In the lead up to #ALTA2024
we're highlighting excellent talks and papers from previous ALTA events.
This paper, from Vlada Rozova, Jinghui Liu, and Mike Conway at #UniMelb from #ALTA2023, applies #NLP to help solve problems of ambiguity and complexity in #medical#clinical data.
This paper also formed part of the informative #tutorial series at #ALTA2023.
I see a lot of people talking about #science as a #religion, or the closely related idea of “#scientism,” the purported ideology that says science is the only way to know things. Oh, I’m not talking about you, they’ll solemnly assure anyone who objects. Naturally you know better. Just … you know … them. Those people, out there. The great unwashed. On the #internet, nobody knows how long it’s been since you took a shower.
You know what I hardly ever see? The phenomenon in question.
There are people who think that way. Yes. Ideologues of science—hardly if ever #scientists themselves—who invoke The #Scientific Method™ (that’s a whole ‘nother rant) as the be-all and end-all justification for whatever nonsense they spew. Such posts and comments have crossed my feed a time or two. But they are vastly outnumbered by those who complain about them, at least where I can see both groups. I have no reason to believe my experience is atypical in this regard.
As a scientist myself, I think science is a very good way to understand certain things. In my field, it’s the best way to know what makes you sick, and hopefully what will make you better. There are other ways to learn these things, sure, and many of them can be useful places to start. If you don’t end up with a #clinical#trial sooner or later, you’re as likely to kill as cure.
To know what we’re seeing when we look up at the lights in the sky. How the natural world around us, of which we’re a part whether we like it or not, changes and how we both affect and are affected by that change. What came before us, and what might come after. The fundamental building blocks of reality. All these require science for real understanding. If you try to puzzle them out any other way, you may learn something, but you’ll also fill your head with a lot of nonsense. Sorting the wheat from the chaff later is a lot harder than doing it right the first time.
Other questions are at least amenable to scientific inquiry, although that process itself may not be enough. What my fiancee does as a #historian looks, to me, a lot like what I do as a #biomedical#researcher. Make observations, construct #hypotheses, gather evidence, test and revise. (And revise, and revise, and …) But #history vanishes every minute. What’s left is always fragmentary, and shaped by the interactions of modern minds with those long since gone to dust. There will never be an objective truth, only the truest story that can be told.
And then there are things beyond any kind of quantitative analysis, or even rigorous qualitative description. We may be able to agree on what makes a true story, more or less, but what makes a good one? That’s inherently personal. A happy marriage, a tasty meal, a satisfying job—only we can define what these goals mean for ourselves. Science may at best, occasionally, provide vague guidelines. Even then, my advice will not determine your experience.
My perspective is unusual in one key way, sure: not too many people do science for a living, at least not compared to other jobs. With regards to the way people talk about science, I think it’s not unusual at all, except maybe that I pay particular attention.
The division above—things that clearly belong in science’s domain, things that clearly don’t, and a whole bunch in the middle—is a whole lot more common than the idea of science as the One True. It’s at least somewhat more common than blanket rejection of science too, but not as much as it should be. That’s also a rant for another time.
Which all makes me wonder what people who never miss a chance to bring up “scientism” and science-as-religion get out of it.
Before a drug is approved by the U.S. Food and Drug Administration (FDA), it must demonstrate both safety and efficacy.
However, the #FDA does not require an understanding a drug’s mechanism of action for approval.
This acceptance of results without explanation raises the question of whether the "#blackbox" decision-making process of a safe and effective #artificial#intelligence model must be fully explained in order to secure FDA approval.
This topic was one of many discussion points addressed on Monday, Dec. 4 during the 🔸"MIT Abdul Latif Jameel Clinic for Machine Learning in Health AI and Health Regulatory Policy Conference", 🔸which ignited a series of discussions and debates amongst faculty; regulators from the United States, EU, and Nigeria; and industry experts concerning the regulation of AI in health.
As #machine#learning continues to evolve rapidly, uncertainty persists as to whether regulators can keep up and still reduce the likelihood of harmful impact while ensuring that their respective countries remain competitive in innovation.
To promote an environment of frank and open discussion, the Jameel Clinic event’s attendance was highly curated for an audience of 100 attendees debating through the enforcement of the Chatham House Rule, to allow speakers anonymity for discussing controversial opinions and arguments without being identified as the source.
Rather than hosting an event to generate buzz around AI in health, the Jameel Clinic's goal was to create a space to keep regulators apprised of the most cutting-edge advancements in #AI, while allowing faculty and industry experts to propose new or different approaches to #regulatory frameworks for AI in #health, especially for AI use in #clinical settings and in #drug#development.
AI’s role in medicine is more relevant than ever, as the industry struggles with a post-pandemic labor shortage, increased costs (“Not a salary issue, despite common belief,” said one speaker), as well as high rates of burnout and resignations among health care professionals.
One speaker suggested that priorities for clinical AI deployment should be focused more on operational #tooling rather than patient diagnosis and treatment.
One attendee pointed out a “clear lack of #education across all constituents — not just amongst developer communities and health care systems, but with patients and regulators as well.”
Given that medical doctors are often the primary users of clinical AI tools, a number of the medical doctors present pleaded with regulators to consult them before taking action.
#Data#availability was a key issue for the majority of AI researchers in attendance.
They lamented the lack of data to make their AI tools work effectively.
Many faced barriers such as intellectual property barring access or simply a dearth of large, high-quality datasets.
“Developers can’t spend billions creating data, but the FDA can,” a speaker pointed out during the event.
“There’s a price uncertainty that could lead to underinvestment in AI.”
Speakers from the EU touted the development of a system obligating governments to make health data available for AI researchers.
💙 Interested in our call 7 topic on #clinical management of #HeartDisease?
👭 Looking for partners for your consortium?
💻 Sign up for our online pitching session at 14:30 today (23/1) and hear directly from potential partners!
Register here 👉 http://bit.ly/3gRG4AI
Find out more about the call and how to apply 👉 https://europa.eu/!64yx8Y #HorizonEU
In a recent study published in the journal #NatureMedicine, researchers presented a wireless broadband acousto-mechanical sensing (#BAMS) system for continuous #physiological#monitoring.
In neonates and children, #cardiovascular and #gastrointestinal problems are the significant causes of death in the first five years of life. Using continuous monitoring systems helps guide #clinical decisions
IMI project result: our eTRANSAFE project has developed an 'app' to make it easier for researchers to analyse which #biomarkers are being used in which #clinical studies.
Find out more: https://europa.eu/!qBdGXG