Introduction
I attended BioIT World in Boston last week, and – surprise surprise – from clinical decision support to target discovery to lab automation, (usually the order is target then automation then clinical) artificial intelligence (AI) is everywhere, especially in bioinformatics right now. Every company has a slide deck with the word “intelligent” in it, and every tradeshow booth at BioIT World last week was demoing a model that promises to do something smarter, faster, or more scalable.
But here’s the thing…
Everybody’s building it.
Nobody really trusts it.
The elephant in the server room
In a field like bioinformatics—where pipelines can stretch from raw reads to regulatory submissions—the stakes are high. The smallest mistake in a variant call, annotation, or model output isn’t just annoying. It could invalidate a trial, mislead a therapeutic strategy, or even affect patient outcomes down the line.
So it’s no wonder that when someone says “AI will help us interpret this data,” the next question is always: “But how do we know it’s right?”
Trust is the new bottleneck
Let’s be clear: this isn’t a tech problem. The models are improving at a maybe fast paced or breath taking pace. What we lack isn’t performance—it’s confidence.
And in life sciences, trust is earned through:
- Traceability: Can we see how the AI got its answer? Can we audit the data and logic it used?
- Consistency: Does it behave the same way across similar datasets or questions? Or is it like rolling dice every time?
- Domain alignment: Is the AI tuned to the nuance of biology, or is it just pattern-matching from noisy training data?
- Human-in-the-loop design: Can scientists interact with it, correct it, and learn from it—or is it a black box that spits out uneditable predictions?
Right now, most tools fall short on one or more of these fronts.
What will it take to build trustable AI?
Here’s what we’re seeing from savvy customers who are getting it right:
1. Training on internal data
Instead of relying solely on public datasets (which are often incomplete or inconsistent), these teams are fine-tuning models on their own experimental data—annotated, vetted, and high quality.
This makes the output more relevant and increases confidence internally. If your model has seen how your lab handles a CRISPR screen, it’s less likely to hallucinate nonsense when interpreting the results.
2. Building interfaces scientists can understand
No one wants a black box. The best tools we’ve seen show not just what they recommend, but why. Think: confidence scores, evidence trails, or even inline citations back to relevant literature or data sources.
3. Bias-aware development
If your model is trained on 80% cancer data and 20% neurodegeneration, guess what it’s going to over-prioritize? Teams that audit and rebalance training data early are the ones avoiding embarrassing (and dangerous) biases down the road.
4. Tight feedback loops
The most trustable AI tools are the ones that learn from their users. Not just in training, but in deployment. When a scientist corrects an annotation or flags a faulty output, that feedback should improve future results. It’s not about perfection—it’s about evolution.
You don’t need AI that replaces you. You need AI that earns your trust.
The future of bioinformatics isn’t autonomous models replacing scientists. It’s collaborative tools helping scientists do more—faster, safer, and with greater confidence.
R&D teams don’t trust AI yet because most of it hasn’t been built to be trustworthy. But that’s changing. And the companies that prioritize transparency, human oversight, and domain specificity? They’re the ones who’ll actually get value from all that cutting-edge machine learning.
Conclusion
At Bridge Informatics, we’ve helped research teams cut through the AI hype and build tools they trust—whether that means fine-tuning models on proprietary data, designing human-in-the-loop systems, or just figuring out where AI shouldn’t be used (yet).
If your team is grappling with how to make AI not just powerful but reliable, we’d love to chat. Reach out and let’s talk about what trustworthy AI could look like in your pipeline.
Jessica Corrado, Head of Business Development & Commercial Operations, Bridge Informatics
As the Head of Business Development & Commercial Operations, Jessica is responsible for driving strategic growth initiatives and overseeing the company’s commercial activities. She has both a keen understanding of the life sciences industry and a strong track record in building successful partnerships.
Prior to joining Bridge, Jessica held a number of leadership roles across sales, marketing, and communications. Outside of work, Jessica is responsible for the majority of marketing and event planning for Shore Saves, a non-profit animal rescue. She enjoys reading and is often reading at least two books of various genres at a time. If you’re interested in reaching out, please email [email protected].
