The WHO on AI in Pharma: Power, Profit, and Global Risk

Host 1: All right. Deep Divers, ready to go deep?

Host 2: Let's do it.

Host 1: Awesome. So, today we're diving into the world of AI and pharmaceuticals.

Host 2: Sounds fascinating.

Host 1: It is. Our listeners sent in this WHO report on the use of AI in drug development and delivery, and well,

Host 2: It's a pretty dense report.

Host 1: Yeah, it is. So, we're going to break it down and figure out exactly how AI is changing the game in healthcare.

Host 2: I think the biggest takeaway for me is that this isn't just, you know, some far-off futuristic thing. Like, AI is already a part of basically every stage of the pharmaceutical process.

Host 1: You're totally right. It actually says here that pretty much every single new medication coming out today is influenced by AI in some way.

Host 2: Uh-huh.

Host 1: So, it's like even if we don't realize it, AI is already impacting the meds we take. Crazy, right?

Host 2: Yeah, it is. So, can you give me an example of that?

Host 1: Sure. So, think about clinical trials.

Host 2: Oh, right, right. For testing new drugs.

Host 1: Exactly. AI is being used to like, uh, streamline patient recruitment, to analyze all that data you get from trials, and even to reduce bias, you know, to make sure the results are super accurate.

Host 2: Yeah. So, it's like it's making the whole clinical trial process smarter and faster?

Host 1: That's exactly it. Which means new treatments can get to patients who need them much quicker.

Host 2: Okay. So, it's not just about AI like designing drugs from scratch, it's about streamlining the entire process.

Host 1: Bingo. And it's also about doing things that we couldn't even dream of before, like take DeepMind's AlphaFold 2.

Host 2: AlphaFold 2. Yeah, I've heard of that.

Host 1: It basically solved this problem, the protein folding problem,

Host 2: Yeah.

Host 1: that had scientists stumped for like decades.

Host 2: Huge deal.

Host 1: Huge. And it's going to change how we understand and treat so many diseases.

Host 2: So, AlphaFold 2 figured out the 3D structure of what was it, almost every protein we know of?

Host 1: Yeah. Think about it. That changes everything for drug discovery.

Host 2: No kidding. And the cool thing is we're already seeing how AI is finding actual new drugs. The report talks about abaucin, this new antibiotic.

Host 1: Oh, yeah, for those superbugs, right?

Host 2: Yep. Abaucin specifically targets Acinetobacter baumannii, which is like one of the worst superbugs out there.

Host 1: The kind that are resistant to like pretty much everything.

Host 2: Right. So, AI is literally helping us fight back against these, you know, potentially deadly infections.

Host 1: That's incredible. Okay, so we've seen AI in clinical trials, in drug discovery. What about personalized medicine? I feel like that's, you know, the Holy Grail of healthcare these days.

Host 2: Yeah, totally. And the report dives into how AI is making personalized medicine a reality.

Host 1: So, what's the basic idea there?

Host 2: Well, the idea is that instead of giving everyone the same treatment for a certain disease, we can use AI to tailor treatments to each individual person, you know, based on their genes, their lifestyle, everything that makes them unique.

Host 1: So, it's like having a custom-made treatment plan.

Host 2: Exactly. Like imagine cancer vaccines that are designed just for you based on your specific tumor's data.

Host 1: Well. Okay, so how does AI actually make that happen?

Host 2: So, AI algorithms can, you know, sift through mountains of data about you from your genes to your medical history, even things like environmental factors.

Host 1: And then what, it spits out a personalized treatment plan?

Host 2: Well, kind of. It helps doctors understand what's going to work best for you specifically.

Host 1: So, it's like having a team of experts crafting a therapy just for you, but it's all powered by AI.

Host 2: You got it.

Host 1: Okay. So, personalized medicine sounds amazing, but there's always a catch.

Host 2: Right. Well, one thing the report brings up is the cost.

Host 1: Ah, right. Because developing drugs is expensive even with AI.

Host 2: It is. And even though the report talks about how AI can make drug development faster and cheaper, there's no guarantee that those cost savings will actually lead to lower drug prices for patients.

Host 1: So, even if AI is saving the pharma companies money, we might not actually see those savings.

Host 2: Yeah, it's tough to say for sure, but there are a lot of factors at play, like market demand, you know, competition between companies, and patents, and all that.

Host 1: Right. So basically, even with AI making things more efficient, there's still a lot of work to be done to make sure that everyone can afford these new treatments.

Host 2: For sure. And it makes you think, right? Like how do we make sure these innovations are actually accessible to everyone, not just people who can afford it?

Host 1: That's a really important point. Okay, so we've talked about the exciting stuff, like new drugs and personalized therapies, but what about the potential downsides of using AI in healthcare, like what about all that data that's being collected?

Host 2: Yeah, data is like the fuel for AI, right? But it also brings up a whole bunch of ethical concerns.

Host 1: Like privacy.

Host 2: Exactly. The report really stresses the need for, you know, strong safeguards to protect patient information. We need to be absolutely sure that this data is being used ethically and responsibly, not for like, you know, advertising or other things.

Host 1: Right. So, people need to know that their medical data is safe and sound and not being used for anything shady.

Host 2: Absolutely. And it's not just about keeping data secure, it's also about transparency.

Host 1: Oh, interesting. So, like knowing how the data's being used?

Host 2: Yeah, and also being able to understand how these AI systems are making decisions. The report actually uses the term black box algorithms.

Host 1: Black box algorithms, huh? What does that even mean?

Host 2: It means that, you know, you might feed an AI system a bunch of data, like medical images, and it might spit out a diagnosis that's totally accurate, but you have no idea how it got to that conclusion.

Host 1: So, it's like the AI is making decisions, but we don't know how.

Host 2: Yeah, it's like a mystery, even to the people who designed the AI.

Host 1: That's kind of scary, especially when it comes to our health. Like how can we trust a system that we don't even understand?

Host 2: I know, right? And that's one of the big challenges that researchers and regulators are dealing with right now. Like how much transparency is enough? Some people argue that, well, as long as the AI is getting it right consistently, does it really matter if we don't understand every step of the process?

Host 1: That's a tough one. It seems like there's this like balance to find, you know, between harnessing the power of AI and making sure that it's being used in a way that's ethical and transparent.

Host 2: Absolutely. And that's a conversation that's happening right now all over the world.

Host 1: Okay. So, AI is powerful, but we need to be careful with it.

Host 2: Yeah, it is.

Host 1: Got it. But the report also talks about how AI can help us tackle some of the toughest challenges in medicine, right?

Host 2: Uh-huh. It mentions using AI to fight neglected tropical diseases.

Host 1: Oh, like diseases that don't get as much attention or funding?

Host 2: Yeah, exactly. The report highlights the work of the Drugs for Neglected Diseases initiative, DNDi.

Host 1: DNDi.

Host 2: Okay, they're using AI actually in collaboration with the team behind AlphaFold 2 to find new drug targets for diseases like Chagas disease, which is a big problem in Latin America.

Host 1: So, it's like AI is shining a spotlight on these diseases that have been, you know, overlooked for so long.

Host 2: That's what's so cool about it. It's like we finally have the tools to really make a difference in global health.

Host 1: But even with AI, developing new drugs is still expensive, right? So, how do we make sure that these new treatments for neglected diseases actually get developed and reach the people who need them?

Host 2: Right. It's not just about the technology. The report really pushes for things like government incentives and funding mechanisms, you know, to encourage companies to invest in these areas.

Host 1: So, it's got to be a team effort.

Host 2: It does. We need technological breakthroughs and policies that make it worthwhile for companies to develop these lifesaving treatments.

Host 1: All right. So, we're starting to see the big picture here.

Host 2: Yeah.

Host 1: AI has this incredible potential to revolutionize healthcare. But we also need to be really thoughtful about how we use it.

Host 2: Exactly. We need to be careful, we need to be ethical, and we need to make sure that everyone benefits from these advancements, not just a select few.

Host 1: Very well said. So, Deep Divers, that's kind of our intro to the world of AI and pharmaceuticals. Lots to think about, right? It's like we need this whole new playbook for how to do healthcare in the age of AI.

Host 2: Definitely. I mean, this report keeps talking about the need for like stronger governance frameworks.

Host 1: Yeah, governance frameworks, that sounds kind of, uh, complicated.

Host 2: It is, but basically it means we need rules and guidelines for how AI is used in drug development and healthcare in general, you know, to make sure everything is ethical and responsible.

Host 1: Yeah. Right. Right, because the technology's moving so fast, it feels like the rules can't keep up.

Host 2: Exactly. That's one of the biggest challenges the report talks about, like how do you regulate something that's constantly changing?

Host 1: Like trying to hit a moving target, right?

Host 2: Yeah, exactly. And the old ways of doing things just don't really apply anymore.

Host 1: So, what do we do? Like how do we bridge that gap between the rapid pace of AI and the need for, you know, rules and regulations?

Host 2: Well, the report suggests a couple of things. One is that we need a more, what's the word, agile approach to regulation.

Host 1: Agile, like flexible?

Host 2: Yeah, flexible, adaptive, you know, something that can evolve as the technology evolves.

Host 1: Makes sense. And I guess that also means we need to be having these conversations about ethics and AI like constantly.

Host 2: Oh, absolutely. And it's not just researchers and developers who need to be part of this conversation. It's policymakers, it's patients, it's really everyone.

Host 1: Because at the end of the day, AI is going to impact all of us.

Host 2: Exactly. And that's why education is so important. You know, we need to make sure that everyone understands the potential benefits and risks of AI in healthcare, so we can all make informed decisions.

Host 1: Okay. So, education, agile regulations, what else?

Host 2: Well, the report also talks about the importance of international collaboration. You know, AI development doesn't stop at borders.

Host 1: Right. So, the rules can't either.

Host 2: Exactly. We need some kind of global consensus on how to regulate AI in healthcare. Otherwise, things could get really messy.

Host 1: Okay, so we need global rules, but even then, how do we actually make sure that AI is being used responsibly? Like once these systems are out in the world, how do we keep them in check?

Host 2: That's the million-dollar question, isn't it?

Host 1: It is. Like what's stopping some company from using AI unethically if it means making more money or something?

Host 2: Well, that's where things like transparency and accountability come in. The report talks a lot about the need for these black box algorithms to be more open and explainable.

Host 1: Right, the black box thing. We talked about that before.

Host 2: Yeah, basically we need to be able to understand how these AI systems are making decisions so we can trust them and so we can hold the developers accountable if something goes wrong.

Host 1: So, no more AI mysteries.

Host 2: Well, it's not that simple. Some people argue that even if we can't understand exactly how an AI system works, as long as it's consistently producing accurate results, maybe that's good enough.

Host 1: Hmm. I don't know about that. It feels risky to just trust a system blindly.

Host 2: I get it. It's a tough balance, right? We want to take advantage of AI's potential, but we also need to be cautious and make sure it's being used for good.

Host 1: Definitely. Okay. So, transparency and accountability are key, but what about the actual technology itself? Like are there any ways to make AI systems inherently more trustworthy?

Host 2: Yeah, actually the report talks about this idea of open-source machine learning.

Host 1: Open-source, like anyone can access the code?

Host 2: Exactly. So, instead of these algorithms being locked away in some company's secret vault, they're out in the open for anyone to see, use, and modify.

Host 1: Wow, that's pretty radical.

Host 2: It is, and it has a lot of potential benefits. First of all, it promotes transparency because everyone can see how the AI system works. There's no more black box mystery.

Host 1: Right, so you can actually see what's going on under the hood.

Host 2: Exactly. And it also encourages collaboration because researchers from all over the world can work together to improve these open-source algorithms. You know, it's like a global team effort to make AI better.

Host 1: That's pretty awesome. And does this open-source thing actually happen in healthcare? Like are there examples of this?

Host 2: Yeah, actually there are. For example, there are some really cool initiatives using open-source AI to develop diagnostic tools, you know, like tools that can analyze medical images and help doctors make diagnoses.

Host 1: And these tools are just like freely available?

Host 2: Yeah, pretty much. The idea is to make these tools accessible to everyone, especially in places where there's a shortage of doctors or specialists.

Host 1: So, it's like using AI to democratize healthcare.

Host 2: That's a great way to put it. It's about making sure everyone has access to the best possible care regardless of where they live or how much money they have.

Host 1: I like that a lot. Okay, so we've got open-source AI, we've got transparency, accountability, international collaboration. It feels like we're starting to build a framework for, you know, responsible AI in healthcare.

Host 2: We are. And it's important to remember that this is an ongoing process. You know, the technology is constantly evolving, so we need to keep having these conversations and keep adapting our approach.

Host 1: Right, it's not like we could just figure it out once and then we're done.

Host 2: Exactly. It's an ongoing challenge, but it's a challenge worth tackling because the potential benefits of AI in healthcare are just enormous.

Host 1: They are. Okay. So, we've talked a lot about the development side of things, like discovering new drugs and making them more accessible, but what about safety? Like once a drug is out there being used, how do we make sure it's actually safe for people to take?

Host 2: That's a really important question, and it's something that AI can actually help with. The report talks about how AI can be used to improve drug safety monitoring or pharmacovigilance.

Host 1: Pharmacovigilance. That's a mouthful.

Host 2: It is, but basically it means keeping an eye on drugs after they've been approved to make sure they're not causing any unexpected side effects.

Host 1: So, it's like ongoing safety checks.

Host 2: Exactly. And traditionally, this has been a really labor-intensive process. You know, you have to collect data from all sorts of sources, like clinical trials, patient records, even reports from doctors and patients themselves.

Host 1: Sounds like a lot of paperwork.

Host 2: It is. But AI can help automate a lot of this process. You know, AI algorithms can analyze massive amounts of data to look for patterns and red flags that might indicate a safety problem.

Host 1: So, it's like AI is playing detective.

Host 2: Yeah, kind of. And it can be a lot faster and more efficient than humans at spotting these potential issues.

Host 1: Which means problems can be caught earlier and hopefully prevented from harming more people.

Host 2: Exactly. It's all about being proactive and using technology to keep patients safe.

Host 1: Mhm, I like that. Okay, so we've covered a lot of ground here. We've talked about AI in drug discovery, in personalized medicine, in safety monitoring. It really feels like AI has the potential to transform every aspect of the pharmaceutical industry.

Host 2: It does, and it's not just about pharmaceuticals, it's about healthcare as a whole. You know, AI is already being used in so many different areas, from diagnostics to surgery to even mental health.

Host 1: It's mind-blowing when you think about it. Like the possibilities are almost endless.

Host 2: They are, but with all this potential comes a lot of responsibility. You know, we need to make sure that we're developing and using these technologies in a way that benefits everyone and doesn't exacerbate existing inequalities.

Host 1: That's a really important point. It's like we can't just get caught up in the excitement of the technology, we need to be mindful of the ethical implications and make sure we're not leaving anyone behind.

Host 2: I completely agree. It's about using AI to create a more just and equitable healthcare system for everyone.

Host 1: Okay. So, big picture time. We're talking about a future where healthcare is more personalized, more precise, and hopefully more accessible thanks to AI.

Host 2: Exactly. And it's a future that's within reach, but it's going to take a lot of hard work and collaboration from all of us to make it happen.

Host 1: All right. So, Deep Divers, that's our update on the state of AI in healthcare. Lots to think about, right? I feel like we've only just scratched the surface here.

Host 2: Yeah, there's so much more to explore.

Host 1: Definitely. So much happening in this field, it's kind of hard to keep up. But out of everything we've talked about, personalized medicine, drug discovery, safety monitoring, where do you see the biggest impact of AI in healthcare happening, say, over the next few years?

Host 2: Mm, that's a good question. I think one area that's really ripe for disruption is diagnostics.

Host 1: Diagnostics, like figuring out what's wrong with someone?

Host 2: Exactly. I think we're going to see a ton of AI-powered diagnostic tools coming out. Tools that can analyze medical images, you know, blood tests, all sorts of data and help doctors make faster and more accurate diagnoses.

Host 1: That makes sense, especially with like the shortage of doctors and specialists in so many parts of the world.

Host 2: Yeah, exactly. AI could really help to bridge that gap. You know, imagine a world where anyone anywhere can access expert-level diagnosis just by using their smartphone.

Host 1: Wow, that would be incredible. Okay, so AI for diagnostics, got it. What about drug development? Do you think AI will actually change how new drugs are discovered and brought to market?

Host 2: Oh, absolutely. I think it's already happening. We're seeing companies using AI to identify new drug targets, to design new molecules, to run virtual clinical trials. It's speeding up the whole process and potentially making it much cheaper, too.

Host 1: Interesting. So, do you think we'll reach a point where like most new drugs are discovered by AI rather than by humans?

Host 2: It's possible. I mean, AI is really good at analyzing huge datasets and finding patterns that humans might miss, so it could definitely play a major role in drug discovery.

Host 1: Makes you wonder what happens to all those like traditional pharmaceutical companies, right?

Host 2: I know, right? It's going to be interesting to see how they adapt. Some will probably embrace AI and integrate it into their existing workflows, but others might struggle to keep up. I think we'll also see a lot of new startups popping up, companies that are built from the ground up with AI at their core.

Host 1: So, it's like a whole new landscape for the pharmaceutical industry.

Host 2: Definitely. And it's not just pharma, it's healthcare in general. I think we're on the cusp of a major paradigm shift, like the report says.

Host 1: A paradigm shift, what does that even mean?

Host 2: Well, it means that we're moving away from this like reactive approach to healthcare where we wait for people to get sick and then we try to treat them.

Host 1: Right. Right, it's all about treating the symptoms.

Host 2: Exactly. But with AI, we can start to shift towards a more proactive and preventative approach.

Host 1: Preventative, like stopping people from getting sick in the first place?

Host 2: That's the goal. AI can help us identify people who are at high risk for certain diseases, you know, based on their genetics, their lifestyle, all sorts of factors, and then we can intervene early, maybe make some lifestyle changes, you know, start taking preventative medications, whatever it takes to keep them healthy.

Host 1: So, it's like personalized medicine, but for prevention.

Host 2: Exactly. And it's not just about preventing disease, it's about optimizing health, you know, using AI to help people live longer, healthier lives.

Host 1: That's pretty awesome. I like that. Okay, so we're talking about a future where healthcare is more personalized, more preventative, more proactive.

Host 2: And hopefully more equitable, too. You know, one of the things I'm most excited about is the potential for AI to make quality healthcare accessible to everyone, regardless of where they live or how much money they have.

Host 1: That's a great point. It's like we have this incredible opportunity to use AI to level the playing field and make sure everyone has a chance to live health- a healthy life.

Host 2: Absolutely. And I think that's something we should all be striving for.

Host 1: I agree. Well, this has been a fascinating deep dive, for sure. Thank you so much for sharing your expertise with us. It's definitely given me a lot to think about.

Host 2: It's been my pleasure. I'm glad we could have this conversation. These are important issues, and the more we talk about them, the better prepared we'll be to navigate the future of healthcare.

Host 1: Well said. Well, Deep Divers, I think that about wraps it up for our exploration of AI in pharmaceuticals. Big thanks to all of you for listening and for sending in your questions and comments. As always, stay curious, keep questioning, and keep diving deep.