Episode 60 · October 28, 2025 · 35m listen · 4,704 words · ~24 min read

Why AI Literacy Matters for the Future of Healthcare with José Acosta | Ep. 43 - Full Transcript | The Med Device Cyber Podcast

Read the complete, searchable transcript of Episode 60 of The Med Device Cyber Podcast - expert conversations on medical device cybersecurity, FDA premarket and postmarket guidance, SBOM management, threat modeling, and penetration testing.

Prefer the listening experience? Open the episode page for the synopsis, key takeaways, topics, and Apple / YouTube listen links.

Episode summary

In this episode of The Med Device Cyber Podcast, hosts Trevor Slattery and Christian Espinosa are joined by Dr. Jose Acosta, a retired naval trauma surgeon with a 40-year career in medicine, including serving as the Command Surgeon for the US Pacific Fleet. Dr. Acosta, an early adopter of new technologies, shares his expert perspective on the integration of Artificial Intelligence, particularly Large Language Models (LLMs), into the healthcare industry. The discussion is centered around the concept of "AI literacy," which Dr. Acosta argues is a critical competency for the next generation of healthcare professionals. He defines this literacy not merely as the ability to use AI prompts, but as a comprehensive understanding of the technology's inner workings, its inherent limitations, and the ethical and governance frameworks required for its safe deployment. Dr. Acosta emphasizes that while AI tools offer tremendous potential, especially in areas like diagnostics and administrative tasks, their application must be approached with caution. A key argument is the distinction between being "pretty good" and being precise. In many industries, an 85-95% accuracy rate is acceptable, but in medicine, where patient lives are at stake, the standard must be near-perfect precision. This leads to a discussion of the "productivity paradox," where AI, instead of saving time, can create more work for clinicians who must meticulously verify its output and manage increased patient loads expected by administrators. The conversation also explores specific applications like ambient AI scribes, which can reduce the burden of data entry but may also strip away the crucial "human touch" and rapport-building conversations in patient interactions if not governed properly. The podcast delves into the balance between leveraging AI's benefits and mitigating its risks. The hosts and Dr. Acosta discuss the security vulnerabilities of AI, such as the potential for training data to be poisoned or outputs to be tampered with, reinforcing the need for robust cybersecurity measures. Ultimately, the consensus is that AI will not replace clinicians but will act as a powerful assistive tool. The professional who masters the use of AI, understanding both its power and its pitfalls, will be the one who excels in the future. The episode serves as a nuanced exploration of AI's burgeoning role in medicine, highlighting the urgent need for education, responsible governance, and a clear-eyed view of both the opportunities and the challenges that lie ahead.

Key takeaways from this episode

AI literacy is crucial for healthcare professionals and extends beyond simple prompting to include a deep understanding of the technology's mechanics, limitations, and ethical implications.
Dr. Jose Acosta, with 40 years of medical experience, stresses that medicine requires a high degree of precision that current AI models, despite being 'pretty good,' may not yet consistently provide.
The 'productivity paradox' posits that AI tools might increase workloads, as clinicians spend time verifying AI-generated information and face pressure to see more patients due to perceived efficiency gains.
Ambient AI scribes are a promising application for reducing administrative tasks, but care must be taken to ensure they don't eliminate the essential human element and personal rapport in patient care.
AI is viewed as an assistive tool rather than a replacement for human clinicians; professionals who learn to use AI effectively will have a significant advantage over those who do not.
The security and safety of AI in healthcare are paramount, as models are vulnerable to risks like data poisoning and output manipulation, necessitating strong guardrails.
Governance is a key component of implementing AI responsibly, defining the rules and context for how these powerful tools are used in clinical settings.
The future of medical education must include training on how to use, interpret, and critically evaluate AI outputs to ensure patient safety and effective care.

Full episode transcript

Hello and welcome to another episode of the Med Device Cyber Podcast. We're joined here today by a very special guest, Jose Acosta, who I recently met on a trip to Boston where we had some really interesting conversations about new application for AI and healthcare and seeing what some of the wonderful things that can happen as a result of this new technology while still understanding there's a little bit of a safety balance we need to have in place there as well as of course, security. I'm also joined by our co-host Christian Espinosa. And I'll start with you, Jose. How are you doing today? Guest: Yeah, Trevor, I'm doing great. It's great to see you and it's great to meet Christian. Host: Yeah, and you're back in uh, New Mexico now, in Albuquerque, is that right? Guest: Correct. I I live in New Mexico, I work in St. Louis and parts in between. Host: Awesome. And then Christian, you're coming in from Phoenix today, right? I know it's kind of hard to pin down where you are in the world at any moment. Christian: I'm in Tempe uh, area, uh Phoenix area. Yep, Tempe, Arizona. I used to live in St. Louis area, Jose, back in the day. Used to teach at Washington University a little bit and lived in Illinois. Guest: So, pretty interesting, Christian. I, um, the first time I, uh, I went to St. Louis was back in 1979 and I was, uh, a transferred from a college in Puerto Rico to WashU. So I was there from 71, 79 through 81, um, and all the summers in between. Christian: Cool. So, you're um, an advocate for a, uh, AI literacy, Jose, it sounds like. So, when we talk about AI literacy, from what lens are you referring to? Uh, I know we talked a little bit before we started recording, but maybe you can elaborate a little bit. Guest: Sure, sure. Christian, let me give you a little bit of my background and that way, it'll kind of give context to, uh, to, um, where I'm coming from in terms of AI literacy. Graduated 40 years ago from medical school, trained as a surgeon, trained as a trauma surgeon, and um, served, uh, in the Navy for the next 30 years. And the vast majority of that was at the bedside, but at towards the end of my naval career, I moved from the bedside to the C-suite, um, finishing as the, uh, Command Surgeon for the US Pacific Fleet. And so, um, retired several years ago. Always have been an early adopter. And so that kind of gives you a frame of where I'm coming from. When I talk about AI literacy, it really focuses mainly on large language models, but you can extend it to all the other tools in AI. And what I'm really interested, as a physician, is that the students that we are training for the future understand the technology way beyond just a simple prompt, understand, um, some of the math that that's used for the predict, for the prediction, that individuals understand where, uh, these models are not accurate. Um, get an understanding of, um, privacy in terms of leakage of information that's put into one of these large language models. And so it's the entire spectrum from understanding the technology, some of the basic tools, understanding that all these models are somewhat different, um, understanding how to interface with them, with the correct prompts, understanding governance, which is very important, and ethics. So, you see it's a lot of things, not just I open my LLM and put in a prompt and um, there we go. Christian: Awesome. And from, curious your take, I think, uh, AI is fairly decent for diagnostics, like, uh, image enhancement, with an MRI or, uh, ultrasound. I think we're a little bit premature for therapeutic use of AI. Just curious what your thoughts are on that. Guest: So, so, so, Christian, I, very interesting. I, uh, you know, the more I use the tools, I realize that just like you said, they, they're pretty good. But the problem in medicine is that you have to be precise. And so, um, I was talking to Trevor when I was in Boston, I had just learned about, you know, alignment in these models. And I had, you know, looked at some of the, uh, the tools and, um, some of the, uh, information related to alignment. And one of the big, big, big LLMs had a graph that I found very interesting that that, um, had the alignment, a specific topic, and what percent of the times that the chats meet that requirement. And it was pretty good. It was 85, 90, 95%. But the problem is in medicine, you have to be as close to 100% as possible.

1 / 6