🔆 Gen AI in Health Tech: A Neuroscientist's Perspective

🗞️ Issue 66 // ⏱️ Read Time: 5 min

Hello 👋

As health tech founders increasingly turn to GenAI, how do you separate hype from reality? This week, we're sharing key insights from Dr. Anna McLaughlin, a neuroscientist and founder, on the promises and pitfalls of AI in healthcare. We'll highlight direct quotes from her recent blog post and Lumiera’s perspective on these critical issues.

Meet the Founder of Sci-Translate

Lumiera’s Top 5 Takeaways

Here are our favorite quotes and expert insights after reading “Science in the Age of AI: Can We Trust Gen AI in Health Tech?”.

Takeaway 1: AI Doesn't "Understand" Science

"AI doesn’t understand science — it mimics it. LLMs generate responses by predicting the most likely next word based on probability, not comprehension."

🔆 Lumiera Insight: Even with the recent advancements in AI reasoning capabilities that go beyond simple mimicry (e.g., chain-of-thought), the core point still stands; these models don't truly "understand" in the way humans do. Human expertise remains essential for interpreting conflicting evidence, spotting methodological flaws, and recognizing when new research genuinely challenges established thinking. This is why AI works best as a powerful tool to accelerate scientific work rather than as the final authority on scientific truth.

Dive into this section: The Hidden Flaws in AI’s Scientific Analysis

Takeaway 2: The Illusion of Accuracy is a Real Risk

"AI models are often most dangerous when they’re wrong with confidence."

🔆 Lumiera Insight: The challenge with well-written responses is that they can mask factual errors or oversimplified conclusions. The authoritative tone of AI outputs is particularly risky in health, where a confidently stated but incorrect interpretation of something like a clinical study could influence decisions that affect patient outcomes. We already know how it goes when we start googling our symptoms, now we’re reaching the next level, where we blindly believe what we see on the other end.

Dive into this section: Challenges of Using AI in Healthtech Startups

Takeaway 3: Bias Can Skew Results

"AI reflects its training data — and that data may not include the people you serve."

🔆 Lumiera Insight: Research on marginalized communities is already scarce; relying solely on AI's limited view risks amplifying existing healthcare disparities. For example, a model confidently summarizing depression treatments based only on open-access literature may overlook culturally specific care practices or alternative approaches detailed in paywalled or less-accessible sources. The result? Decisions that unintentionally sideline the needs of underrepresented populations.

Dive into this section: AI tools for scientific research: Should you use them?

Takeaway 4: Augment, Don't Replace Human Expertise

"Sam Altman, CEO of OpenAI, says he only uses AI for ‘boring tasks’...Use AI for iterative tasks (like summarising, formatting, or data cleaning), but keep humans in the loop for all critical thinking, interpretation, and decision-making."

🔆 Lumiera Insight: AI should accelerate large-scale, time-consuming tasks and support, not replace, complex human reasoning. The goal should be deeper human insights, not shortcut conclusions. In practice, this could mean using AI to process massive datasets or auto-generate research summaries, freeing your scientific team to focus on the complex questions that impact patient outcomes, like whether a screening tool validated in university students will work for elderly populations, or if your intervention needs to be redesigned for communities with different approaches to mental health.

Dive into this section: AI tools for scientific research: should you use them?

Takeaway 5: The Right Questions Still Matter

"If you only ask questions that AI is good at answering, you may miss the questions that actually matter."

🔆 Lumiera Insight: The "monoculture of knowing" in health is a bit like trying to understand a city by only looking at its Google Maps data. You'll see the traffic patterns and popular spots, but miss the details that make each neighborhood unique. When we limit ourselves to AI-friendly inquiries, we risk building solutions that look good on paper but miss crucial human elements: the nuances that affect medication adherence, the unspoken fears that prevent people from seeking care, or the community dynamics that influence health behaviors. These insights rarely emerge from data alone; they come from sitting with patients, observing behaviors, and asking questions AI wouldn't think to ask.

Dive into this section: The red flags scientists are seeing in AI

At Lumiera, we believe in the power of intellectual generosity and perspective density. That's why we feature insights from experts like Dr. McLaughlin. Her work at Sci-Translate exemplifies these values by bridging the gap between cutting-edge scientific research and real-world application, empowering businesses to build more effective, science-backed health solutions. By sharing Anna's expertise, we aim to provide you with the tools and knowledge you need to navigate the fastest-evolving fields in technology.

Until next time.
On behalf of Team Lumiera

Emma - Business Strategist
Allegra - Data Specialist

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