The Future of AI in Medical Affairs: How MSLs Can Stay Ahead with Anjali Rana

Hello, and welcome to another office hours with Dr. Swathi. Today, we have an incredible guest, actually, one of our medical affairs interns, Anjali, who's been with us for almost a year now. And so before I hop into that, just wanted to thank everyone for coming live today. I know everyone has really busy schedules, so to see these many people here live is is awesome. And I know people are tuning in from LinkedIn and from other platforms as well.

So so excited to see everyone. As always, if you ever have any questions about what we're talking about, about, you know, becoming an MSL, if you're an aspiring MSL, or if you're a current MSL, how you can be the best MSL ever, please feel free to put, you know, any question that you have in the chat, because we see that on our end. What we're gonna do today is we have a bit more of a topical presentation. So if you've attended any of these before, which I see a few familiar faces, when we've done them in the past, it's just been kind of an open q and a discussion, with whichever guest we have, that week. But today, we're actually gonna do something similar to what we did a few months ago where we talked about clinical trials and MSLs.

Today, we are gonna talk about the impact of artificial intelligence or AI on medical affairs. And so Anjali played a role in bolstering some of the content that we have on the MSL Academy about this topic. She's also just passionate about this topic in general. So thought she would be the perfect person to come on and talk about this. So what we'll do is I'll ask her some questions, and we'll run through some slides of information that we think could be helpful to the audience, and then we'll open it up.

But as we're going, please feel free to put anything you'd like in the chat, any questions about becoming an MSL, AI, anything in between, and we will answer all the questions at the end. Alrighty. So I think we should get started. I don't see any other I don't see anything, like, time sensitive in the chat, so I think we should just get started. So, Anjali, can you tell us a little bit about yourself and your role this past year at the MSL Academy?

Yes. So hi, everybody. My name is Anjali. I'm an upcoming postdoctoral fellow at St. John's University College of Pharmacy and Health Sciences, and I'm also a medical affairs intern here at the MSL Academy.

Right now, I'm helping support the creation of different tools to support the MSL's workflow, especially around using digital tools and AI. I'm really excited to be a part of the discussion today, and I hope we all walk away learning something new. Alright. Great. So what is medical affairs?

And and the reason I'm asking this is because a lot of people who have been coming to our webinars, they're actually pretty new to roles in the pharmaceutical industry. So because we're gonna be talking so much about the impact of AI in medical affairs and its role currently and in the future. First, let's define what medical affairs is. So, Anjali, what and how would you define medical affairs? Oh, that's a really great question.

So on this slide, it walks you through the foundation of what medical affairs is in a typical pharma or biotech company. So its main job is to connect science with real world clinical practice. What that means is it's taking complex research and making it useful for doctors, patients, and other health care teams. The goal is to make sure medical information is accurate, balanced, and up to date to share information clearly. We see medical affair teams support a lot of areas.

You could see them in scientific communication, gathering insights from clinical trials, developing real world evidence, working with thought leaders, and even developing internal training and education. Now you guys are probably wondering, where does AI come into the picture? So artificial intelligence is actually starting to play a really big role in medical affairs. It's helping us analyze data at a much faster pace. It's allowing us to personalize scientific communication and also helping us uncover insights that we may have missed previously.

So with this power, it comes a really big responsibility. We need to make sure that these tools are being used ethically, transparently, and at the end of the day, helping patients produce stronger outcomes. Alright. Great. So, I wanted to hop to, you know, the new course that you helped us put together, at the MSL Academy that is specifically on AI and its use in medical affairs.

So I wanted to ask you, you know, what inspired the creation of this new course? Oh, that's a really great question as well. So this course, it was created primarily because there was a growing gap. Specifically, AI is changing health care at a very fast pace. But in medical affairs, there's a lot of miscommunication in the sense of how it actually will make a difference in that space.

A lot of medical science liaisons, they may not be sure of where to start, specifically on how it applies to their workflow. So the goal of this program was to give MSLs and medical affair teams a chance to better understand how these tools can be used in more practical ways, showing them how to lead innovation in the work that they do. Great. And so just to get started on, like, some of the topics that are in the course, why should MSLs care about AI right now? As you mentioned in one of the previous slides, you know, there are so many potential impacts of it, of course.

But, you know, why should they care right now, and how could it impact the future of their role? So based on this slide, it walks you through three key areas of how it can impact their role in the future. AI is already changing the way medical affair teams operate, specifically in the ways that they're shaping their insight collections, the way that it's helping them personalize engagements with different different health care professionals. It's also making it easier to analyze large sets of medical data, which is why the role of an MSL is now becoming even more strategic and data driven. So those who are better able to understand the way that AI is being applied, it allows them to build even stronger relationships and drive more impactful outcomes.

Amazing. That makes sense. So, you know, how is AI currently being used in medical affairs? You have, like, a real world example so that our audience can understand more specifically? Yes.

So we wanna go over four examples just to provide more of an understanding of how it's making a difference. So one example is content generation. So let's say a lot of patients are asking about a new drug that's about to launch. The medical affairs teams, they need to give accurate evidence based answers so AI can step in and help draft the first version of those responses using a standard prompt, for example. It keeps the message consistent, saving time.

However, the medical expert, the MSL, or the medical affairs team still comes in reviewing everything before it's shared to the general public. Another example is insight mining at scientific congresses. So there's so much data that's being discussed, posters, presentations, and abstracts. Now AI could come in and summarize key points, group related topics, or even flag anything new or important. It can also link that information back to the company's ongoing research.

We also see how AI is helping health care professionals with segmentation. So what that means is figuring out which health care professionals to focus on. So when you look at data like past conversations or research interest and behaviors, this helps the field team tailor who they want to speak to and what they wanna share. And lastly, AI is also being used to detect real trends in real time. So if there were multiple MSLs hearing the same off label question, AI can pick up on that pattern and alert a department or a team and to respond and adjust their strategy accordingly.

So, overall, AI is helping medical affairs move faster, allowing them to stay organized and also respond more effectively. Yeah. Definitely. And so, you know, given these examples, what are some of the AI tools or technologies that are most relevant for MSLs today? So if you look at this visual, AI in an MSL's day, you can see how AI fits into every part of an MSL's routine.

So if we wanna first look at literature search, AI tools like natural language processing, it can read through tons of research articles and pull out key points. That way, MSLs can stay up to date without spending hours reading every single paper. Next, if we wanna look at meeting preparation, this is where predictive tools come in. They help MSLs figure out which doctors to meet with or what topics they care most about and even what follow-up actions make the most sense. Then when we wanna look at a health care professional engagement, so what this means is meeting with the health care provider and interacting with them.

So when you think about personalized talking points or even summaries, the AI could possibly understand the provider's interest, saving more time and making the conversation more focused. And lastly, insight capturing. After the meeting, MSLs often take notes about what they heard. AI tools can help turn those notes into cleared summaries so important feedback gets shared quickly with the rest of the team. So whether it's reading, planning, engaging, or even reporting, the AI is already making a difference in the MSL's workflow.

And so I wanna double click on data interpretation and real time decision making for MSLs. How can AI play a role in those key aspects? That's a really good question. So on the following slide, it just picks a visual of how AI can help turn unstructured data into clear strategy for MSLs. So at the top of the funnel, we have what's called raw data.

This includes things like a customer relationship system, meeting notes, scientific papers, and email summaries. So in other words, large amounts of information that may not always feel organized or easy to sort through. Then when we get to the middle of the funnel, AI can step in. It can scan meeting notes and pick up common themes using language tools. It can find patterns in how a doctor responds or what questions they ask, and it can group related topics together to spot gaps or new medical needs.

This is really important because it shows cases how you can identify information at a fast pace. Then when you move to the bottom of the funnel, AI can give you something even more useful, maybe recommendations on what to do next, which health care professionals to follow-up with, tips to prepare for the next meeting, or a clear summary of what you've learned in the field. So MSLs can now focus on what matters most to them, talking to the right people, engaging with them with the right message in mind. Yes. Definitely.

And so, you know, with so much discussion about AI right now and how it can be used in pharma and in medical affairs, what are some of the most common misconceptions that you've heard? Because something that you didn't mention is that you actually have quite a bit of experience working, with field teams in pharma. So I'd love to hear, you know, what are some of the most common misconceptions that you've heard just being, you know, like a a fly on the wall, in your, previous internships? So there are often misconceptions being had about the space of AI and medical affairs. And listed here are four common key themes I've noticed.

One of the biggest assumptions is that sense of fear, a concern that AI might replace a potential person or an MSL. But, really, AI has a potential to support us in our role. It could help with faster decision making, and it could help build stronger relationships. Another barrier is this idea that AI is only for those with a technical background. But many of us forget that today's tools are especially designed to be user friendly.

So through training, m MSLs could start using them without needing to know how to code, for example. There's also an assumption that AI is always accurate, but it still needs human oversight. It might help organize and highlight key points, but the MSL who brings context to the situation makes that final call. And lastly, there's also a limitation that maybe those with larger resources can use AI effectively, but all organizations have the ability to benefit, becoming more efficient, more targeted, and more strategic. Yes.

Definitely. And speaking of being more strategic and efficient, how can AI help with, you know, compliance and reporting tasks? So listed here is a specific example. So AI can make compliance and reporting feel a lot more manageable. On the left, you could experience this usual stress, stacks of papers, tight deadlines, and that feeling of, did I forget something?

But when you look on the right side, this is what it could look like using AI. It can help you autofill templates. You could do real time compliance checks. You can keep everything organized. So instead of scrambling and feeling not ahead of schedule, now you can save time and keep things more consistent.

And it can now free up your schedule to do things that actually matter to you, like engaging with the health care professional and having those scientific conversations. Yeah. Definitely. And I I've heard from so many MSLs that, you know, they feel like the person on the left side of the screen. And I think a lot of it is, like, you know, maybe they don't use AI as a part of their day to day in all these different capacities, especially I love the image of the life cycle of, like, a day in an MSL, and they don't use it in all those different touch points.

And they feel like they just have so many notes in their phone or, like, notes in a notebook. And so I, yeah, I I love that image because I feel like a lot of MSLs feel that way. Even if they're not necessarily sitting at a desk and have that pile of paper, they have their, like, kind of their version of that, pile of paper. Yeah. Definitely.

And then so you started talking about health care provider engagement. So can you talk a little bit about how, you know, how can we think about AI powered decision support systems, and how can MSLs use them during those engagements? Yes. Of course. Of course.

So now here, we've all been in situations where a great question comes up in a meeting, and we think, ugh, I just read that paper. Where is it? That's what you see on the left. The MSL wants to help, but they don't have the right resource in that moment. But now when you look on the right side, this is what AI could make possible.

It can listen to the conversation and understand the context. It could bring up the most relevant studies, like visuals or data right when they're needed. This kind of smart support, it helps MSL show up with confidence, keeping conversations moving and also making each interaction more valuable for the health care professional. And so one of the things that you touched on briefly is the potential hurdles once it comes to, you know, ethical or regulatory concerns with AI. So can you talk a little bit about, you know, what are some of the concerns and how do they play a role in medical affairs?

So when you look at this slide, these are the major areas that we all should be cautious of. So first, transparency. If we don't understand how AI is generating its answers or can't explain it, then how can we expect others to trust it? So what I mean by that is we have to really think about what's being shared to others without having an expert's point of view. And then next, like, data privacy.

Because AI is being run on large datasets, this could mean that we need to be extra careful with sensitive information, especially anything involving patients or health care professionals. Then there's also off label risk. So AI can sometimes generate content that crosses the line into nonapproved uses. That's why strong guardrails and human review are so important. And lastly, regulatory oversight.

So as you've mentioned, it's very important to consider because this content, it has to meet all the same standards we follow in medical affairs. That includes medical, legal, and compliance review review. And at the end of the day, AI is there to help us. So when we put these right controls in place, we can really use it to work smarter, stay compliant, and to maintain trust with both patients and providers. Yes.

Definitely. And so as, you know, let's say professionals in the MSL world or in medical affairs, they wanna use these AI tools, but they're not certain about, like, you know, are these tools biased? And, like, how can we, as, like, a community, deal with that? And how do we ensure that AI tools are not biased? And this is, like, particularly, I'm interested in this question when it comes to, like, patient segmentation and, like, recommendation engines.

So to really understand segmentation and bias, we have to think about how AI tools how they feed diversity and representation through data. So if the training data lacks that variety or reflects outdated assumptions, bias can become built from the start. So AI really requires that human oversight. So medical professionals, especially MSLs, they play a critical role in reviewing AI outputs to ensure that they're clinically accurate. So we need to remember that AI is a tool, but we're still the ones that are guiding that impact.

Definitely. And so for current MSLs, I see there's there's at least a few current MSLs on, right now live. So what questions should they be asking when they're evaluating whether an AI tool is ethical and compliant and, you know, also not biased? So for an MSL, it's not it's not just enough to just use the AI tool. You wanna understand how they're built and governed as well.

So when you're evaluating whether an AI tool is ethical and compliant, you could possibly ask yourselves these series of questions, such as what kind of data was this trained on? Was it diverse, up to date, and representative of the populations we serve? How are the outputs being reviewed? Was there scientific accuracy, regulatory compliance, or medical relevance? Is there an audit trail or explainability feature?

Can we trace how that AI arrived at its recommendation? Who's accountable for validating that information? And finally, is it aligned with our internal policies and the latest industry regulations? So when you ask yourselves these questions, it's ensuring that we're not just using the AI, but we're using it responsibly. Yes.

And I'll I'll leave it on the screen for a second if anyone wants to screenshot that because I was recently catching up with one of the MSL coaches at the MSL Academy, and she was talking about how, like, one of the things that she's working on as now a senior MSL in the field is, you know, they're actually helping to train some of their internal AI platforms on, like, clinical decision making and things like that. And so I think this list resonates with a lot of what she's thinking about as well when, you know, working on the tool that her company is is coming up with internally, with their data team. So very interesting. Cool. And so let's say, you know, it looks like we have a combination of aspiring and current MSLs who are listening in right now.

What AI related skills should they be, you know, looking into or starting to learn now so that they will be prepared to use some of these tools once they're in industry or maybe they're in industry now and their company hasn't yet started integrating them? So when thinking about an MSL's role, it's important to start building the right skills to work with AI confidently. So the first skill that we recommend is possibly focusing on prompt writing. So what this means is learning how to ask clear, specific questions. AI only works as well as the prompt that you give it.

So knowing how to structure your questions, that makes a big difference. Also, getting comfortable interpreting AI outputs. So that might be a summary, a graph, or even a visual dashboard. So you don't need to know how to code. You should just be able to look at what the AI gives you and decide if it makes sense.

And finally, if you're working with digital or data science teams, it helps to know the basics of how AI is built. You don't need to be an expert, but understanding the general process makes that collaboration easier. So these skills, it will not just allow you to use the AI, but it allows you to be more responsible when having those conversations. Yes. And I think we talked about this a little bit already.

Like, we haven't explicitly come out and given the answer. But I hear this all the time that people are like, oh my gosh. Will AI ever replace me as an MSL? Is AI going to replace, you know, medical information? So I'd love to hear your perspective on the statement given, like, the deep dive and the information you presented so far.

I think that we've kind of alluded to it throughout the discussion, but would love to hear, you know, your more specific answer to the age old question. I guess it's not age old because this is not that old of a conversation, but I just feel like everyone is talking about, will AI replace insert job here? So will AI ever replace MSLs, Anjali? So that's a really fair question. For me, personally, I don't believe that that's the case.

AI is incredibly fast and powerful. Yes. It can process data, scan literature, and surface insights in seconds, but what it lacks that's very valuable to the MSL role is that sense of empathy, clinical judgment, and the ability to build trusted relationships. So if you look at the left side of the slide, you'll see what AI can do independently. It's efficient but limited when it comes to that emotional intelligence.

Now when you compare that to the right side, this is where the real potential lies. When MSLs leverage AI to tailor insights, free up time for those deeper engagements, and become more impactful with the conversations being had, It showcases that AI won't replace MSLs, but MSLs who know how to use AI effectively will absolutely elevate their role. Yes. I a % agree with that. And the other thing I would add to is we're talking about AI like a tool, and AI is going to be one of those tools that we should all have in our toolbox as, you know, medical affairs professionals.

So I I too agree, AI will not replace MSLs, but I do think that MSLs who are able to utilize AI and other related tools are going to really excel. Okay. Amazing. So as we're as we're wrapping up, I have a few kind of final questions. So, you know, this is similar to something I asked before, but I'm always, like, hearing, you know, people interested in how they can make themselves stand out in an application to an MSL role or a medical affairs position.

So how can a potential candidate distinguish themselves as an AI forward strategic partner, whether they're already employed at a company or they're looking to get their first role in pharma? So for those of us preparing to step into that MSL role, it's time to think beyond clinical knowledge. We also have to understand now how to bring value through innovative tools. So one way to stand out is by sharing insights on new AI tools, for example. Even if you're not using them directly, showing awareness and curiosity about how it could benefit a medical team, it signals that you're thinking ahead.

You can also volunteer to support pilot projects or digital initiatives. This helps build your experience and also shows that you're open to collaboration. And, finally, when you're looking to bridge science with technology, whether that's helping explain data tools to your peers or thinking about how AI could enhance a health care professional's interaction, you should really think about how to position yourself not just as someone who's ready for the role, but how you can help shape where the field is going next. Absolutely. And, you know, just to as a final question before we open it up to q and a, what is next for AI in medical affairs?

Like, what should people be looking out for? And, you know, the course that you helped put together, you know, how can this course help prepare professionals for what's coming? Absolutely. So the future of medical affairs is moving fast. It's being shaped by intelligent data driven systems.

So AI won't just support the work that we're doing. It's becoming a core part of how we engage with those health care professionals, specifically how we're educating and generating those insights. So what makes this moment extremely important is the way that this model is being used to enhance the future of medical affairs. This course is designed to help professionals, especially MSLs, prepare for that shift, specifically in how to build a strong foundation, how to understand how these tools work, and how to use them responsibly. So I'm really excited to be a part of this change, and I'm excited for others to feel ready, curious, and capable of what's next in medical affairs.

Okay. Amazing. I'm so glad we got through that information in great time. So we were able to focus on, you know, our kind of topical discussion today on AI and medical affairs and kind of showcase some of the work that you've been up to at the MSL Academy. I'd love to open it up to questions.

I don't see any in the chat right now. But if you have any questions specific to, you know, medical affairs, to AI and medical affairs, maybe you're an aspiring MSL who is trying to, you know, help stand out in the pool of applicants, like, what questions can we answer for you today? We're happy to stay on for, like, you know, up up to thirty minutes if people are interested. That's the time we've kind of allotted for this. But we'll stay until, we see a question.

We'll we'll stick around for about five minutes to see if a question comes in. If a question doesn't come in, then please do not hesitate to reach out to us. You're more than welcome to, you know, send us a a LinkedIn DM. I I think that's your your next slide. Right, Angelie?

If you wanna go to that next slide. You're more than welcome to oh, actually, there's a poll everywhere. Do we wanna do the poll maybe in the meantime? I was actually just gonna say, like, if people had any questions, we would do some of those first and then also jump to the poll, in between. But let's see, yeah, if anything is coming through.

But, again, this is a lot of information in a short period of time. I feel like we did, like we we did a great job of providing some concise, tangible takeaways, but so happy to answer any questions that you might have on the topic or about transitioning into, becoming an MSL, and, yeah, anything that we can help with. We do have these fun poll everywhere questions. So, you know, they kind of pertain to what we talked about today. We can hop to those if if there aren't any other questions in the chat.

So I don't mind starting. And if you guys have any questions, feel free to ask away after, the questions are completed. Sure. Okay. Now that we've covered how AI is shaping the future of medical affairs, we now wanna take a moment to hear from you.

So we're gonna use these questions to get your thoughts to see how everyone's thinking about this shift in real time. So you can respond in the chat box below. So let's kick it off with our first question. Which area of medical affairs currently benefits the most from AI integration? So if you think about what you've seen in your own experience or what you've noticed across the field, we want you to take a second to make this choice and let us know the answer again in the chat box below.

And it's okay if you don't know the answer as well. We're gonna go through it. Some people are being shy with putting their their responses in the chat. No worries. So I'll I'll reveal the answer.

So if you're thinking insight collection and analysis, you're right. This is where AI is making a big difference right now. MSLs gather a lot of information during the health during their health care professional meetings. For example, questions, trends, and treatment gaps. And it's often written in free text, which makes it hard to organize.

But now AI could help clean that up. It can group insights by topic. It can highlight trends and even point out patterns we might not catch on our own. So instead of digging through notes, we're using that time to take action and guide strategy. So thank you guys for thinking about this question.

I also wanted to add to that. I think that, like, it's not only because if you think about the way an MSL works, like, they're pretty autonomous and that, you know, they might only be working in a particular region. I love the idea of, like, you know, if you're working with other colleagues who are, you know let's say, like, you're responsible for New York City, and your colleague is responsible for Boston, and one of your KOLs moves from New York to Boston, then it's so much easier to do, like, a knowledge transfer if all of these insights have been, like, collected and analyzed through AI and all of, like, the key points have kind of been distilled versus sending over here a bunch of, like, my free form notes that I've taken over, like, the nine months of getting to know this woman, and now she's moving to Boston. And now, like, it's it's up to you to continue, you know, fostering that relationship. So from, like, a very practical perspective, this just makes so much sense.

I also think, like, as you're evaluating the insights that you're collecting, I think it's also fair that, like, an MSL team that is, you know, all across The US might also be interested in, like, what are all these GI doctors saying about this drug? And you can kind of compile all of these free form pieces of, you know, information, insights, content, and put it all together and actually analyze it through AI. So to me, this is not just exciting for the one off use case of, like, one MSL using it for their specific role. It's like because we always think about MSLs as being so autonomous, but it could be so great for, like, you know, if a KOL moves, if we're thinking about larger integration of information, or even, like, let's say, you know, like, you've been at the company for a few years, you're looking for your next role, you get a new role somewhere, it would be so much easier to do a knowledge transfer if you're leaving the company with, let's say, the woman who's gonna take over your role. Then it's so much easier to be like, hey.

These are all this is all the information I have on this person, on that person, and you'll be able to put it together in such a neater package that's just so much easier to understand. So, yeah, I I really see this. I mean, I do think that some of the answers, like a through d, could also be really helpful. But I do think that currently, just with, like, how subjective some of the work is done with, you know, with letter b, that I I would agree that that is the correct answer. But I could totally see, like, AI being an amazing asset to, like, add board planning and execution.

Maybe not the execution part as much, but more of the planning side of, like, okay. How are we gonna set up the agenda? Like, who's gonna be involved? Who's gonna talk when? When are we gonna feature, like, different, you know, people in the advisory board, things like that.

So I think that could be interesting. Even, like, KOL engagement tracking, I think could be really interesting as well with the use of AI of, like, okay. Like and there already are some platforms like this that are, like, you know, where you keep track of, like, oh, who am I reaching out to? You can use, like, HubSpot, for example, or Veeva or these other platforms of, like, oh, I haven't reached out to this person in, you know, a certain time frame. It's time for me to reach out to them again.

So I do think all all of them to an extent could be helpful, but I, yeah, I do think b is where it could have the biggest impact in the short term. Thank you, Swati, for walking us through the different responses. And now I'm excited to share another question too. So the next question now is making us think a little bit more about tasks. So which of these tasks do you think is least appropriate to fully automate using AI?

So translating medical content into plain language, prioritizing which health care professionals to reach out to, building strong relationships with key opinion leaders, or summarizing notes in a customer management system. So take a second and pick the one that feels like it really needs a human behind it, and we'll talk through the answer together. If you want, I can share the answer, and then we could discuss it as a group. So if you thought about building relationships with key opinion leaders, that is the correct answer. This is something that AI just isn't being built to do.

Yes. It can organize info. It could prepare summaries. It could even suggest topics for a meeting, but relationships are also a lot more than just the data. So as we mentioned, they're about trust and knowing when to speak and when to listen and also reading the room and understanding those social cues.

That's something that people really know how to do. So while AI could be a great tool to support these conversations, this actual connection that humans have built and bring can can really change the course of these relationships moving forward. Absolutely. Yeah. I have nothing really to add there.

I think that, like, humans like, this, like, to me is, like, the case in point why, you know, MSLs are not going to your place by AI. Because all of these things that a through d are all pretty important to be able to do as an MSL, But c is the one thing, at least for now, the the AI or robots or, you know, whatever, like, technology that's up and coming, they haven't figured out how to do that effectively. And I think that's because health care providers, at least in my experience, just really like the peer to peer engagement. They like the opportunity of, you know, like, getting to know someone. And I know I I've talked about this with other MSLs before.

Like, half the time when you're taking notes in a meeting, you're you're, like, taking notes about, like, oh, their, you know, their kid recently did super well at their ballet recital. And then right after that, you're writing a note about, like, a clinical insight from, you know, their use of the product on a 69 year old. You know? So it's like it's very, like, back to back, and it's as much IQ as it is EQ. And that's something that, at this point, AI hasn't been able to to master.

And I I don't think that's where AI is going to fit in medical affairs, which we talked about briefly already, but I really think that MSLs are gonna continue to be on the rise. I mean, the the projected value of of MSLs, like, with upcoming, you know, launches and everything is, like, a 35% increase projection in, like, the number of jobs that are gonna be out there, within, like I think that's by 2035. Is that true? Something it's it's like a statistic very similar to that, but it's quite a jarring number of an increase of MSLs that will be joining the workforce and an increase in number of positions. Because, like, even with the trajectory of where AI is going, we are noticing and realizing that AI is not gonna replace MSLs.

Instead, we're gonna need even more who can actually use AI really effectively. I I agree. And the next question helps make this a little bit more clear as well. What's the biggest challenge to AI adoption in medical affairs? We talked a lot about what AI can do, but what's now actually holding us back from using it more confidently or consistently?

Is it concerns around data privacy, not having the right technical training, a general resistance to change, or is it all of the above? So if you are thinking about choice d, that is the best answer here. Data privacy, as we've spoken about previously, in a highly regulated space, teams have to be cautious about feeding sensitive information into AI tools, especially when it involves patient data or proprietary information, then there's that technical skill gap. Most people in med affairs may not have that trained technical aspect in mind. But without having that education or support, it may feel a bit intimidating to use an AI tool.

And finally, change management is really big. Even with secure tools and training, there's sometimes that hesitation about, is this really going to help me? Or what if it messes up something important? So if you're feeling that sense of curiosity and also caution when thinking about AI, it's important to understand that you're not alone, that this is how majority of this landscape is feeling at this time. Yes.

Absolutely. And our last question of the night is, in five years, what role do you think AI will most likely play in medical affairs? Will it replace MSLs completely? Will it support personalized scientific engagements? Will it write entire regulatory submissions or just monitor social media for adverse events?

This one's all about future direction and about how we see AI's evolving role in this field. So if we are thinking about choice b, supporting personalized scientific engagement, that to me is the best answer here. AI isn't here to take over. It's a chance to enhance how we connect with people, specifically building relationships. So if we listen, adapt, and respond in ways that the AI can't, it's about how the AI is working behind the scenes, pulling those key insights, tailoring content, and making sure that together, the MSL and utilizing AI could create more meaningful conversations.

Very interesting that you have on here about regulatory submissions. I haven't really thought about that, like, that much just because, you know, I'm kind of in medical affairs. You're headed into medical affairs. Most people I talk to within pharma are kind of in that space or in that functional area. But I would be very interested to hear about how regulatory affairs is thinking about AI because I didn't really think about that.

I do think that it's it's similar in that you still need a human to oversee everything. Like, I do not think AI can, you know, fully replace regulatory affairs by any means, especially with, like, the new regulations and things kind of ever evolving. But I would love to hear I don't think we have anyone in the chat from what I'm looking at who is in regulatory affairs, but perhaps in a different life, maybe before you transition to medical affairs or something maybe some of you know. I would love to hear, you know, from, like, a regulatory affairs perspective how AI is currently impacting the role and how AI could potentially impact the role in the future. I think I'll reach out to some of my colleagues if we don't get anything in the chat.

I'm definitely gonna reach out to some of my colleagues I haven't spoken to in a while and see and see what they say about it because I'd be very interested to find out. Keep me updated as well. I'm I'm really excited to hear what's next. But we just wanna say thank you all for being here tonight. As we wrap up, we just wanna say that if you're interested in learning more or getting involved, please check out the MSL Academy to learn more and to stay connected with us.

We also have our information listed here on the screen. Amazing. Yeah. I feel like we gave people quite a bit of time for questions. I didn't see any come in.

So I would say, like, we can end early. But as you know, if you're listening to the recording, which we get a lot of people, you know, who are unable to attend live, they listen to the recording, or they send the recording to one of their colleagues who might be interested. So if you are listening to the recording, you know, you have our information on the screen. You have the website on the screen if you wanna learn more about some of our programs and offerings at the MSL Academy. Don't be a stranger.

We're we're here to help, and we we love talking about this stuff clearly. We filled a whole hour with us just talking about it. So, yeah, if there's anything else we can do to help, definitely let us know. Alrighty. Well, have a good evening.

Bye bye. Bye, everybody.