Artificial intelligence in medicine and science

[Trish]

"I Was an Oncologist. When I Got Sick, I Did What Doctors Warn Patients Never to do"

"I have hypermobile Ehlers-Danlos syndrome, a connective tissue disorder that affects nearly every part of my body. My joints dislocate with alarming ease. My gastrointestinal tract operates by its own rules. My autonomic nervous system misfires constantly. I have chronic pain, episodes of exhaustion, medication sensitivities and symptoms that refuse to stay inside one specialty. Managing a disease like mine requires a cardiologist, gastroenterologist, neurologist, rheumatologist, pain specialist, rehabilitation physician and a primary doctor all communicating with one another in ways modern medicine rarely allows."

"Eventually, I did what patients are warned not to do. But I was also a physician, trained to stay with difficult medical problems until they made sense. I immersed myself in everything I could learn about each condition and how it affected every part of my body.

Then I wanted another mind on the case, the way physicians bring cases to a tumor board.

I turned to artificial intelligence.

Even now, writing that feels like a professional betrayal. As a physician, I understand exactly how dangerous AI can be in medicine. It can hallucinate, provide dangerously incorrect information and miss diagnoses. It lacks the accountability clinical care demands. AI should never replace physicians."

I began hearing similar stories from physicians treating other poorly understood chronic illnesses. A leading mast cell activation syndrome expert told me about a woman whose neuropsychiatric symptoms went undiagnosed for six years. Out of curiosity, he uploaded years of hospital and clinical records into ChatGPT. ChatGPT identified MCAS almost immediately and laid out a rationale that aligned with the diagnosis the specialist later reached after three weeks of evaluation.

There is something else AI offers that medicine often does not".

They could have got the same answer on social media where hEDS and MCAS are commonly linked.

 

[hotblack]

For some time now I have been using a committee of AI LLMs

I’ve seen some really interesting work with adversarial iteration until agreement is reached. Sounds like what you’re doing?

 

[mariovitali]

I’ve seen some really interesting work with adversarial iteration until agreement is reached. Sounds like what you’re doing?

No, I wouldn't necessarily make sure that there is an agreement. Basically I start with -for example- several findings from ME/CFS studies (e.g. GWAS, replicated findings etc) and then I ask a number of LLMs (e.g. 3) to suggest a causal hypothesis.

2) Next step is to present to each LLM the hypotheses of the other two and rank it.

3) A PageRank algorithm (this is an example) gives a ranking of the hypotheses and how much each LLM agrees that another hypothesis is better.

4) An "orchestrator" LLM looks at all hypotheses, identifies where all LLMs agree, where they disagree, whether any factual discrepancies were found and whether novel information deserving further investigation has been identified.

It works surprisingly well in my opinion.