Well, hello, everyone.
Long time no see.
And welcome to another exciting episode of Pharma Insights,
the talk show brought to you by Platforce, where we explore the intersection of technology, the strategy,
and innovation in the pharmaceutical industry.
For all of you who do not know me, my name is Juliana Kreisel,
and I'll be one of your moderators for today.
Do you want to harness the power of AI agents into your business?
Do you want to discover how these technologies can enhance innovation and streamline processes in pharma?
Then you are in the right place.
The healthcare landscape is rapidly evolving and artificial intelligence is at the forefront of this transformation.
As AI agents become increasingly integral to pharma operations, understanding the role in driving innovation is crucial.
In this episode,
We will explore how AI agents are reshaping strategies
and enhancing operational efficiency within the pharmaceutical industry.
For this, we have prepared a special panel,
but before we introduce them, I need to introduce my co-host and colleague for Platforce
Mr. Stefan Rappin, head of marketing.
Hello, Stefan.
Namaste, everyone.
How is everyone?
It's evening here in Europe.
where I'm based right now.
I'm super happy to talk to you guys about anything other than chat GPT.
Today we're about a pretty interesting thing which is called AI agents.
they're so unique to, I would say they were so unique to Pharma that it made sense for us to get the best of the best.
And we got Harvey, Salva, and one more guest from Microsoft, a very special guest for you.
You don't want to spoil it.
I don't want to spoil it because the guest is very special.
It took a lot of effort to get that person with us.
And I would welcome the guest to the stage.
So start to enjoy and please ask us questions.
Ask all the participants questions.
We will be delighted to answer.
Thank you.
So first we have Salvalor.
Carlucci.
Hi Salvador, how are you doing today?
Hi Juliana, hi Stefan.
Good to see you.
Thank you very much for the invitation to be here.
It is a very exciting topic.
I do believe artificial intelligence is probably the technology
that we will experience in our lifetime that is going to revolutionize the way we see the world.
For us at Atacana, we believe there is also change
how we make decisions within the pharmaceutical industry.
but also how companies are going to compete in the new market as AI
gets embedded within the different parts of the organization.
I believe AI agents is probably the first step that we're going to see that's going to be concrete
on how we interact with them.
Then I do believe there's going to be agentic workflows,
AI teams that are going to have to put in place as technology evolve.
My background has been in Big Pharma.
I was the head of global competitive intelligence for Novartis.
I was the global head of competitive intelligence for Roche and then left corporate to set up Atacana.
We're working with 9 out of the top 10 biggest pharmaceutical companies.
And a lot of the discussions I'll say, especially this year has been around
artificial intelligence in particular, is coming up about AI agents and how can we leverage them to
make better decisions.
Nice.
And I know, well, of course I know, that you have an especially invitation for our attendees.
So I'm going to share it here
in our screen.
We have a master class coming, transforming decision making in pharma with AI.
Guys, you need to go into this.
So register.
Yes.
You can follow me on LinkedIn also.
And then I can say the invitation.
Since we're working with pharmaceutical industries and big pharma, a lot of the conversation is,
can we use AI agents to process large amounts of data?
and drive new insights?
And I think it's very fitting here with the Pharma Insights show.
But it's really this idea on how do we see the technology evolving?
And how do we see this technology being embedded in decision making
within the pharmaceutical industry from preclinical all the way to loss exclusivity?
In the masterclass, we got a little bit deeper in terms of how do we see this evolving, the agents evolving over time?
Thank you.
guys,
Register.
Now I will introduce our second speaker for today, Mr.
Harvey Castro.
Hello, Harvey.
How are you doing?
Yeah, I cannot hear you.
It's okay.
okay.
Where's the...
I had muted it.
I wanted to make sure.
Thanks for having me.
I'm excited to be here.
Thank you.
Thank you so much.
And now we're going to introduce the spoiler that Stefan didn't want to introduce first.
We have Mr.
Shadab.
Hello.
How are you doing today?
You're the special guest.
Stefan's special guest, I will say.
Hello, everyone.
Okay, so...
We have introduced the panel.
We have introduced a topic.
So I think now is the time to start discussing a little bit about this.
So my first question, and it's something that we have been discussing prior off camera, was what are AI agents?
Salvador, do you want to start with that?
Yeah, I'm looking.
I'm very curious to hear from Harvey and Shabad on their definition of AI agents.
I think there's been multiple definitions
in different companies describe it different way.
The way we kind of simplify the definition is that an AI agent has some type of reasoning and planning.
I don't think they acknowledge this there yet, but they have a large language model.
It's kind of core component.
Then they have a purpose.
And for us, the purpose can be the system prompt.
So what is the job description that the AI agent has?
And then they have a task that they need to complete, which might be the short prompt,
or what they need to do, like summarize in a particular document.
And then for us, AI agent also has a skill set.
The skill set can be access to a vector database.
It can be access to the web.
It can be access to SQL database.
So then these agents now can interact with the external world.
I think Cloud 2.5 came out this week, and they can control a computer.
So I think this skill set is also going to evolve as they also get smarter and multi-modal.
OK.
Harvey, do you want to give your insights as well?
Yeah.
I see agents kind of like an AI detective
aware of its environment, looking for patterns that you can have him or she help you find things.
I personally think of it as just as an assistant, my digital assistant, and
it's able to find things and observe and doesn't ever get tired, work 24/7, it helps me out.
Nice.
And Shadab, I know you had more insights into the AI spectrum in Microsoft.
Do you want to give your insights, especially since you are
in pharma but on other markets as well.
Sure, know, agent is someone who has nice cars, lot of money, travel all around the world and has a license to kill.
Joking, that's the wrong agent.
We're talking about the AI agent, right?
So the AI agent is really, you know, the, it's an assistant who has the ability to plan, to reason, to orchestrate,
to respond to the percept from the environment and take actions on your behalf, right?
And also importantly, learn.
So it continues to learn as well.
And that's the definition of the modern AI autonomous agents.
That's what it is.
And these agents, the good thing about that agent is that this just cuts across the industry,
not just the pharma.
They just cut across the entire industry.
In fact, the most popular example of an agent I can give you is it's your own city.
For example, it does things on your behalf.
It's a great example of it.
Now think of it in different settings, whether it's an agent for a doctor to help with the notes or diagnosis, or it's an even agent on your own behalf.
So everybody can have their own agent, and then those agents can work with even other agents or other humans to do it.
So consider this.
This is like an assistant for specialist tasks.
That's what I will define it as.
Love it.
Thank you for that.
We were discussing that agents can learn or one of the insights from agents is that part that they are continuously learning.
do you think that they can predict market trends and consumer behaviors into the pharmaceutical?
Can we use it that way?
Or do you think it's too much of, I don't know, to future vision kind of thing, like not into information?
So my take on that would be that, look, you know, your AI has learned things from what has been generated by humans.
It is not creating something totally out of place, right?
It is generating based on what it has learned.
So it cannot speak a language, which it has not learned, for example.
So it can predict based on the pattern that it has seen.
But if, let's say, an unprecedented event happened and things completely changed,
that disturbed the data pattern on which it has learned.
So as long as the patterns are being followed, it's going in the same direction using the patterns, yes, it can forecast.
It can forecast trends.
It can forecast what it has learned, but it cannot create something totally new altogether.
OK.
Salvador, do you have something into that?
I think with the clients that we work with, they rely on accurate information and the latest information.
So what we have found is that we need to be providing the latest data that sometimes is not in the public domain to these agents.
So a lot of what we're doing is how do we do automation the way that the agent either
through a vector database or SQL can access the latest information that is being either
created within the client side.
So maybe internal information that is not in the public domain,
or information that maybe is restricted for whatever reason,
to make sure that those agents are working on very specific tasks to get access to that.
Yeah, and I'd want to add to that, think of the agents as different tasks, and so you can assign an agent for every little task.
And so for that portion, then when you mentioned predictive analytics, I mean, we could have an agent that's looking into the market in real time.
and constantly searching and then every time something new comes, that's what that has.
Then you can have another agent to validate what that guy said or her, the agent.
And then you can have another agent that's coordinating all the agents for the bigger picture.
And so you can really segment this down to different parts of this to have those predictive analytics to have that tailor customized for you what you're looking for.
Obviously, when you have these agents going, every time it does a query, a question is going to take up some
percentage of tokens and that percentage of tokens may translate into amounts of money.
So depending how long you guys are working these agents, it's going to return how much it's going to cost you.
So it's not as cheap of saying, okay, I'm just going to run this agent all week.
You you may have a nice bill at the end of the month if you haven't been careful.
The good thing, Harvey, is that companies like Microsoft, they give you money to run some of these experiments.
So we did get a 50,000 grand from Microsoft.
So thank you, Shabad,
for that, that allows me to do a lot of these experiments.
So now I know who to call when I need some tokens.
There you go, see we're building connections here.
You know a fun fact, in the last two years the cost of the tokens have gone down 99%.
So cost is not so much of an end it will continue to do so, right?
I mean the way the things are going, it's such a competitive environment that cost would hardly be a constraint, honestly.
Yeah, whatever you want to.
Like I've seen that a lot of people are coming with the agents.
The last we were discussing it with Stefan in the last Salesforce, Dreamforce event for Salesforce,
they also are announcing their AI agents and how is everything is revolution,
making a revolution into every single industry as we discussed prior.
But I want to know how do you think the AI agents will transform the pharmaceutical, for example, research and development processes?
Harvey, do you want to take on that?
Yeah, as a doctor, one of the main things I'm always looking at is data.
How good is that data?
Is it the famous phrase in AI garbage in, garbage out?
And so if I can point the AI to start looking at certain databases and do that research for me,
Not that I'm being lazy.
If I'm trying to go through an entire amount of data information, AI can bring that data to me and I can have a strict criteria of what I'm looking for.
Then it brings that data to me and then now I can go into the weeds and see every article and make sure that that makes sense.
When it comes to clinical trials, there's so many issues, but one of them, one of the main issues
is making sure that the person that I'm trying to study is the correct person for
the right problem, for the right solution.
Having AI to be able to better predict on: is that patient qualified?
Is that person something that can be in this study?
And then once I have you in my clinical study, are you going to stay?
Are you just here for a month?
Are you going to stay the whole trial or are you just in and out?
So then to be able to use another agent to analyze the data
and say, okay, this population is more likely to stay and here's the reasons why, and then I can totally capture that.
And so there's many ways of using an agent to help during clinical trials for sure.
Thank you for that.
And Salvador, do you want to add?
Yeah, I think the dependent where we look at the pharmaceutical industry and we look at the drug development, there's different opportunities.
I think Moderna did a great job showing how much you can do with AI, right?
They developed the COVID vaccine.
from the time that they got the sequence from China, it only took two days to develop the vaccine and they took a year to then test and build the capacity.
But even the manufacturing capacity was done
with the AIs.
So I think we're moving from drug discovery to drug engineering,
where it's not so much about trying to figure out where what kind of drug we should develop is more about being
more intentional.
This is what we're going to do.
So I think there's going be tremendous change and hopefully a lot more breakthrough
therapies that actually going to cure diseases in the near future.
And then when we think about the commercialization part or even some of the clinical trial,
this idea of digital twin or digital humans, where now you can role play a patient
how they feel, what they think about a specific drug.
So you can train these models with patient data and then they can role play.
So then again, that probably gonna speed up also how we think about
bringing these drugs to the market and also what is the right patient to bring into these clinical trials.
Thank you.
So we were, yeah.
And let me just quickly add, not to forget that the Nobel Prize for this year's in chemistry
was for AI to actually discover the protein structure.
And think about it, what it would lead to.
It would actually lead to a lot of R&D, new drugs, new molecule or protein discovery, etc, that would eventually help.
And ultimately, all of that you can package within the agents as well.
So there could be research agents literally working alongside the humans and helping with this protein structure.
And then eventually that will lead to all these new drugs and treatments, hopefully.
My question now will be related to the big, I will say the big scary part of the AI, the losing jobs or it's a trending that some...
Some people believe that AI will bring a lot of layoffs due to the work that they can do, as Harvey said before.
They can do enormous tasks or a lot of tasks in a short amount of time.
Do you think that it will impact on the job market or do you think that it's, as another speaker told us, it's a tool?
that we need to learn how to use so you can re-utilize or give it more value to our workload right now.
Harvey, do you want to take on that?
Yeah.
So my favorite phrase in AI world, it's human plus AI will beat the best human or the best AI.
It's working together that will give you a better output.
The second thing I want to say is I call it the great shift.
If I'm a, pretend I'm a dermatologist.
and you're a family practice doctor and you're using AI, am I going to fire you?
No, what's going happen is AI is going to enable you to do things more in your scope and maybe broaden your scope.
And some of the things that you might have sent to me as a dermatologist will change because AI is going to start helping you to some degree.
And then me as a dermatologist, I'm going to start seeing sicker patients and more concentrated on things that I need to do.
So I call it the great shift,
then
For the job security, people are like, well, I'm going to lose my job.
My answer, Mac, is it may be a shift.
You may do your job a little differently.
And so that's my two cents on that.
Thank you.
Shabad or Salvador, whoever.
Go ahead, Salvador.
I think certain tasks are going to disappear from the job description or the job profile.
So I think certain low-level tasks like summarizing,
going through documents to extract the relevant information that we have a lot of like entry level analyst jobs.
A lot of times they do that.
I think those type of activities probably will disappear.
Like Harvey said though, you still need a human to bring the creativity, to bring the politics of organization,
to bring the influencing and negotiation components of
how they reposition that information in a better way.
So, definitely I'll say tasks
are going to disappear.
And if people are focusing on very specific tasks, then of course those people need to be re-skilled
in return to take advantage of the technology.
That is true.
Thing is that, for example, one of the survey that recently happened
it's predicted that between 2030 to 2060, half of the thing that we do today
as tasks will be done by the AI,
at least.
Or it will not appear altogether.
So the thing is that the jobs will definitely change.
And that would mean that a lot of the drudgery of the work will actually disappear.
And to be honest, not just the drudgery of the work, like even highly intelligent work, like coding, etc.
Honestly, that will also be done.
It's just that people will be much more productive now.
That means that you will probably need less.
However, that doesn't mean that the job disappears.
Well, that did not happen with the advent of computer, right?
I mean, it just exploded into altogether new areas of economy, which nobody expected before, right?
Imagine those industries, when the industrial age comes, same fears and apprehensions were there as well.
And I'm not saying that this fear is unfounded.
Yes, the jobs will change.
Yes, some jobs will disappear.
That's true.
But there are new opportunities we'll also create.
What is important is for people to understand that
there is a shift coming.
So they need to actually just essentially change with the time,
not trying to resist it, but just trying to skill yourself for the new expectation that you will have in the job.
That's what it is.
Thank you.
Thank you for that.
Stef, do you have a question for the panel?
You're muted.
Question, in fact, for the panel.
So I was listening very carefully to what Harvey said, and he as a clinician, as a doctor himself,
I guess he sees a lot of potential improvements that AI can help with in pharma and
my, you know, I like to dig, I like to give hard questions.
So my question is, considering that AI is so advanced, we still have the human factor.
And what's the problem?
Why is the not far, not, why is
Pharma so resistant to change and not getting ahead with AI.
What's the problem in integrating AI into critical processes that Pharma has like drug discovery, R&D and so on?
Because these things, like you spend a lot of money as a pharma company on this.
How is it integrated?
What are the problems?
I would like to hear your opinion guys.
Yeah, I'll jump in.
As a healthcare professional,
Healthcare is very cautious, very conservative.
And the reason mainly, obviously, there's a lot at stake.
If you make a mistake, someone can die.
The stakes are high.
It's not like, I made a bad choice and I lost some money or I maybe had to spend some tokens or made a bad stock choice.
If this messes up or hallucinates, someone may die.
So with that said, there's also the cultural aspect.
If medicine is an art and it's been this art and it has been taught from
generation to generation.
Because if you think about the perceptorship, I go to residency, I go med school and someone older is teaching me how to practice medicine,
then the culture has not changed.
We need more leaders like not to brag, someone like me that knows medicine, that understands AI,
that can combine the two, that can teach the next generation so that
culture can shift.
Anytime you bring a new technology to someone, the first thing is the fear of change.
Like, my God, I don't know what that is.
That must be the devil.
AI is horrible.
But then when you start unpacking it and educating and teaching doctors and healthcare professionals
how to use this tool, then they say, wait a second, this is good.
To your point, I love what DeepMind did with the Alpha Fold and got the Nobel Prize,
being able to discover, I truly think this is gonna open the door to personalized medicine.
The second thing that AI has done is companies are now able to produce medicines and discover them cheaper.
And some studies have said some of these have dropped down by 90%.
And so what does that mean?
If you have a rare disease that maybe let's pretend only a thousand people in the world have that disease,
is the pharmaceutical company going to go out and make a drug that may
be $10 million for a dose?
The answer is they're not.
But if I can bring that price down and I can break it down to some point that now
it makes financial sense for the pharmaceutical company, now they're going to start creating drugs
that will address that smaller population.
So it really can revolutionize the world.
That's why I'm so happy to see they got a Nobel Prize because I truly think this is going to help.
So big picture is going to just we need to change the culture and we need to educate.
Well said.
In fact, we had a guest on the podcast later, well, earlier in the year,
and he works for an orphan drug company,
he said that he's seeing way more orphan, well, orphan drug companies are dealing with rare diseases, basically.
So he's seeing way more companies pop out in like mushrooms,
because it's easier for them to deliver and like test new drugs.
specifically with AI agents and something that he mentioned would be a company like,
for example, Turbine AI, where you don't need that many, well, you still need clinical tests,
but like basically you could sort of use some VR and you could use like a Turbine and
play around the model and how the actual drug molecules react with like biological targets like
people or like, you know, animals without even
administering it to any biological targets.
And that's amazing because that opens the door to way more pharma, way more R&D, way more development.
Yeah.
Would you guys agree or did you have actually experience with something like that
where you had an AI fully built maybe within the last few years and then you had something like
a breakthrough or something?
Well, at least in the RNG space.
That's a question for everyone.
some of you, Harvey, you should have. Don't be shy.
Yeah, I think there are several companies.
One is called Insilico where they're really using AI to do drug discovery.
I think finally they have a few compounds in the clinical development.
I'm not sure, I guess, depending on which is the definition of an agent,
but clearly there are companies now being able to, especially around this technology, being able to predict
the protein folding.
that are coming up with new drugs that are moving into the clinic.
A lot of times, big pharma especially, they're probably going to wait
until this technology goes into phase one, phase two, and then they buy them up.
So the model for big pharma, a lot of times, has been kind of a big bag investment where
the goal is not to innovate too much, but is really to go out and buy innovation from
smaller players and then build those up because of the commercial
capabilities.
But I just see quite a bit of activity now
across our clients and other companies that we're talking to.
That AI, I guess, thanks to Chat GPT, can people understand it better now?
And now they're seeing the potential in the day to day.
So I they're being little bit more bullish on also investing across all different decision-makings in the industry.
Thank you.
Guys, we have a question from the audience.
One second, I'm going to showcase.
It says, from Isaias Cuello, considering AI agents, what is the real balance between automation and human expertise?
and how to keep the balance?
Who wants to take on that?
Harvey?
I can see you smiling.
Yeah.
Again, I'm going repeat what I said earlier.
I truly believe it's the human plus AI, the combination that's going to give you the best.
It's my clinical gestalt, my human intuition, my human ability to see things,
plus using AI to create what alpha falls out there and what
what molecules and then putting those together and saying, wait, we can discover this or we can create this.
Finding the balance, again, I truly, I'm stuck on it.
I truly believe it's about education, educating the doctors and the healthcare professionals and people making these drugs.
What are the limitations AI?
What are the bias?
How can we make this more transparent so that when I make that decision, I'm able to take it to the next level.
Quick example.
If I'm using today, chatGPT, and tomorrow I'm using Claude and then I'm using Gemini, there's all these models.
I don't know which one does what.
But if we had a food label equivalent that now I look at it and say, okay, this is the positive, this is the negative, these are the bias.
This is why it's not transparent.
This is why this model would work.
This is why it wouldn't.
Now I'm educated.
Now when I'm creating the different drugs, now I'm able to know which tool to use and which one I should and why and what population.
Really powerful stuff.
And that's how we could find balance.
I get some other one.
Yeah, I think it is still a world where the human needs to be in the loop.
My recommendation is if the agent can automate it and can do the task and let the agent do it.
Because I think like Shabad said, I think in the real world, the AI agents are going to take all the boring work away from our plate.
And then that allows us to focus on the things that we like and join doing the most decorative,
creative work, but also I think a lot of times,
they don't have enough time to think through different scenarios,
enough time to maybe go after different activities.
I think the agency is really gonna increase the productivity, but also is gonna allow us to really explore
bigger domains, ask more questions, and then as humans we're also gonna get
better and probably do work that we enjoy more moving forward.
Nice.
We lost you, Shadab, but you're back.
So that's a good thing.
We have another question coming from the audience saying,
who will validate what AI is giving us as a result that is relevant?
And that's why we need the human in the loop.
There you go.
He came back with an answer.
I was here all the time.
It's just that you could not see me.
was just like an agent just keeping an eye.
But yeah, that is exactly why we need a human in the loop because there are
liabilities and particularly the industries which are regulatory like the pharma and the healthcare
and bunch of other industries.
You definitely want to have the human in the loop.
Not just that, there are other aspects as well.
You need to also make sure that not just that the
the answer that are being given are relevant,
but they're also accurate, they are bias free, they are certified.
So it takes a lot to get those data prepped.
So data is how we said earlier, right?
Garbage in, garbage out.
So we need to make sure that the, for example, the diagnosis results, they are based on those demographics, etc.
So it takes a lot.
You have to design a system.
to take care of these kind of things.
There's no hallucination.
So that's human in the loop is required while developing it.
Human in the loop is required while actually interacting with it
so that people get the certified answer.
And that's why there is, I would say,
for example, if you have to go and get a diagnosis done
or get a health care related answer, I would not just solely rely on an agent.
I would like an
to be a doctor with an agent.
That's what my preferred model would be.
And that would be, I think, probably the most optimal model in the future.
I think I guess one question is who validates what the humans produce, right?
There's also a check there that needs to happen.
And then the other one is shouldn't the AI validate what a doctor is prescribing, right?
If we look at seroideology, where the AI is really better at detecting serointestinal
cancers and if that has access to all this information shouldn't the AI
or the doctor validate what he's prescribing with AI that can look at all the drug to drug interactions,
maybe connect the dots across the whole patient history.
So I think there is a part of somebody needs to validate what the AI is creating,
but there's also a scenario where the AI is going to be required for people when they're
prescribed to make sure that they have a complete picture of the patients.
they have an agent to understand all the drug to their interaction to make sure that what you're prescribing
really makes sense for that patient.
If not, it tells you, hey, maybe you want to consider this X, Y, Z.
And then the physician can go back and say, OK, does it make sense what I'm prescribing or
is there a better option that I might consider?
And I want to add to this.
think of the whole lifecycle.
Who has to do it?
So obviously, we need the data scientists to make sure they validate the data saying this is doing correctly.
For example, there's something called data drift.
I can make a model.
It's working great.
But then with time, it'll start drifting and it'll start creating something that it wasn't meant to do.
And so having those data scientists analyzing that AI,
making sure that it's doing the task that it was supposed to do.
Second, we need domain experts,
pharmacists, doctors, people that are in there, scientists that are looking at that AI,
making sure that that does make sense, using that human in
the loop, making sure it's doing reinforced learning to make sure that it's going down the path as supposed to.
Having the open mind to understand AI, but then knowing when to use it and not.
Obviously, regulatory, we need the FDA or government agencies
are gonna regulate this and make sure those bodies are doing their part.
And then the last part, obviously, the whole reason we do all of this
is at the end is the end user, the patient.
Make sure the patient is able to look at it and saying, yes, it is doing what it's supposed to do
and being able to give feedback back to the system so that way we can
create that whole ecosystem to make the best AI product.
Thank you.
Thank you for that.
We have two more questions.
Our attendees are waking up or something.
I don't know.
The first one is, can AI be effectively applied in a patient-centric manner
to enhance health care outcomes and improve patient experience?
Who wants to tackle that?
Harvey?
So at the end of the day, I
I'll joke with you guys.
It's all about communication.
When you fight with your spouse, when you fight with your kids, your boss, it's about communication.
So how can we use that AI to better enhance patient-centric manner health care?
I truly think it's leveraging AI.
This is a great example how people from around the world are listening to this.
And if I'm your doctor and I give you, I'm in Texas, Texas examples, you'd be like, I don't live in Texas.
I have no idea when he says football.
I think of soccer as football.
But if I can leverage AI to speak your language, your examples,
your accent, your language that is primary, now you'll understand.
Now I'll have a huge impact of how I deliver health care because you'll be able to hear me,
but not just hear me, you'll be able to understand me.
And if you understand me, you're less likely to come back into the healthcare system
in the sense that it's like, you know what, I didn't understand what the doctor said or how
to take my meds or X, Y.
The second part to this,
is having things like edge technology or things like this phone or iWatch
that's telling me, hey, you didn't sleep enough.
Like every morning I look at it, I'm like, all right, I probably need
to sleep a little bit more if I want to live longer, if I don't want to gain weight.
And so using AI to now notify my watch and saying, hey, Harvey's not sleeping.
And then instead of being a reactive health care, we create a proactive health care.
So now the doctor is texting, calling, saying, hey, you need to increase your meds.
Hey, you need to come in sooner.
And so that's how we'll be able to use this, like the question asked.
like that.
I think my doctor will be crazy, crazy sending me messages of things I'm doing wrong.
What can you do?
I would like to mention about a recent, I think there was
a paper that was written, I think most of you would already know, but it was fascinating.
It was, I think,
paper about the use of AI in the healthcare setting.
It was actually a paper based on, think 50, you know, I think
the doctors and the physician assistant trying out AI for diagnosis.
And I'm going to paraphrase some other things.
going to, I've forgotten the size numbers, so pardon me, but here it is, right?
The outline was that half of the doctors and assistants were given the
no tools except for the conventional tools that they have used.
The other half were given AI, chat GPT access to actually use that to help with the diagnosis and the treatment plan, etc.
And surprisingly, you would assume that the AI folks would have gotten it better.
They actually did.
It was just marginally better.
Now, which was surprising.
But on the other hand, we actually ran another group test where only AI,
some AI experts, not the doctors, but the AI experts actually used AI.
And the AI alone, Chai GPT alone was able to give 15 % better results.
So what does that mean?
Like AI is better than human and human in the AI loop with AI as well?
No, actually the important thing was that it is important that the adoption is done
Right.
So for example, everybody in the healthcare setting, whether it's a doctor, assistant, nurses,
everybody, you can't just throw a tool at, You need to actually teach how to
properly use it.
Secondly, there is an adoption, right?
I mean, you can't expect somebody highly qualified to just accept what a chat bot says really.
So there is that adoption process.
So if we do the right adoption and we teach everybody to use it right, there is a lot of upside here.
15 % more, imagine, better treatment plan or the diagnosis.
So there is a lot to gain, but there is a lot of work to be done.
It's not just creating an account and then let them run wild with it.
That's not going to how it works.
That's the balance we need to find, the right speed, the right skilling, and the right adoption plan.
Thank you.
Thank you for that.
Salvador, do you want to add something?
Yeah, I think there was another study coming out of the UK, NHS, where
patients got a chance to talk to the physicians or got chance to talk to the chatbot.
They're going to kind of where you're saying, Harvey, that the chatbots can be empathetic to the patients.
And even though they're not talking to technology,
the way they communicate and the amount of information they have
they can really find to that message.
they don't have the stress of having seen 50 patients throughout the day.
They don't have the stress of the fault, maybe with the wife or the husband the day before,
maybe they had a nice shift and they're tired from that.
They don't have all of that, right?
So they bring like the best qualities if provided with the right tools
to engage with the patients and provide answers that are a little bit more empathetic than human.
So.
We're seeing that in these studies, I think also from our customer service,
I think as one of the industries are going to be probably disrupted quite a bit because a lot of the
engagement probably can be done better by AI.
So I think that also is going to have an impact also as we move forward on
when does it make sense to talk to a human versus when does it make sense to talk to a machine,
especially when it gets specific tasks done.
And I do want to add that
It's just because I'm a doctor and I hear that study always quote and I put it on all my presentations.
I want to convey that it's again, it's about the communication.
When a doctor answers your question, unfortunately, they may be quick.
The average time in the United States with the doctor is 13 minutes.
So that interaction is very limited.
But AI can give you a whole book on your subject.
And then the other thing I'm going to encourage you to ask for the most for your health care is the following.
Before you see your doctor.
Obviously, make sure there's GDP and HIPAA and all that stuff.
Ask your medical questions and say, I have diabetes, hypertension.
What are the top questions I need to ask my doctor?
Because when you're there in those 13 minutes, you want to maximize.
So I'm going to encourage you to use AI for your health care to improve.
I love that.
It's very short, 13 minutes.
I always say hi and I'm already out of.
or I speak a lot.
Next question is, we have another question for our attendees.
Appreciate the insights.
How do you anticipate AI agents will be used initially for pharma,
drug discovery, R &D, help sales teams or something else?
Actually, everything, to be honest, because AI agents, you know, AI agents,
you can design the AI agent for everything, whether it's a process, whether it's like a knowledge board.
And remember that AI agents can be very, very smart as well.
They can actually even replicate.
There is an architecture pattern called AI swarm
in which the AI agents, based on the problem that you throw at them,
they can actually reason, they can create a plan.
And if they have to be, for example, if they have to,
let's say, create the new agents for doing some tasks, they can actually do that.
So AI agents can be very, powerful from that regard.
So I would say that they are not limited to any particular function or aspects.
They can be part of anything.
What is important is to identify the purpose of it.
So most of the agents are very specialist, specific tasks.
So for example, a travel app or let's say for example, patient onboarding.
That could be a specific process.
Within the patient onboarding itself, there could be multiple agents,
not just one agent, working together, depending on what the task will be required.
And that's just one area.
There could be tons of areas like this where you can actually do the work.
But yeah, everything is relevant for the agents.
Thank you.
Salvador, do you want to add?
Yeah, again, I think there's a difference between just applying AI versus applying agents.
I think in terms of agents, this is going to be how pharma engages
with the patients, the physicians, the different stakeholders in the ecosystem.
I think that's going to play a role,
especially around when we're talking about specific diseases or specific drugs.
the agent will be able to give kind of like HIPAA compliant, legal compliant answer.
I think we're also seeing in the customer engagement,
using AI to connect the dots and figure out, for this physician,
what is the right detail aid?
What are the topics that this physician likes to talk about?
So it can help in the sales rep and the medical first people to have the right message for the right physician.
We go in-house, I think there's a lot of use cases around
Training and onboarding of also employees.
So like Shabad said about onboarding a patient
that maybe needs to take different medications or needs to use a medical device,
there is an onboarding there.
And then our focus of the Atacana is around decision-making, right?
How do you use these agents to process the information
and have the right information at the right time when you're making the decisions?
Thank you.
Stef, do you have a question for the speakers?
Do you want to follow up?
I always have a question.
In fact, I was commenting on AI swarm in the chat.
So that's a really useful tool, ladies and gentlemen,
for pharma and beyond pharma if you're also in tech.
I wanted to show you this.
So some of you maybe know, some maybe don't.
It's book written by a famous surgeon.
His name is Atul Gawande.
The book is called The Checklist Manifesto.
He was a surgeon.
He talks basically, the whole book is about checklists, right?
And it's more of an insight than anything else.
He talks about the fact that in medicine you use checklists, in aviation you use checklists,
in many other fields where there is a high responsibility on people, use
checklists.
And I was wondering, maybe again, it's sort of an open question for everyone.
Maybe with, we could...
implement AI better in pharma in particular or in many industries if we had a checklist about
how to use pharma and how to use AI and how to implement AI in the right way maybe
using a checklist that could be a good role model what do guys think?
I know when I was coming up the ranks in med school, was a big, big issue.
I would always say we need to work on checklists, make sure things perform.
I know surgeons are notorious for checklists and I mean it in a good way
being able to go from A to Z, making sure that everything is done the right way in the right sequence.
That way you're not taking off the wrong leg, cause you're not following the checklist.
And it's been really big on in the airline industry, obviously, before they fly any plane, they go through their checklist.
I think it's very important in medicine, having AI create that checklist
and then applying that to AI agent in particular, we could do that as well.
There's already a science to this when you look at how to discover medicine, how to do things, it's very A through Z.
and creating the AI to make sure you're going through the checklist.
So I totally agree.
And I'm already on Amazon and I'm going to order that book for me because I love reading.
Nice, nice.
I'm being on checklists as well, but I'm not a doctor.
I'm being on checklists for everything.
My lists have lists and so on.
Salvador, what are your insights regarding this?
Yeah, I read the book and definitely had an impact on how we think about
educating the team on what they need to do to get the right outputs or the right deliverables.
I think with AI, I think there is a place for it.
I think the challenge for us in terms of AI is that every other week there's something new
that comes out that has a new different skill set,
new capabilities.
So if you have the right checklist for the AI today,
probably in a couple of months, we'll already be outdated.
So for us, it's been more about thinking, what are the processes,
maybe the steps to put in place that as the new technology,
you're to take out the cartridge, you take out the
GP3.5, and then you put the GPT next inside.
And then that way, you don't have to rethink the whole process, but you only
improve the whole process by now swapping these different LLMs as they become more powerful.
So yeah, it is a good question trying to see how they will evolve as the technology also evolves.
Thank you.
Thank you for that.
Steph.
Well, Shadab didn't answer so I'd like to hear that
No, I think you know I won't get that book but I can tell you that
I use the AI a lot already in lot of things and I'm
gonna take Harvey's advice on Using the AI to prep before the doctor's exam
and you know give them a hard time with all the questions, right?
Really?
So but yeah, I think that is very very cool and remember that I think now that
If you use, for example, Apple iPhone, now you have Apple Intelligent on the phone.
And I use a lot of these devices for fitness monitoring, health monitoring and things like that.
I think, you know, AI is, whether you want it or not,
it's going to be everywhere and it's going to be on your devices and you can actually really make a good use of it.
Remember your Apple watch itself is going to have a lot of AI and you can actually configure it.
It can monitor a lot of vital signs, etc, as well.
Of course, I think that the question is whether it can do the diagnosis.
I think a lot of companies will stay away because of the compliance and others.
So think it will be fun to see because one aspect that we don't have very clearly spelled out
is that, what will be the regulatory framework around all these using the AI in the,
you know, in the diagnosis, in the personal space, et cetera.
It would be fun to watch.
you know, how will that emerge?
Will it slow down?
Will it enhance the innovation phase?
I don't know, but it will be fun to watch.
Yeah, a recent study on AI usage and AI
actually showed that AI is really good with gathering research and giving you the data, but
it's still people who have to take the decision.
So it's, you know, it's human led AI.
And although you can gather a lot of data
and Harvey mentioned here that
if you have garbage data and you feed it to the AI, you're going to get garbage out.
A very easy concept.
So no matter how much humans or AI you're going to have,
if the data is not enough, or it's not statistically significant data, then you cannot be objective with your AI.
And to be fair, AI does its fair part of creating things out of thin air, which do not exist.
So that's why you always got to have a human and maybe another human to make sure that you do not think that
the same mistake again.
Guys, we have a question from the audience from Stefan.
What are the idea?
What are the ideas to think beyond applying AI to the way we do business
and to rethink the way we serve customers and patients in combinations of AI and the human component?
It depends honestly on a lot of things.
In my experience, what I've seen is there are times where it's easier to just interact with AI than the human to be honest.
Sometimes it's pain working with humans in certain areas.
On the other side, sometimes it's pain.
Honestly, to work with the AI, you just want to talk to someone because your problem may not be
Because like, for example, I'll tell you, I was planning a international itinerary and it was complicated.
And all I could get was AI agents to try to help me.
And they just, I just couldn't explain properly sometimes because in order to use AI effectively, you need to know how to use AI, right?
I mean, how you instruct him because, you know, if you can't give that AI is not always going to...
read the sentiments and everything properly.
sometimes it's just not going to give you the right answer if you don't know how to ask the right question.
So that's important.
Many times customers don't know that.
And that's why human in the loop in customer scenarios is still important.
Patients are still important.
On the other hand, there will be people who will be just comfortable asking directly to the AI.
They don't want to talk to a human.
It could be a patient discussing something sensitive, etc.
In those cases, you know,
AI could be first channel to actually start with, right?
mean, so, you know, it depends.
I would say that it's hard to say that one size fits all, that, you know, just, you know, it really depends.
I think what it will do is it will give a lot of new options
for the customers and patients to get their problem solved, which is what I'm excited about.
You know, they can choose the channel.
I would say that
That is where we should be, not just eliminating the channels, like
put bots rather than humans completely, or the other way,
it should be multiple of channel because different
people have different comfort level working with the channels.
Yeah.
And I just want to add real quick to it.
Leveraging the last part of your question, leveraging humanity and AI, how to make that better.
And I'm going to stick to healthcare, love doctors to death, but not all the doctors have the same empathy.
And that empathy, you can argue that it's hard to teach.
I'm working on a product that will do the following.
I will take the doctor, healthcare professional,
and use AI to analyze the way they speak, the way they deliver news,
and then have the AI teach that doctor to be more empathetic.
And that's a great example of how to use a human using AI and to make that more humane.
Because you're like, you're using AI to make that person more human?
Yes.
And so that's just a quick example.
Thank you.
We have a question.
Someone wanted to say something.
Yeah, I like this idea where you can, and this has been a lot of cases,
not just between the physician and the patients, this idea of role playing with the AI, right?
So I think a lot of people that have stage fright or the one that figure out how to have a difficult conversation,
or you can start role playing that with the AI and then you can go
into a meeting, be more prepared, like how you're saying
as the strategic data, which questions was the physician was also the same thing.
if you're facing a negotiation, you can role play with the LLM
and help you think through, OK, I can have better interaction in the next meeting than going to
based on this context.
Thank you.
We have five minutes, so we have one big, long comment.
I will try to read it and showcase it here.
It says from Nathan, in the practice of medicine, ensuring the accuracy and reliability of medical results is a paramount.
Validating AI's responses require a collaborative approach involving multiple stakeholders.
While AI offers innovation solutions, medicine is still fundamentally driven by human expertise and ethical considerations.
Studies and research are often funded by entities with best interests, necessitating transparency and rigorous validation to maintain integrity.
Transparency is essential for both AI and human researchers to enhance accountability and trust.
As AI integrates into healthcare, its role is to assist providers by handling administrative tasks and supporting clinical decision-making.
And it's the last part.
It says AI can
... accountability...
One second.
Decision making.
can identify patterns that may be overlooked due to social or cultural nuance, enabling more comprehensive care.
However, AI contribution may be continuously validated by qualified medical professionals to ensure accuracy and safety.
Ultimately, validated AI's responses in medical contexts requires a balanced
approach that leverage AI's capabilities while maintaining the essential role of human judgment and oversight.
I hope, guys, you were able to listen and read it.
Commentary rather than the question.
And I would say I agree.
That's all I can say.
Because I think, yes, I mean, that's why it will be fun to see how the compliance and regulation will emerge.
The way we see is that AI is potential.
I think that last year it was 4 % adoption.
It already has jumped to 22 % adoption.
It is already extremely fast so far.
I think there are areas in which you still are going to do the wait and watch.
And health care could be one of those segments.
The question is that how soon the regulations will emerge.
We don't know.
Europe is.
Too much regulating, US is relaxed, but we will see.
It is an area still too much.
For example, I'll tell you that recently there was a news about a teen killing himself because of the interaction with chatbot.
So these kind of things, it's an example of ultimately in a setting which could affect your health.
So mental health is also part of the health care overall.
We will see.
This is now in the court.
It will be decided whether AI board is actually responsible or not.
But these kind of legislation that are happening, that will define how AI will be used in the future in the health care setting or not.
So something to watch for.
Thank you.
Thank you for that.
Salvador, Harvey, closing comments regarding the comment for our attendee?
Yeah.
Good.
I got a hard stop at four, but go ahead.
Technology is evolving.
Regulation is going to be evolving.
Clearly, there is a human in the loop component that cannot be taken away right now.
But I the key takeaway for me is we need to embrace the technology,
figure out how to leverage it for our advantage.
And of course, it's changing so rapidly that we're going to see different regulation
different guardrails are going to be put in place to mitigate any risk.
Great.
And all I was going to say is this is growing so fast.
Come to seminars like this to learn, follow people that are here.
The more you follow them, the more you're learning because it takes a village to learn this information.
It's growing so fast and no one person really is an expert, but together we are an expert.
So with that, I appreciate being here.
Thank you.
Thank you all.
Thank you, our attendees for tuning in and don't forget to follow these guys.
We have set up their podcasts and things.
So.
Follow that and go to Salvador's event.
We will know if you haven't been there.
So with that, thank you all and goodbye.
All right, thank you.