Deep neural networks (DNNs) have shown to perform very well on large scale object recognition problems and lead to widespread use for real-world applications, including situations where DNN are implemented as "black boxes". A promising approach to secure their use is to accept decisions that are likely to be correct while discarding the others. In this work, we propose DOCTOR, a simple method that aims to identify whether the prediction of a DNN classifier should (or should not) be trusted so that, consequently, it would be possible to accept it or to reject it. Two scenarios are investigated: Totally Black Box (TBB) where only the soft-predictions are available and Partially Black Box (PBB) where gradient-propagation to perform input pre-processing is allowed. Empirically, we show that DOCTOR outperforms all state-of-the-art methods on various well-known images and sentiment analysis datasets. In particular, we observe a reduction of up to 4% of the false rejection rate (FRR) in the PBB scenario. DOCTOR can be applied to any pre-trained model, it does not require prior information about the underlying dataset and is as simple as the simplest available methods in the literature.

RadVid-19, a program which identifies lung injuries through artificial intelligence, is used at the University of Sao Paulo in Brazil. RadVid-19, a program which identifies lung injuries through artificial intelligence, is used at the University of Sao Paulo in Brazil. The tasks resemble those that lawyers, doctors, financial analysts, and management consultants solve for a living. One asks for a diagnosis of a six-year-old patient based on nine pieces of multimedia evidence; another asks for legal advice on a musician's estate; a third calls for a valuation of part of a healthcare technology company. Mercor, which claims to supply "expert data" to every top AI company, says that it spent more than $500,000 to develop 200 tasks that test whether AIs can perform knowledge work with high economic value across law, medicine, finance, and management consulting.

It's an interesting time to be a long-running science fantasy media property in the streaming TV age. Star Trek is in the grip of an existential crisis as it (wrongly) fears it's too old-aged to be relevant. Star Wars became a battlefield in the culture war and, to duck all future bad faith criticism, gave us The Rise of Skywalker. And then there's Doctor Who, which is somehow managing to plough a 62-year furrow and still fill it with original ideas. This week the Doctor and Belinda go up against a sentient cartoon holding the patrons of a 1950s cinema hostage.

The start of any season of Doctor Who is important, doubly so when there's a new co-star to introduce. "The Robot Revolution" has to get us to fall in love with Belinda Chandra (Varada Sethu), ensnare new fans and keep existing ones hooked. Especially since it's the second of two series that Disney paid for, meaning it's got to do well enough to keep the money flowing. It's an awkward teenage date, with Alan clearly trying to win the heart of his beau by buying her one of those star adoption certificates. In 2025, Belinda is now a nurse at a busy London hospital where, in the background, the Doctor is searching for her.

In recent years, large language models (LLMs), empowered by massive text corpora and deep learning techniques, have demonstrated breakthrough advancements in cross-domain knowledge transfer and human-machine dialogue interactions [1]. Within the healthcare domain, LLMs are increasingly deployed across nine core application scenarios, including intelligent diagnosis, personalized treatment, and drug discovery, garnering significant attention from both academia and industry [2, 3]. A particularly important area of focus is the development and evaluation of Chinese medical LLMs, which face unique challenges due to the specialized nature of medical knowledge and the high-stakes implications of clinical decision-making. Hence, ensuring the reliability and safety of these models has become critical, necessitating rigorous evaluation frameworks [4]. Current research on medical LLMs evaluation exhibits two predominant trends. On one hand, general-domain benchmarks (e.g., HELM [5], MMLU [6]) assess foundational model capabilities through medical knowledge tests. On the other hand, specialized medical evaluation systems (e.g., MedQA [7], C-Eval-Medical [8]) emphasize clinical reasoning and ethical compliance. Notably, the MedBench framework [9], jointly developed by institutions including Shanghai AI Laboratory, has emerged as the most influential benchmark for Chinese medical LLMs. By establishing a standardized evaluation system spanning five dimensions--medical language comprehension, complex reasoning, and safety ethics--it has attracted participation from hundreds of research teams.

Rodriguez detailed that the MARS system gives surgeons "two extra arms" for instrument control, as well as camera stability. SURGICAL'REVOLUTION': Surgeon and CEO Dr. Alberto Rodriguez conducted the first-ever augmented reality (AR) abdominal surgery March 11 in Santiago, Chile. 'SCARY' SCHOOL TREND: Multiple Los Angeles-area school districts have investigated instances of "inappropriate," artificial intelligence-generated images of students circulating online and in text messages in recent months. AI IN PDF: Adobe announced that its new Acrobat artificial intelligence assistant will be available to Acrobat and Reader users starting on Tuesday. POTHOLE HEALER: Tech firm Robotiz3d is developing three technologies as part of its Autonomous Road Repair System.

When medical student Alyssa Murillo stepped into surgery, she was met with something most wouldn't expect to find in an operating room: a towering surgical robot. She wasn't there to observe the kind of surgeries she was used to seeing; instead she was getting an in-depth view inside the patient's body through the robot's video console. "It was incredible," says Murillo, who is now a forth-year general surgery resident at the University of California, San Francisco. "You have a full 3D view, which is different from any other minimally invasive surgery technique." The robot Murillo is referring to is the Da Vinci Surgical System.

What if, in the end, we are done in not by intercontinental ballistic missiles or climate change, not by microscopic pathogens or a mountain-size meteor, but by … text? Simple, plain, unadorned text, but in quantities so immense as to be all but unimaginable--a tsunami of text swept into a self-perpetuating cataract of content that makes it functionally impossible to reliably communicate in any digital setting? Our relationship to the written word is fundamentally changing. So-called generative artificial intelligence has gone mainstream through programs like ChatGPT, which use large language models, or LLMs, to statistically predict the next letter or word in a sequence, yielding sentences and paragraphs that mimic the content of whatever documents they are trained on. They have brought something like autocomplete to the entirety of the internet.

A team of researchers from the University of British Columbia and BC Cancer have developed an artificial intelligence (AI) model that predicts cancer patient survival more accurately and with more readily available data than previous tools. The model uses natural language processing (NLP)--a branch of AI that understands complex human language--to analyze oncologist notes following a patient's initial consultation visit--the first step in the cancer journey after diagnosis. By identifying characteristics unique to each patient, the model was shown to predict six-month, 36-month and 60-month survival with greater than 80 percent accuracy. The findings were published today in JAMA Network Open. "Predicting cancer survival is an important factor that can be used to improve cancer care," said lead author Dr. John-Jose Nunez, a psychiatrist and clinical research fellow with the UBC Mood Disorders Centre and BC Cancer.

Google has showcased a new AI model that helps you to read doctor prescriptions and find the medicines prescribed easily.