🏥Google AI in Healthcare: Revolutionizing Patient Care🧠
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Welcome back to AI Unraveled. This is another deep dive created and produced by Etienne Newman, you know, senior engineer and passionate soccer dad up in Canada. Great to be here. Yeah. And hey, if you're enjoying these explorations into AI, seriously, take a second to like and subscribe. It makes a real difference in helping others find us. It really does. So today we're tackling something huge, really significant. Google AI's role in health care.

It's evolving so fast. Incredibly fast. It's almost hard to keep up sometimes. Totally. We've pulled together various articles, research papers, basically looking at Google's impact. And our mission, as always, is to distill it down.

What are the key insights? How is Google AI actually reshaping health care right now? And what might be coming down the pipeline? Exactly. We want to give you a clear, engaging picture cut through the jargon. Sounds good. Where should we start? Let's kick off with personalized medicine. It's a fascinating concept. It really is. This idea of, you know, N of one medicine, tailoring treatments just for you.

It's not science fiction anymore. And AI, especially Google's work, is a massive driver. Right. Moving away from that one size fits all approach. Precisely. It's about designing treatments specifically for your biology, your circumstances. And Google's tools like Vertex AI and MedGemma, they're central to this. Absolutely. They're designed to crunch these enormous complex data sets. We're talking genomics, electronic medical records, statistics.

scans, imaging, even lifestyle factors. Wow. All of it together. All together. The goal is simple, really. Make treatments work better and cause fewer problems, fewer side effects. Okay. So Vertex AI, that's like the platform they use. Yeah.

Think of Vertex AI as the workbench, the platform that lets researchers and hospitals build and roll out these custom AI models for personalized care. Got it. And MedGemma? MedGemma is particularly interesting. It's specialized in understanding the really messy parts of patient data, like doctors' handwritten notes, which are often unstructured, or interpreting different kinds of medical images. So it reads between the lines almost. In a way, yes. It helps build that complete patient picture from all the different pieces of information.

You need seamless data ecosystems for this and these multimodal AI models that can handle text, images, numbers. Makes sense. So how is this actually changing things on the ground, like for cancer treatment? Oh, cancer care is a prime example. Google Cloud's genomics API, often working hand-in-hand with Vertex AI, is really powerful for analyzing tumor genomics. Meaning looking at the specific genetic code of a tumor. Exactly.

By identifying the specific mutations driving that patient's cancer, doctors can select targeted therapies. These drugs attack those specific weaknesses. Well, it sounds much better than just blasting everything with chemo. Often, yes. It can be far more effective and usually comes with fewer harsh side effects. And analyzing this stuff takes massive computing power. Right. These genomic data sets must be enormous. Like the TCGA, the Cancer Genome Atlas.

- Huge data sets. And that's where Google Cloud's infrastructure comes in. It provides the horsepower needed to process and analyze it all, comparing a patient's profile against vast libraries of information. - And they're working with others on this too, right? Not just in-house. - Definitely.

Collaboration is key. For instance, PwC and Google Cloud are partnering up. They're building oncology data platforms using Vertex AI and the Healthcare Data Engine 2.0. Okay, what's the goal there? To integrate all that diverse data records, genomics, imaging to really enable personalized cancer care decisions.

- Interesting. You know, for listeners interested in the nuts and bolts of tech like Vertex AI and cloud infrastructure for this kind of work, Etienne Newman's book, "The Google Cloud Generative AI Leader Certification Guide,"

is a fantastic resource. It's on djmcac.com, Google Play, and Shopify links are in the show notes. Good point. It covers that foundational tech well. And there are other collaborations too, like with ASCO, the American Society of Clinical Oncology. Oh yeah, what are they doing? They're building an AI-powered ASCO Guidelines Assistant. Uses Vertex AI and Gemini. The idea is to give oncologists super fast access to the latest treatment guidelines right when they need them during patient care.

helping them tailor the plan on the spot. Exactly. Personalizing care based on the very latest evidence. And we shouldn't forget, verily, Alphabet's life sciences arm. They're also doing significant work in personalized oncology research. It really feels like a paradigm shift in cancer treatment. More precision, less guesswork. That's the direction. Absolutely. Okay. Well,

Let's switch gears slightly. What about diabetes? So many people live with that. Yeah. Another huge area where AI can really help manage a chronic condition day to day. For diabetes, you've got AI predictive models. Built with tools like TensorFlow running on Vertex AI. Often, yes. They analyze this constant stream of data from continuous glucose monitors, CGMs, insulin pumps,

maybe data logged about food or exercise. All that real-time info. Right. And the main goal is to predict before glucose levels go too high or too low. Like an early warning system built into your monitor. Kind of, yeah. So you or your pump can intervene proactively, smoothing out those potentially dangerous swings, and

Google's even developed a specific AI model for this, the Personal Health LLM. LLM, like the large language model. Yeah, a fine-tuned version of Gemini, specifically trained to interpret health sensor data and give personalized advice or alerts. That's fascinating. And Verily's involved here, too, with their LightPath platform. It uses AI and CGM data for personalized diabetes and obesity management support. And universities are researching this, too. Oh, yes. Yes.

Stanford, for example, is using AI with CGM data trying to identify different subtypes of type 2 diabetes. The idea is that different subtypes might respond better to different treatments. More personalization again. Exactly. And other projects like BGR are looking at AI with CGM specifically for those predictive alerts we mentioned. It's all about making management more dynamic and tailored. It's incredible. But with all this personalized data flying around,

Ethics must be a huge consideration. Oh, absolutely critical. Data privacy and security are paramount. You've got regulations like HIPAA in the US, GDPR in Europe. And Google Cloud tools have features for that, right? Like de-identification. Yes. The Google Cloud Healthcare API includes features to help organizations comply, like removing personally identifiable information. But it goes beyond just technical safeguards. Oh, so.

Well, there's the issue of equitable access. We need to make sure these powerful AI-driven treatments don't just benefit the wealthy, potentially widening health disparities. That's a really important point. And algorithmic bias is a major concern. If the AI is trained on data that reflects historical biases in healthcare, the AI might perpetuate or even amplify those biases. Leading to worse outcomes for certain groups? Potentially, yes. It requires very careful attention to training data and model evaluation.

Then there's informed consent. How do we ensure patients truly understand how their data is being used by these complex AI systems? Right. It's not just ticking a box anymore. And finally, it changes the doctor-patient relationship. The clinician becomes more of an interpreter of AI insights, guiding the patient through this data-rich landscape. It requires new skills. Definitely a lot to navigate there. Okay, let's move to another really exciting area. Early disease detection.

You mentioned AI acting like a super-powered eye for medical images. That's a great way to put it. Tools like Google's Cloud Vision API or Med Gemini use deep learning to scrutinize medical images. X-rays, CTs, mammograms, retinal photos, pathology slides. Looking for things humans might miss. Exactly. Detecting those incredibly subtle patterns, textures, or anomalies that could indicate the very earliest stages of a disease long before symptoms might appear.

And Medgema fits in here too. Yes. Medgema is pre-trained specifically for medical image tasks like classification or interpretation in fields like radiology or pathology. It's about spotting those tiny clues early on. Which can make a world of difference for treatment success. Where is this showing the most promise right now?

Well, oncology, again, is a big one. Google AI models analyzing mammograms, for instance. There was a major study in Nature showing their accuracy was comparable to or sometimes even better than experienced radiologists. Wow. And crucially, potentially reducing both false positives, unnecessary anxiety and biopsies, and false negatives, meaning missed cancers.

That's huge. Are these being used in clinics? They're working on it. Google collaborates closely with clinical partners to figure out the best way to integrate these tools into the actual radiology workflow. And there's LYNA lymph node assistant. What does that do? It helps pathologists detect metastatic breast cancer cells and lymph node biopsies more efficiently and accurately. It's like an AI assistant for the microscope. Incredible potential impact. What about other major killers like

Like heart disease? Cardiovascular disease is another fascinating frontier. AI algorithms are now analyzing images of the retina, the back of the eye. To predict heart attack risk, how does that work? The AI identifies subtle changes in the blood vessels in the retina, which reflect overall cardiovascular health. It can pick up on biomarkers linked to things like blood pressure, cholesterol levels. Just from looking at the eye? Just from a retinal scan.

A study in Nature Biomedical Engineering showed AI could predict risk factors for heart attack or stroke surprisingly well. It could even tell smokers from non-smokers with about 71% accuracy just from their retinas.

and predict future cardiovascular events with around 70% accuracy. That's astounding. So potentially a non-invasive, easy way to screen large populations. That's the potential, yes. Scalable, accessible straining. It's the kind of groundbreaking application that, again, understanding the underlying AI is key for.

Etienne's book, The Google Cloud Generative AI Leader Certification Guide, covers the AI principles that enable this kind of analysis. Right. Understanding how the AI actually sees those patterns. And what about brain diseases like Alzheimer's? Early detection is so critical there. It absolutely is. And AI is making inroads there, too, though it's very complex.

Models are being developed to pick up on those very early subtle signs that might predate significant memory loss. Using what kind of data? Often multimodal data. Combining brain imaging like MRIs or PET scans with cognitive test results, maybe biomarker data from spinal fluid or blood tests.

The Foundation for Precision Medicine, for example, uses Google Cloud's BigQuery ML to analyze patterns in electronic health records that might flag early Alzheimer's risk. Trying to piece together the earliest clues from different sources. Exactly. Deep learning is particularly suited for finding complex patterns in that kind of mixed data. So all this early detection capability. Mm-hmm.

It really feels like it fundamentally shift health care from being reactive to proactive. True. It's a massive shift. Instead of waiting for symptoms and then diagnosing, we move towards identifying risk or early disease before it becomes a major problem. That retinal scan for heart risk is a perfect example. And that has implications beyond just the patient, doesn't it? For the whole system. Oh, absolutely. Think about screening programs, insurance models.

If you can reliably predict risk earlier, it might incentivize preventative actions, change how insurance is structured. It also empowers individuals with more knowledge about their own health. And the downstream effects must be significant to better outcomes, lower costs. That's the hope and increasingly the evidence. Catching diseases early usually means treatment can be less aggressive, less invasive and more effective.

That generally leads to better survival rates, better quality of life. Makes sense. Early-stage cancer is often much more treatable than late-stage. Precisely. And from a system perspective, treating early-stage disease is often far less costly than managing advanced chronic conditions or dealing with emergencies. Fewer hospital stays, less need for expensive late-stage drugs, optimized resource use. The potential cost savings are substantial. But is there a risk of, like...

Overdiagnosis, finding tiny things that might never have caused a problem. That is a valid concern. Finding very early, perhaps indolent conditions could lead to unnecessary anxiety and treatment.

So these AI tools need careful calibration and validation. And crucially, human oversight remains essential. The AI is a tool to assist clinicians, not replace their judgment. Good point. OK, this is all fascinating. Let's pivot to another massive area, drug discovery. I hear AI is set to basically rewrite the rulebook there. You hear correctly. The traditional way of finding new drugs is online.

Well, it's incredibly long, incredibly expensive, and most candidates fail somewhere along the way. Yeah. It's a tough business. Yeah, it takes like a decade and billions of dollars, right? It's easily. But AI, particularly Google's capabilities, is acting as a major catalyst for change. It starts with analyzing just immense amounts of biological and chemical data. Think about Google Cloud's readily available BIDO datasets giving researchers access to this stuff. So AI can sift through information at a scale humans just can't manage.

Exactly. And frameworks like TensorFlow allow scientists to build sophisticated AI models. These models can analyze chemical structures, predict how molecules might interact with biological targets, understand complex pathways, even analyze clinical trial data more effectively. Speeding everything up. And potentially making it smarter. Google Cloud even has specialized tool suites for this, like the Target and Lead Identification Suite and the Multimix Suite, designed specifically to accelerate these early stages.

You know, understanding the cloud architecture that supports these massive computations is really key, which, again, Etienne's book on Google Cloud Generative AI Leader Certification covers well. Right. The infrastructure enabling the science. And AlphaFold.

That's been huge news. How does that fit into drug discovery? AlphaFold is a genuine game changer. Its ability to predict the 3D structure of proteins with remarkable accuracy is fundamental. Proteins are the targets for most drugs. So knowing the shape helps you design a drug that fits. Precisely.

Integrating alpha-fold predictions with platforms like Vertex AI lets researchers rapidly identify potential drug targets, understand how mutations might affect a protein, and even design potential drug molecules computationally that's in silico drug design. Doing experiments on the computer instead of in the lab. A lot more of it, yes. It doesn't eliminate lab work, but it can drastically reduce the amount needed, saving time and resources by focusing experiments on the most promising candidates identified by AI.

It sounds like a much more efficient process. And I know Google has some dedicated teams or initiatives focused just on this, right? These AI powerhouses. They do. Alphabet has invested heavily here. You have AlphaFold3 and Isomorphic Labs. AlphaFold3 takes it a step further than predicting single protein structures. How so? It models the interactions between different molecules, proteins with DNA, RNA, small molecules like potential drugs.

Understanding these interactions is crucial for designing effective medicines. And Isomorphic Labs. Isomorphic Labs is the alphabet company tasked with taking these incredible biological insights from AlphaFold and actually translating them into new therapies. They're aiming to have their first AI-designed drug candidate in clinical trials by the end of 2025. Wow, that's incredibly fast for drug development. That's right.

What else? Then there's the AI co-scientist concept, often powered by models like Gemini. This isn't just a tool. It's more like a collaborator for researchers. Exactly.

The AI can help generate hypotheses, suggest experiments, analyze complex data. It's shown promise in areas like finding new mechanisms for antibiotic resistance or identifying potential drugs for liver fibrosis. It's a real synergy between AI's power and human expertise. Like having a brilliant research partner. Kind of. And then there's TXH Gemma. These are open models based on Google's Gemma architecture, specifically tuned for drug discovery tasks.

They can understand scientific literature, chemical structures, and help predict things like safety and efficacy much earlier in the process. So more computational experiments, potentially lower costs. Does this mean drugs could become cheaper or more accessible? That's certainly one of the major hopes.

By making the R&D process more efficient and less reliant on costly, time-consuming wet lab work, AI could lower the barrier to entry. Making it feasible to develop drugs for rarer diseases, maybe. Exactly. Diseases that affect smaller patient populations often get neglected because the traditional R&D costs are just too high relative to the potential market. AI could change that economic equation. And maybe even tackle diseases we previously thought were untargetable.

That's another exciting possibility. AI's ability to analyze complex biology in new ways might reveal targets or pathways that were previously overlooked or considered too difficult to drug. It could open doors for conditions with significant unmet needs. Like that story about the Googler using Gemini to understand his son's rare disease, Alexander disease. Yes, that's a powerful example. Using AI to synthesize information and connect dots led to new research hypotheses for a very rare condition.

It shows the potential for AI to accelerate understanding even in challenging areas. But just like with the other areas, the ethical side needs careful thought here too. Absolutely. Safety and efficacy are non-negotiable. AI-assisted drug discovery doesn't bypass the need for rigorous clinical trials to prove a drug works and is safe. Of course. Then there's equitable access again. How do we ensure these AI-developed medicines reach people globally, not just in wealthy countries?

Data privacy for the biological data used is critical. And algorithmic bias in training data could lead to drugs that work less well in certain populations. So similar challenges to personalized medicine. Very similar. It underscores the need for responsible development, transparency and keeping humans firmly in the loop, overseeing the process. Understanding responsible AI principles is crucial. And that's something Etienne's book touches on as well within the Google Cloud context. Right. It's a recurring theme. Incredible power.

but needs careful handling. OK, we've covered so much already personalized medicine, early detection, drug discovery. It's mind blowing. Let's look ahead now. What's next? What does the future of Google AI in health care look like? Well, if the present is this exciting, the future looks even more transformative. We've seen the groundwork laid in these key areas.

The next generation of applications will likely build on this foundation in some amazing ways. Like what? What are you seeing on the horizon? Maybe robots doing surgery? AI-powered robotic surgery is definitely a big one. We're moving beyond just robotic arms controlled by humans towards systems with more AI assistance, maybe even semi-autonomous capabilities for certain tasks. Semi-autonomous, like the robot does part of the surgery itself.

Potentially for specific, well-defined steps. The goals are things like even greater precision, enabling more minimally invasive approaches, reducing complications, speeding up recovery. Verily was involved in that area, weren't they, with Verb Surgical? Yes, their collaboration with Johnson & Johnson on Verb Surgical, which is now fully with J&J, explored digital surgery platforms.

And Google Brain has done research on training AI by watching videos of surgeries, teaching it specific tasks. Oh, OK. What else beyond the operating room? Virtual health care assistants could become much more sophisticated.

Think beyond simple chatbots. We're talking AI systems capable of complex diagnostic reasoning and importantly, empathetic conversation. Empathetic AI. That's the goal. Google's project AME is exploring exactly that AI for better medical dialogue. And tools like Google Cloud's Vertex AI Conversations let organizations build their own sophisticated virtual agents. What could these assistants do for patients? Provide 24/7 support.

help manage chronic conditions, remind about medications, maybe even perform initial triage for symptoms. They could become like orchestrators guiding patients through complex care journeys. So much more than just a symptom checker online, a real guide. Potentially, yes. And then there's continuous health monitoring. Wearables like the Google Pixel Watch or Fitbit generate so much data. Right. Tracking steps, heart rate, sleep. Exactly. AI is key to unlocking the meaning in all that data.

Google's Personal Health LLM, PHLLM, we mentioned earlier, is designed for this interpreting sensor data, providing personalized insights. Like the loss of pulse detection on the Pixel Watch 3 that got FDA clearance? That's a perfect example. Using AI to detect a potentially serious event in real time from sensor data. It blurs the lines between consumer wellness gadgets and actual medical monitoring. Which probably means new regulations are needed. Most likely, yes.

As these devices become more medically capable, regulatory pathways will need to evolve to ensure they're safe and effective for those uses. Understanding that AI driving these health features is becoming really important for consumers and professionals alike again. Resources like Etienne's book can help bridge that gap for the technically curious. It truly feels like we're entering a new era. How healthcare is delivered, managed, experienced. It's all changing. It really is. The pace is incredible. And staying informed is key. For listeners wanting to keep up,

Besides continuing with AI Unraveled, of course. Google AI's own health website, AI.GoogleHealth, is a good starting point for their initiatives. And for the real deep science, the Google Health Research Publications pages have links to their papers. We'll put those links in the show notes too. Great. Because this field demands continuous learning, not just for the AI models, but for all of us involved in healthcare. Absolutely. Yeah. Which leads to a final thought, maybe.

As AI gets woven deeper into our health, these really personal aspects of our lives, what new responsibilities pop up for us as individuals, for society? How do we make sure this incredibly powerful tech truly serves humanity in the best, most equitable way? That's the crucial question to keep asking. Definitely something to ponder.

Well, that brings us to the end of this deep dive. Huge thanks for joining us. And to our listeners, please remember to like and subscribe to AI Unraveled if you haven't already. Thanks for having me. And one last reminder, check out Etienne Newman's fantastic AI certification prep book, Google Cloud Generative AI Loader Certification. You can grab it at djamgatech.com, Google Play, or Shopify. All those links right there in the show notes. Thanks for tuning in.