Artificial intelligence in medicine and science

Probably very little because even if AI was perfect, the editors would still be in charge.
Given how much incompetence, bias and corruption it will expose, if anything it will show the necessity of moving away from human gatekeepers, with all their biases, friends in high places and how cheap humans are to buy.

We've even seen in recent years how even the flagships of medical science, the big journals, have little value of their own outside of the unearned reputation they have for having value. Especially, the entire idea of self-correction has been effectively disabled.

It will be exciting, for sure. A lot of people will be howling mad. What comes after will be unrecognizable, but what exists today is not sustainable, and will be swamped by AI-produced science anyway.

A lot of us will be able to say "we told you so", but things will be so chaotic that it will likely get lost in the mix. Everything depends on who controls the AIs, or if they can be controlled at all. And as much as we need a stable society to function, the current stability is so stagnant that it holds no value to any of us returning to a normal life.
 
"Project OSSAS: Custom LLMs to process 100 Million Research Papers"
November 11, 2025

"Today, we’re introducing Project OSSAS in collaboration with LAION and Wynd Labs. Project OSSAS is a large-scale open-science initiative to make the world’s scientific knowledge accessible through structured, AI-generated summaries of research papers. Built on the foundation of Project Alexandria, OSSAS uses custom-trained Large Language Models, and idle compute sourced from tens of thousands of computers around the world to process scientific research papers into a standardized format. This machine-readable format can be explored, searched, and linked across scientific disciplines."
 
Project OSSAS is a large-scale open-science initiative to make the world’s scientific knowledge accessible through structured, AI-generated summaries of research papers
If they can do this with reasonable accuracy it sounds promising. The metadata could be interesting and scale of the project sounds interesting. They seem to be focusing on relationships between papers/embeddings, but I’m unsure if this would translate into tje granularity of some of the concepts we’d be interested in looking at.

Even just being able to run the models locally could be useful on individual papers, 12 and 14B parameters is a bit beyond my meagre RaspberryPi! I’m sure they’ll be quantised and GGUF or MLX packages available soon.
https://huggingface.co/inference-net/Aella-Nemotron-12B
https://huggingface.co/inference-net/Aella-Qwen3-14B
 
There's a website to explore a subset of the papers summarized so far: https://laion.inference.net/embeddings

I typed in "chronic fatigue" and clicked on one at random out of 8 results, and it happened to be authored by @DMissa. I don't see a way to share the link to the summary directly, but it's for this paper: Dysregulated Provision of Oxidisable Substrates to the Mitochondria in ME/CFS Lymphoblasts, 2021, Missailidis et al

But the title in the website seems different. I don't know if this is a paraphrasing by the AI or an alternate title to the paper: "Dysregulated Fuel Source Preference in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Lymphoblasts: Combined Transcriptomics and Proteomics Reveal Elevated Pentose Phosphate Pathway, Fatty Acid β-Oxidation, and Amino Acid Catabolism". I can't find a paper with this title anywhere. But the data seems to match.

Here is one of six claims from the summary, which from a quick check seems to match the numbers in the paper:
Details: Glycolytic enzyme expression is unchanged at the protein level in ME/CFS lymphoblasts, while PPP enzymes are upregulated (mean +20 ± 9%; p = 0.034) with G6PD elevated by 43 ± 10% (p = 5.5 × 10^−4).

Supporting Evidence: Figure 3B shows no significant differences in glycolytic enzyme levels (16 enzymes; binomial and t-tests non-significant). Figure 4A shows PPP enzymes upregulated (mean +20 ± 9%; t-test p = 0.034). Figure 4C shows G6PD elevated (p = 5.5 × 10^−4).

Implications: Supports a shift away from glycolysis toward PPP to supply TCA cycle substrates and NADPH, consistent with compensating for inefficient ATP synthesis.

A claim from another paper that was summarized, this time actually using the real title as I see it in the journal: Transforming growth factor beta (TGF-β) in adolescent chronic fatigue syndrome (2017) Wyller et al.
Details: Plasma levels of TGF-β1, TGF-β2, and TGF-β3 are not elevated in adolescents with CFS compared to healthy controls.

Supporting Evidence: Independent sample comparisons showed no differences across all three isoforms (Table 2; Additional file 2: Table S1). Subgroup analyses by Fukuda and Canada 2003 criteria also showed no differences.

Implications: Systemic TGF-β levels are unlikely to serve as a biomarker distinguishing adolescent CFS from healthy controls; focus should shift to neuroendocrine–immune coupling mechanisms.

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