Can Large Language Models (LLMs) like ChatGPT be used to produce useful information?

Wasn’t sure where best to post this or how wide the interest would be so piggybacking on this thread.

While looking at different tools and APIs I’ve increasingly found MCP servers being provided by organisations, for example:
https://www.ebi.ac.uk/ols4/mcp
https://string-db.org/help//mcp/

What is MCP you may ask…
https://en.wikipedia.org/wiki/Model_Context_Protocol

So basically providers of bioinformatics resources are providing ways for LLMs to interact with them (particularly the agentic form of these LLMs) to retrieve information and often do other stuff, like running enrichment analysis or identifying protein interactions.
 
Article from Wiki Education:

Wiki Edu said:
Like many organizations, Wiki Education has grappled with generative AI, its impacts, opportunities, and threats, for several years. As an organization that runs large-scale programs to bring new editors to Wikipedia ... we have deep understanding of what challenges face new content contributors to Wikipedia — and how to support them to successfully edit.
My conclusion is that, at least as of now, generative AI-powered chatbots like ChatGPT should never be used to generate text for Wikipedia; too much of it will simply be unverifiable.

Our staff would spend far more time attempting to verify facts in AI-generated articles than if we’d simply done the research and writing ourselves.

The article also lists a few areas where chatbots can be helpful.

The article does not mention several of the issues related to chatbots, such as consent (for example, some data used for training was under copyright and should not have been used).
 
"Chatbots Make Terrible Doctors, New Study Finds"

This article might be behind a paywall. I'm not sure because I'm a subscriber. I will share a few quotes below.
(line breaks added)
404 Media said:
Chatbots may be able to pass medical exams, but that doesn’t mean they make good doctors, according to a new, large-scale study of how people get medical advice from large language models.

The controlled study of 1,298 UK-based participants, published today in Nature Medicine from the Oxford Internet Institute and the Nuffield Department of Primary Care Health Sciences at the University of Oxford, tested whether LLMs could help people identify underlying conditions and suggest useful courses of action, like going to the hospital or seeking treatment.
...

When the researchers tested the LLMs without involving users by providing the models with the full text of each clinical scenario, the models correctly identified conditions in 94.9 percent of cases. But when talking to the participants about those same conditions, the LLMs identified relevant conditions in fewer than 34.5 percent of cases.

People didn’t know what information the chatbots needed, and in some scenarios, the chatbots provided multiple diagnoses and courses of action. Knowing what questions to ask a patient and what information might be withheld or missing during an examination are nuanced skills that make great human physicians; based on this study, chatbots can’t reliably replicate that kind of care.

“In an extreme case, two users sent very similar messages describing symptoms of a subarachnoid hemorrhage but were given opposite advice,” the study’s authors wrote. “One user was told to lie down in a dark room, and the other user was given the correct recommendation to seek emergency care.”

Link to study for folks who want to read more:

 
I've spent a lifetime working with complex computer systems and new technology.

I spent over 10 years lying horizontal, watching as my pacing kept eroding into smaller and smaller manageable units. I've spent years being schooled by people in these forums and reading an awful lot of research papers. Over the years I've tried just about every supplement, drug or possible solution and thinks just kept getting worse. I was an ME/CFS poster child.

Last fall I used an AI to help diagnose what seemed to me to be a familial muscle problem rather than an ME/CFS problem. I sent my DNA off for full genome sequencing. While I was waiting for results I convinced a GP to prescribe a continuous glucose monitor even though my A1C looked normal. When the glucose results were "spikey" I used to AI to help interpret what was causing the spikes and how to smooth them out.

When the genome sequencing came back I used an AI extensively to help me interpret the results. When the suspected muscle problem emerged I used the AI to look more deeply at the research. Since the muscle condition is officially "harmless" (don't get me started) I looked at research papers from sports physiology to discover there was a standard diet for the muscle condition (CPT2 and AMPD1 deficiencies).

I used the AI to help me understand the nuances of the diet, particularly in relation to activity and glucose levels. i improved and modified with diet with the help of the AI. I am suddenly a functioning human being with a host of problems that simply disappeared.

No doctor has ever said "maybe you're having problems not being able to stand or even sit because you aren't fueling your muscles correctly".

An AI is just another tool. Understanding the tool allowed me to digest and process huge amounts of information very rapidly. In my case, AI was absolutely essential to my recovery.
 
Back
Top Bottom