We have about 1,000 people who receive our blogs, and a few have asked why I am both so optimistic about Generative Artificial Intelligence (AI) and, at the same time, critical of its direct role in medical care—except in a few areas, such as Radiology, Pathology, Genetics, and others we frequently mention.
In our last blog of 2024 (linked below), I shared a perspective based on 30 years of actual healthcare experience, and I want to quote it directly for clarity: “AI will not make it deep in healthcare until an AI company does three things: a) actually save time for the provider while being more accurate; b) be affordable relative to the time saved; and, the really big one, c) indemnify the provider for medical malpractice, peer review, licensing, and credentialing. I don’t see ‘c’ happening in my lifetime.”
The reason this discussion remains so relevant is that we continue to see reports claiming “AI outperforms doctors” in highly controlled (some may say engineered) diagnostic settings. The reality, however, is that in the real world—where time, money, and resources are limited—the outcomes are far more complex. Nowadays, there’s no quicker way to get a research paper published than by mentioning AI.
A recent article from Medical Xpress and Florida Health News by Darious Tahir caught our attention. The headline reads: “Healthcare AI, intended to save money, turns out to require a lot of expensive humans.” It highlights an important point: “Despite the hype over artificial intelligence in medicine, the systems require consistent monitoring and staffing to put in place and maintain.”
AI in Healthcare: Promise and Practicality
This article emphasizes that while AI’s integration into healthcare diagnostics offers promising advancements, it also presents substantial challenges that deserve careful consideration. One key issue is the potential degradation of AI algorithms over time. For instance, during the COVID-19 pandemic, some predictive models experienced declines in accuracy, which could lead to missed opportunities for important patient-provider discussions—such as end-of-life care planning—ultimately compromising patient outcomes. As a result, regular monitoring and recalibration are essential for maintaining the reliability and effectiveness of these tools.
Moreover, deploying AI in healthcare diagnostics often requires considerable human oversight and additional resources, which can erode the anticipated cost savings. To properly evaluate and maintain AI systems, healthcare organizations must invest in performance monitoring, data management, and infrastructure integration. Without adequate human capital and support, errors in algorithmic functioning may go unnoticed, risking diagnostic inaccuracies. Healthcare providers must weigh the benefits of AI against these operational demands, ensuring that both technology and human resources are properly aligned to maintain high standards of patient care.
The Case for Genetics: Potential, but Not Without Risks
AI’s application in genetics shows particular promise, though not without risks. A recent article in The Australian titled “AI to Supercharge Genomic Medicine, but Risks Loom” points to the significant potential AI holds for diagnosing rare diseases, especially through advancements like Google’s AlphaFold, which has revolutionized our understanding of protein structures. This progress can help identify disease-causing genetic variations more efficiently. However, integrating these tools into clinical practice requires expert interpretation to ensure the results are both accurate and clinically relevant.
The risks are further complicated by bioethical issues, including concerns about genetic data storage, usage, and the potential for biohacking. These complexities demand stringent regulations to ensure patient privacy and data security. In addition, regulatory bodies like the Australian Therapeutic Goods Administration (TGA) have expressed concerns about AI applications, such as Helfie.ai, which claims to diagnose conditions like skin cancer using smartphone images. The lack of clinical trial evidence and appropriate medical device classification highlights the need for rigorous validation before AI tools can be deployed safely in clinical settings.

Additionally, successful AI adoption in healthcare hinges on having a skilled digital health workforce. In Australia, initiatives involving universities are training professionals to effectively use digital technologies in clinical settings. To fully realize the potential benefits of AI, addressing challenges like data management, security, and ethical concerns is crucial. Without well-trained personnel, AI’s promise in diagnostics and treatment may not come to fruition.
The FDA’s Perspective and Future of AI Regulation
We’ve been working on a blog about the outgoing U.S. Food and Drug Administration (FDA) Administrator and his optimistic view of AI. He mentions that the biggest challenge with AI is its constantly evolving nature—a concern shared by the FDA due to the “black box” issue we’ve discussed before. We’ve delayed publishing that blog because we’re unsure how the FDA will approach AI under the new leadership beginning January 20, 2025, with President Trump. While we suspect little will change in the short term, we’ll revisit this topic in a few weeks.
AI and Medical Treatment: Not Quite Ready for Prime Time
We’ll also explore in a future blog why we believe AI is not yet ready for prime time in medical treatment. Despite this, AI received record investments in 2024, dominating funding across various sectors.
As we’ve mentioned, a significant portion of investments are going toward infrastructure, including a West Palm Beach-based company that recently raised over $300 million. Another major focus is the development of Large Language Models (LLMs), but the most valuable long-term applications will involve leveraging that infrastructure and those LLMs to create products for both businesses and consumers.
AI for the Consumer: Practical and Powerful
We are increasingly confident that the best current application of AI is empowering consumers to be more informed about their health and better equipped to engage with healthcare providers and insurance companies. This level of practical, consumer-facing AI is achievable today and could play a key role in transforming patient experiences and interactions within the healthcare system.
About HealthScoreAI ™
Healthcare is at a tipping point, and HealthScoreAI is positioning to revolutionize the industry by giving consumers control over their health data and unlocking its immense value. U.S. healthcare annual spending has exceeded $5 trillion with little improvement in outcomes. Despite advances, technology has failed to reduce costs or improve care. Meanwhile, 3,000 exabytes of consumer health data remain trapped in fragmented USA systems, leaving consumers and doctors without a complete picture of care.
HealthScoreAI seeks to provide a unique solution, acting as a data surrogate for consumers and offering an unbiased holistic view of their health. By monetizing de-identified data, HealthScoreAI seeks to share revenue with consumers, potentially creating a new $100 billion market opportunity. With near-universal EHR adoption in the USA, and advances in technology, now is the perfect time to capitalize on the data available, practical use of AI and the empowering of consumers, in particular the 13,000 tech savvy baby boomers turning 65 every single day and entering the Medicare system for the first time. Our team, with deep healthcare and tech expertise, holds U.S. patents and a proven track record of scaling companies and leading them to IPO.
Noel J. Guillama-Alvarez
https://www.linkedin.com/in/nguillama/
+1-561-904-9477, Ext 355
https://medicalxpress.com/news/2025-01-health-ai-money-require-lot.html
https://www.theaustralian.com.au/