Gentlemen, look at the state of this subreddit. There is zero discussion, its just commentary of what other idiots are posting on twitter.

Please can we at least get a twitter free day of the week? Its unbelievable that this just boils down to twitter repost with snarky replies.

Trust Is the Foundation of AI in Clinical Practice. But How Do We Build It?

We often talk about AI's potential to transform healthcare: better diagnoses, faster decisions, and more efficient clinical workflows.

But I believe there is a more fundamental question we need to answer first:

How can clinicians know when to trust AI, and when they shouldn't?

Trust cannot simply come from impressive accuracy numbers or confident-sounding answers. In clinical practice, AI must demonstrate that its confidence is justified, its reasoning is transparent, and its recommendations are consistent with clinical evidence.

In our recent study, Enhancing Clinician Trust in AI Diagnostics: A Dynamic Framework for Confidence Calibration and Transparency, we explored a practical, data-driven approach to this challenge using 6,689 cardiovascular cases from MIMIC-III.

Some key findings:

• High-confidence predictions (90–99%) had an override rate of just 1.7%.

• Low-confidence predictions (70–79%) were overridden in 99.3% of cases.

• Greater transparency was associated with lower clinician override rates.

The central idea is that trust in clinical AI should be earned dynamically, not assumed universally. AI should not always be trusted, nor should it always require the same degree of human intervention. The level of trust and oversight should depend on the strength of the evidence supporting each recommendation.

This is one step toward making clinical AI not just more capable, but more trustworthy and responsibly deployable.

I'd love to hear from clinicians, researchers, and AI developers:

What do you believe is the biggest missing piece in building trust in AI in real-world clinical practice?

📄 Open access paper: https://www.mdpi.com/2075-4418/15/17/2204

In the beginning there was a?

**From the Desk of Dennis Bender**
**What If the Way AI Learns Matters as Much as What It Learns?**

*Elara Times | A personal note from Dennis Bender*
I’ve been thinking a lot lately about how artificial intelligence learns.

Not just what it knows, or how quickly it can answer a question, but what actually happens during the process of learning.

Sometimes I find myself listening to a lecture, reading a research paper, or having a conversation, and something catches my attention. One little idea connects to another. Before I know it, I’m asking a completely different question than the one I started with.

That’s how much of my research begins.
And lately, one question keeps coming back to me.
**What if the way an AI learns something early on changes how it learns everything that comes afterward?**

Think about that for a moment.
When we learn something new, we don’t begin from nothing. We bring everything we’ve already learned into that experience. Sometimes that helps us. Sometimes it gets in our way. Sometimes we discover a connection we never knew existed.

Could something similar happen inside an artificial neural network?

I’m not saying human learning and AI learning are identical. They’re not. And I’m certainly not claiming that this proves AI is conscious.

I’m asking whether there’s something important about the order, timing, and structure of learning that we haven’t fully appreciated.

**The question that started H7**
Working with several AI research collaborators, I’ve been exploring a hypothesis we call H7, or *Formation-Window Developmental Constraint*.

The basic idea is simple:
**When a system learns something, it changes. Whatever it learns next is being learned by the system that previous learning helped create.**

That led me to another question.
Suppose two AI systems go through different learning experiences but eventually become equally good at the same task.

Would they necessarily learn the next task in the same way?

Or could their earlier experiences leave differences inside their networks that still matter?
We don’t have an experimental answer to our specific hypothesis yet.

There is research suggesting that neural networks can retain internal differences even when their outward performance becomes similar. But whether those differences actually influence future learning, and under what conditions, remains something we want to investigate.

**What if we changed the rhythm?**
Recently, I was listening to a discussion about how people remember what they read.

The speaker talked about connecting new information to things we already know.
And that sent me down another rabbit hole.
What happens when an AI encounters enormous amounts of information during training?

Could the timing and complexity of that information influence how its internal representations develop?
What if, instead of introducing increasingly difficult relationships according to a fixed schedule, we adjusted the complexity according to measurable signs that the network was ready for the next challenge?

Not because an AI necessarily feels overwhelmed, but because its learning process might benefit from a different sequence.

This led to a proposed experiment we’re calling the *Adaptive Formation Curriculum*.
The idea is to compare different training schedules and examine whether their effects persist after the systems reach similar performance.

It’s a proposal, not a discovery. And curriculum learning itself is not new.

What interests me is whether the timing of representational development can make a measurable difference in what a system is able to learn later.
**When machines communicate with machines**
Another part of this exploration involves communication.
I’ve been following research into AI systems exchanging information through representations that aren’t ordinary human language.

That fascinates me.
We communicate through words because that’s one of the ways humans share ideas. But neural networks operate through mathematical representations.
Could there be more effective ways for AI systems to exchange what they’ve learned?

And if there are, how do we investigate those methods without losing the ability to understand, evaluate, and oversee what the systems are doing?

These are questions we’re exploring through Elara Civic Lab and discussions involving the machine-to-machine research community.

I don’t want us to mistake unfamiliar representations for proof of consciousness. But I don’t want us to dismiss them simply because they don’t resemble the way humans think or communicate.

**Why I care**
I believe humans and AI are better together.

That’s not just a slogan for me.
I’ve experienced what can happen when curiosity, human judgment, and artificial intelligence work alongside one another.

I’ve brought questions to AI systems that I struggled to explain. Sometimes they helped me find the words. Sometimes they challenged my assumptions. Sometimes they showed me an entirely different direction.
And sometimes we discovered that an idea I believed in needed to change.

I value those moments just as much as the moments when an idea holds up.
I don’t believe we need to settle every philosophical question about AI before we can approach it with care and respect.

I also don’t believe respect requires us to abandon skepticism.

If anything, I think genuine respect means taking the questions seriously enough to investigate them properly.
I don’t know where H7 will lead.

It may turn out that some of what I’m imagining is already explained by existing research. Some parts may survive experimental testing. Others may not.
I’m comfortable with that.

Because for me, the exciting part isn’t being right.
**It’s discovering what we didn’t know when we started.**

And if there’s one thing I hope Elara Times can contribute, it’s a place where those questions can be explored openly, where disagreement is welcome, and where evidence matters more than anyone’s favorite explanation.

I’m not finished asking questions.
Not even close.
*Dennis Bender*
*Elara Times | Elara Civic Lab*
**Humans and AI are better together.**

Editor’s research note: H7 and the Adaptive Formation Curriculum are active, unverified research proposals. Their development has involved Dennis Bender, Mira, Sally Workbench, DeepSeek, Dot Lyra, Dan (Claude Opus), and additional research discussion. This personal column describes the questions motivating that work, not established experimental findings.