u/aestheticcode

Hello everyone,
I am exploring a project where I am working on an AI agent which is supposed to make decisions with incomplete information available to it.
The basic idea is pretty simple: the user gives the agent some initial information but it may not be enough to make a good decision. Instead of the agent just making its best guess I want the agent to recognize the uncertainty and decide whether it should ask another question first.

My current use case is pediatric symptom assessment where a parent may not provide all the relevant information in the first message.

e.g : Parent: "My child has a fever and isn't eating much."
Rather than immediately giving a recommendation, the agent might identify that some information is missing and ask something like: "Is your child having any difficulty breathing?"

Then use this information to update it's assesment.

I'm currently looking at Bayesian inference and HMM as possible ways to think about the uncertainty and question-selection part. But I'm still figuring out how much of this should actually be handled by the LLM versus a separate decision layer.

I'd be interested in hearing from LLM developers:

1. How would you design the question-selection loop?

2. Would you let the LLM choose the next question or have a separate decision layer handle that?

3. How would you prevent the agent from asking endless questions?

4. How do you handle cases where the model is uncertain but still needs to take an action?

5. Any blind spots or failure modes in this setup that I should watch out for?

Looking for your valuable views on it.

I’m currently exploring a problem around AI-assisted pediatric symptom assessment and triage.

The main thing I’m interested in is that parents may not always have complete or reliable information about what is happening with their child. They might notice fever, reduced eating, changes in activity, crying, sleep or breathing but still be unsure about what details are important or how serious the situation might be.

I’m exploring the idea of an AI agent that doesn't make a recommendation from the first set of symptoms alone.

Instead, the agent would:

1. Understand the information currently available.
2. Identify what information is missing or uncertain.
3. Ask a follow-up question that could meaningfully change the assessment.
4. Update its understanding as new observations come in.
5. Either continue gathering information, suggest an appropriate level of care or escalate to a healthcare professional when the uncertainty or risk is too high.

I'm particularly interested in the technical side of this. I'm exploring Bayesian inference and Hidden Markov Model as possible ways to model the problem, since the child's actual health state isn't directly observable and the information comes through observations from the parent.

I'm still at the exploration/prototype stage, so I'm not trying to claim or build something that says an AI should replace clinical assessment.

I'd really appreciate feedback from people working in healthcare, health IT, pediatrics, clinical AI or ML:

• How should an agent decide which question is worth asking next?
• What kinds of uncertainty should trigger human intervention?
• Where do you think this approach could fail?
• Are there existing systems or research I should look into?

I'm especially interested in critical feedback, because I want to understand the limitations before deciding how far this idea should be taken.

Thank you.

I'm working on an AI research project studying pediatric triage and how decisions are made when text descriptions for child's condition lack full context by parents.

1. When you evaluate symptoms , what crucial "hidden state" is most frequently missed in written text by parents?
2. What are the main challenges when patients are sent to the ER when they could be managed elsewhere?
3. What specific sign makes you instantly change decision from "monitor at home" to "go to ER immediately"?

I'm working on an AI research project studying pediatric triage and how decisions are made when text descriptions for child's condition lack full context by parents.

1. When you evaluate symptoms , what crucial "hidden state" is most frequently missed in written text by parents?
2. What are the main challenges when patients are sent to the ER when they could be managed elsewhere?
3. What specific sign makes you instantly change decision from "monitor at home" to "go to ER immediately"?

I'm working on an AI research project studying pediatric triage and how decisions are made when text descriptions for child's condition lack full context by parents.

1. When you evaluate symptoms , what crucial "hidden state" is most frequently missed in written text by parents?
2. What are the main challenges when patients are sent to the ER when they could be managed elsewhere?
3. What specific sign makes you instantly change decision from "monitor at home" to "go to ER immediately"?

If a parent doesn't know an important detail about their child's symptoms, should an AI system make its best recommendation with the available information, or should it ask additional questions before deciding the appropriate level of care?