of polyintelligence, which Berkan Aysan, Global Medical Engagement Excellence and Digital Health NPP Lead, Merck, hopes will allow us to achieve synergy in addressing problems and performing tasks as a society.
SciencePOD had the opportunity to speak with Berkan in advance of the Reuters Events: Pharma 2025 conference, which will run from 9-11 April, Barcelona, Spain. Berkan will be speaking in a panel on “AI-Driven Customer Centricity: Transform Your Engagement Strategy” chaired by Sabine Louët, SciencePOD CEO.
“We need to realise that the strength of AI will lie in polyintelligent systems, where different intelligence types converge synergistically to solve problems or perform tasks.”
Could you describe your current role and main focus?
I have two positions at Merck and one of them is in medical engagement excellence. In that role, we take care of the strategy and excellence for our medical field force, as well as our global strategy for our medical engagement teams, meaning the team that interacts with the doctors and key thought leaders (KTLs).
The second role is the digital health new product planning. We look at our pipeline and then we see how we can enhance assets within the R&D process, assess go-to-market models and enhance the differentiation of our in-market assets.
Could you give us an example of how you have been using AI for scientific data dissemination to ensure data-driven customer centricity?
Yes, so as part of my medical engagement role, we are using AI to mine mass databases of KTLs to find interesting people and trends, and conversely the KTLs are interested in our assets. This is one of our biggest use cases, and AI has enabled significant cost and time savings.
For my second role in new product planning, the AI is more than using big data. We use it for hyper-personalisation, to assess patient adherence and smart monitoring, as well as dosage response and treatment protocol adjustments to improve the health outcomes of our patients.
What kind of best practices would you adopt with AI?
When it comes to AI, I don’t think we’re in a place to use best practice yet, because best practice always changes. We’re still learning while the technology develops.
We are constantly discovering better ways to use this technology. So, what we have decided is that instead of having a hammer and looking for a nail, we see where the nails are and work out how to nail them in, using AI or otherwise.
What this means in practice is that we are developing AI use cases depending on business needs. When AI came first, people said “We have this tool, where can we use it?” Now we say, “we have this problem — how can we develop a tool to address this?”
What excites or interests you most around the use of AI?
Some of the most interesting applications for AI for myself personally involve improving patient outcomes. In particular, finding the right treatment for the right patient, and AI is helping us to identify subgroups where our drugs could be most beneficial. So, personalising the treatments, monitoring, and adherence — that is where we can best serve our patients, and this is what excites me the most and what can have significant impact, because it’s tailored to each individual patient.
How do you anticipate AI will change the way you work in the future?
I’m really excited about our advancements, but I don’t think anyone really knows how this will develop, and that’s why I’m always working on upskilling myself and my team.
We need to realise that the strength of AI will lie in polyintelligent systems, where different intelligence types converge synergistically to solve problems or perform tasks. This could involve pairing AI with human intelligence or intelligence from biological systems. AI already exploits polyintelligence, as much of the input data used to train these systems derives from humans.
I don’t see AI as a nemesis, but an enabler. It will enable us to save more time and effort so that we can use our brains to be creative. We can evolve together with AI, rather than it being a nemesis.