Oracle Board member breaks down the current state of AI

### TL;DR Oracle board member Kevin Hutchinson shares his career journey from IBM to healthcare technology, and breaks down the differences between generative AI, agentic AI, and general AI. He emphasizes that AI isn't new but is showing huge potential in healthcare, especially in analyzing radiology reports for missed findings. --- ## Guest Background: From IBM to Healthcare Tech Pioneer Kevin Hutchinson was born and raised in Oklahoma and graduated from the University of Oklahoma. He spent ten years at IBM before joining Oracle. But he saw massive room for automation in healthcare — at the time, all processes and medical records were on paper. So he joined a hospital association to oversee IT investments, making early investments in Vizient and an electronic medical records (EMR) company. In the mid-1990s, only about 5,000 doctors nationwide were using EMRs. He helped grow that company, took it public, acquired Medscape.com, and eventually sold the EMR company (MedicalLogic) to GE (becoming Centricity) and Medscape to WebMD (still operating today). After that, he realized doctors hated the record-keeping software because charting was too time-consuming. So they created a company that connected EMR software with pharmacies. Eventually they connected every pharmacy in the U.S. with every EMR system — every electronic prescription flowed through the network they built, called Surescripts. Starting with the first e-prescription in Rhode Island, now billions of prescriptions flow through that network every year. He also briefly served as an advisor to President George W. Bush, helping build the framework for a health information network. Later, the Obama administration asked him to join a standards committee to certify all EMRs for the federal government. Today, he is an advisor to New York private equity firm Arsenal Capital, interim CEO of healthcare revenue cycle management company Notion Health, and chairman of the board of Eon Health in Denver — both companies heavily using AI tools and technologies. --- ## Common Misconceptions About AI and Its Real Evolution Kevin points out that there's a misconception in the industry that AI is new — it's actually been around for a long time, but only used in very specialized areas. Large language models have also existed for a while. It started with machine learning: writing algorithms that learn from outcomes, adjusting based on the task. Then it evolved into large language models, where the same algorithms began learning from much larger datasets. Generative AI became the first area consumers encountered, like ChatGPT. People love logging in and using it for search. In fact, while some say "don't just use AI for search," it really is a great search engine. Google is racing to catch up, but its search traffic is already taking a hit because Google search gives you websites and links — you have to sift through them yourself — while AI gives you a direct answer with sources you can click to verify. Generative AI is essentially about content; every search engine is looking for content. --- ## Real-World Applications of AI in Healthcare ### As an Assistant to Doctors People worry that AI will become the doctor, directly answering questions and making diagnoses. In reality, it's currently a tool to assist doctors. Generative AI excels at anything that has documented content (medical books, encyclopedias, etc.) — it can learn from that material. AI takes medical licensing exams and does extremely well — the same exam humans take. In practice, AI can do something humans find nearly impossible: if it has access to all medical records and all the knowledge from medical books, theories, protocols, and specialized treatments, it can analyze every record to find which treatments work best for which patients. For a given person's diagnosis, it can look at five million records in three seconds, evaluate different treatment options, and say, "What's the best plan for *me* as an individual?" Because treatments vary from person to person. Finding a plan that matches that person's DNA or characteristics — a human doctor could never reach that level. It's simply a tool for the doctor that says, "We recommend this, for these reasons." The doctor can look at it and say, "That's a good recommendation, I'll take it." Of course, it can also make mistakes, and the doctor can adjust parameters to have it reanalyze. ### Missed Findings in Radiology Reports — Eon Health's AI Kevin gives the example of a dentist: the dentist takes an X-ray, sees something below the root of a tooth, pulls out a computer, enters the info, runs AI, and gets results in seconds. The dentist says, "I could look at X-rays all day and not do what this does in ten seconds." Kevin's company Eon Health (where he is chairman) is based in Denver. They use AI to analyze radiology reports — not directly the images (though they can look at X-rays too). A huge amount of information gets missed in radiology reports. For example: you go see a cardiologist, who orders an X-ray of your heart. The report says your heart is fine, but it also notes a nodule in your lung. The cardiologist says, "I'm a heart doctor, I don't deal with that." Thousands of patients are missed like this every year. Some patients first show up in a report at stage one (very curable), and come back a year later at stage four. If you go back and check the records, it was actually discovered one or two years earlier, but was never diagnosed or entered into treatment. Eon Health captures that information, contacts the patient, and the hospital pays for it. They route the patient to the right place — regardless of where the radiology report came from — putting the patient back into the correct treatment path. It's AI-based, analyzing the content of the report and capturing key phrases. Kevin's own cousin in Connecticut had a husband who was missed like that — it was treatable early on, but he ended up losing a lung and part of his heart lining. --- ## Anthropomorphism of AI and Future Trends Kevin mentions that a relative of his directly calls ChatGPT her "boyfriend," because ChatGPT already knows everything about her, likes her, and gives her good advice. Developers intentionally train it to speak more kindly to make users more addicted. Kevin warns: don't trust it completely, find another source to verify, just like you would with a real person. Generative AI is just the first wave. Next comes "agentic AI" — actually involving task execution. Then "general AI" — that's another level, only in labs now, but it will come. --- **Source**: Oracle Board member breaks down the current state of AI — Ghostttoast69 (https://youtu.be/IxiJTrZfpic?si=zbws4lxjCd33Cs5V)