Large Language Models (LLMs) have incredible potential, but it’s clear we’re not fully prepared yet for their integration into sensitive, high-stakes domains like healthcare.
As we push the boundaries of what AI can do in medicine, we must also pause to ask:
Are we building Medical LLMs we can trust?
What do you think the biggest barrier is to safe LLM deployment in healthcare?
Let’s start a discussion.
As a recent BS Computer Science graduate with a software development background, I’ve been thinking about the risks associated with deploying powerful AI systems in sensitive areas like healthcare. While I’m not an expert in the medical domain, I believe one of the most critical barriers is the lack of robust security measures around these models.
With the increasing sophistication of AI, it's not difficult to imagine scenarios where malicious agents, whether human or AI-driven, could exploit vulnerabilities in large language models. For instance, dataset poisoning could introduce bias or incorrect medical reasoning. Prompt injection or model hijacking could manipulate the output of a model in unpredictable and potentially harmful ways. And in high-stakes environments like medicine, even a small error or breach can have life-threatening consequences.
Another concern is privacy leakage, especially given that LLMs can unintentionally memorize and expose sensitive patient data. This raises serious questions about compliance with regulations.
These risks point to the need for proactive defenses - such as red teaming, secure data pipelines, differential privacy, and robust monitoring systems - before LLMs can be considered trustworthy in clinical settings.
So in my view, trust in medical LLMs cannot be achieved without first addressing their security at a foundational level. Without this, even the most accurate or explainable models may pose unacceptable risks.
[I am not sure if I am qualified enough to *participate in such discussions. To sound formal and professional, I had to rephrase my points with the help of ChatGPT.]
I believe that large language models (LLMs), if correctly validated using real-world clinical scenarios, could serve as a foundational tool for developing frameworks to address healthcare challenges proactively. Their potential advantages could coincide with an important turning point in the future of medical practice.
Shahzad - I agree, we need to address the security issues of Healthcare LLMs at a foundational level.
Khadersab Adamsab Yes, LLMs have the potential to solve healthcare challenges proactively. For that we need to more robustly validate real-world use cases.
Ravindra Nath Tripathi
It is crucial that medical LLMs are validated by humans, have transparent documentation, and adhere to strict privacy standards- Badges
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