Group items tagged

"Cancer diagnosis and management depend upon the extraction of complex information from microscopy images by pathologists, which requires time-consuming expert interpretation prone to human bias. Supervised deep learning approaches have proven powerful, but are inherently limited by the cost and quality of annotations used for training. Therefore, we present Histomorphological Phenotype Learning, a self-supervised methodology requiring no labels and operating via the automatic discovery of discriminatory features in image tiles. Tiles are grouped into morphologically similar clusters which constitute an atlas of histomorphological phenotypes (HP-Atlas), revealing trajectories from benign to malignant tissue via inflammatory and reactive phenotypes. These clusters have distinct features which can be identified using orthogonal methods, linking histologic, molecular and clinical phenotypes. Applied to lung cancer, we show that they align closely with patient survival, with histopathologically recognised tumor types and growth patterns, and with transcriptomic measures of immunophenotype. These properties are maintained in a multi-cancer study."

"An artificial intelligence program has been developed that is better at spotting breast cancer in mammograms than expert radiologists. The AI outperformed the specialists by detecting cancers that the radiologists missed in the images, while ignoring features they falsely flagged as possible tumours."

"Hospitals in London have had to cancel cancer operations this week because of a Russian cyber-attack that continues to cause serious disruption to NHS services in the capital. St Thomas' and King's College hospitals have postponed procedures that their surgeons were due to perform on cancer patients since the attack began last Monday, the Guardian can reveal."

"AI Can Now Predict Your Chances of Surviving Cancer Oct 20, 2023 at 1:00 PM EDT "

"Computers can classify skin cancers as successfully as human experts, according to the latest research attempting to apply artificial intelligence to health."

"Sequencing cancer to find the breaks has become easy and cheap. But information on which drugs might or might not work on particular mutations remains buried in PDF files and scattered across medical journals."

"Drug and insurance companies will from later this year be able to buy information on patients - including mental health conditions and diseases such as cancer, as well as smoking and drinking habits - once a single English database of medical data has been created."

"Apocalypsarians who are equally convinced that a super-intelligent AI will have no interest in curing cancer or old age, or ending poverty, but will - malevolently or maybe just accidentally - bring about the end of human civilisation as we know it."

"I've got a theory of change I call the "peak indifference" theory. The early stage of a crisis involves trying to convince people that the crisis even exists, because things haven't gotten really terrible yet and it's not obvious that there's anything to really worry about, and the people who profit from the status quo will spend liberally to convince people that there's no reason to worry or change anything (see also: climate change, Facebook, cancer from smoking)."

"Unlike other companies, Zebra's algorithms provide an actual diagnosis, completely automatically using only imaging data. This is a very new field - older technology was always driven by the radiologist, and never automatic. The algorithms are part of the Zebra Analytics Platform - a cloud based analytics engine that receives medical imaging studies, analyzes them and returns results to participating hospitals and physicians."

"Rather than being "taught" medical knowledge, the AI was asked to learn using unsupervised deep neural networks, known as autoencoders, without being given any medical knowledge. The researchers developed a method for translating the features found by the AI-only numbers initially-into high-resolution images that can be understood by humans."

"Do the potential paying customers for these large models add up to enough money to keep the servers on? That's the 13 trillion dollar question, and the answer is the difference between WorldCom and Enron, or dotcoms and cryptocurrency. Though I don't have a certain answer to this question, I am skeptical. AI decision support is potentially valuable to practitioners. Accountants might value an AI tool's ability to draft a tax return. Radiologists might value the AI's guess about whether an X-ray suggests a cancerous mass. But with AIs' tendency to "hallucinate" and confabulate, there's an increasing recognition that these AI judgments require a "human in the loop" to carefully review their judgments. In other words, an AI-supported radiologist should spend exactly the same amount of time considering your X-ray, and then see if the AI agrees with their judgment, and, if not, they should take a closer look. AI should make radiology more expensive, in order to make it more accurate. But that's not the AI business model. AI pitchmen are explicit on this score: The purpose of AI, the source of its value, is its capacity to increase productivity, which is to say, it should allow workers to do more, which will allow their bosses to fire some of them, or get each one to do more work in the same time, or both. The entire investor case for AI is "companies will buy our products so they can do more with less." It's not "business custom­ers will buy our products so their products will cost more to make, but will be of higher quality.""