AI in diagnosis: Real-world applications

- As a heart surgeon, in my years of practice, I've made thousands of diagnostic decisions, but after that young NHL hockey player's 83-minute cardiac arrest, I had one burning question. Could artificial intelligence help us spot life-threatening conditions such as a cardiac arrest before they even happen? Have you ever wished you could predict something before it happened? Well, in healthcare, it can happen through the power of AI-driven predictive analytics, enabling us to foresee complications, optimize treatments, and save lives with unprecedented precision. Let me share three real-world AI applications that are transforming healthcare right now. First one is early detection of eye disease. Think of the back of your eye like a complex roadmap of blood vessels. In diabetic patients, these vessels can become damaged, leading to vision loss if they aren't caught early. Traditionally, doctors must carefully examine each image, looking for tiny changes. Some as small as a pinpoint. Think of what could go wrong. Think of how many things can be overlooked. What amazes me is how AI analyzes these images in seconds, breaking them down pixel by pixel. Using advanced imaging analysis, it can detect microscopic changes in blood vessels that might otherwise go unnoticed with the naked eye. With this technology, eye clinics can now screen more patients daily, detecting damage months or even years before symptoms appear, including even predicting your blood sugar. So now it's your turn. Before we move to the next application, head to your workbook and think about your own screening processes such as blood pressure or your heart rhythm. Where do you need this kind of detailed, rapid detection? Learning point number 2, enhanced medical imaging. Every day, hospitals produce thousands of medical images, whether it's an x-ray, a CT scan, an MRI, or an echocardiogram. Each contains hundreds of small details that could indicate serious conditions. We may miss this. About 12% of the data for each patient is what we really, really use. Now, instead of having different specialists review the same scan, AI can check for multiple conditions at once in just seconds. While one part of the system looks for signs of pneumonia in chest x-rays, another at the same time may even check for fractures, say, in a trauma patient, and the third could identify potential tumors, which could just incidentally be found. Most importantly, it flags urgent cases like a brain bleed, ensuring critical patients get immediate attention. Right now, take another pause and let's think about a few things. Here's what I want you to do. List three diagnostic tasks in your practice that, one, require checking multiple details, two, need quick decisions, and three, could benefit from automated screening. Your learning point number 3, personalized treatment analysis. Finally, this is what really excites me about AI in healthcare. It can analyze a patient's complete medical story, from their genetic code to their latest test results, and help predict which treatments would work best for them personally if you like personalized medicine, except using AI as a catalyst. As we know, every patient's body responds differently, so this really needs to be tailored. Within seconds, the AI analyzes vast databases of clinical outcomes, identifying successful treatment patterns, and also ones that failed from similar patient cases. Through my experience with using some of these tools in our AI lab, I've discovered these essential requirements. They really must work within our existing daily routines to make it applied. They need to explain their findings clearly. Any kind of ambiguity will be a big problem and cause medical errors for things that we would not want. They should learn and improve from each case. So learning is reinforced. We're at our last exercise now for this video, and your next step is to consider a few things. Of the three tools we discussed here, which of these could help your patients the most? How would they fit into your daily work? Who on your team needs to learn about them? Just remember, good AI tools aren't about replacing our expertise. They're about giving us better information to make better decisions and safer decisions and more efficient decisions for you.