A Sensor Sniffs for Cancer, Using Artificial Intelligence


Researchers at Memorial Sloan Kettering Cancer Center (MSK) have developed a sensor that can be trained to sniff for cancer, with the help of artificial intelligence. Although the training doesn't work the same way one trains a police dog to sniff for explosives or drugs, the sensor has some similarity to how the nose works. The nose can detect more than a trillion different scents, even though it has just a few hundred types of olfactory receptors. The pattern of which odor molecules bind to which receptors creates a kind of molecular signature that the brain uses to recognize a scent. Like the nose, the cancer detection technology uses an array of multiple sensors to detect a molecular signature of the disease.

How Do Patients Feel About AI in Health Care? It Depends


May 12, 2022 – Artificial intelligence has moved from science fiction to everyday reality in a matter of years, being used for everything from online activity to driving cars. Even, yes, to make medical diagnoses. But that doesn't mean people are ready to let AI drive all their medical decisions. The technology is quickly evolving to help guide clinical decisions across more medical specialties and diagnoses, particularly when it comes to identifying anything out of the ordinary during a colonoscopy, skin cancer check, or in an X-ray image. New research is exploring what patients think about the use of AI in health care.

Have You Heard? AI Can Edit Genes Now


Artificial intelligence does all kinds of things….genomics Genetic engineering has always been a go-to plot twist in sci-fi movies and TV shows. The idea of genetically mutated humans with superior abilities and unique DNAs still has ripple effects on Marvel fans and box offices. But what if we can alter genes in real life? CRISPR gene editing has been doing that since 2012 (no Wolverine or Magneto though). In 2022, this powerful genetic engineering technique is complemented with artificial intelligence.

Artificial Intelligence (AI) Discovers Precise Treatments for Blinding Diseases


The team used artificial intelligence (AI) to analyze images of retinal pigment epithelium (RPE --a layer of retinal tissue that nourishes and supports the retina's light-sensing cells, photoreceptors) at single-cell resolution to create a reference map that locates each subpopulation within the eye. New Discovery on Eye Diseases Distinct differences among the retinal cells (tissue comprising the retina -- vital to human visual perception) have been now identified by the present study that sheds light on tissue targeted by age-related macular degeneration and other diseases. App Helps Parents Detect Signs of Eye Disorders in Children New App named CRADLE surpasses the'gold standard' of sensitivity in diagnosing the pediatric cancer Retinoblastoma, Baylor University researchers say. "These results provide a first-of-its-kind framework for understanding different RPE cell subpopulations and their vulnerability to retinal diseases, and for developing targeted therapies to treat them," says Michael F. Chiang, MD, NEI, National Institutes of Health. "The findings will help us develop more precise cell and gene therapies for specific degenerative eye diseases," says the study's lead investigator, Kapil Bharti, PhD, NEI.

AI recognition of patient race in medical imaging: a modelling study


Previous studies in medical imaging have shown disparate abilities of artificial intelligence (AI) to detect a person's race, yet there is no known correlation for race on medical imaging that would be obvious to human experts when interpreting the images. We aimed to conduct a comprehensive evaluation of the ability of AI to recognise a patient's racial identity from medical images. Using private (Emory CXR, Emory Chest CT, Emory Cervical Spine, and Emory Mammogram) and public (MIMIC-CXR, CheXpert, National Lung Cancer Screening Trial, RSNA Pulmonary Embolism CT, and Digital Hand Atlas) datasets, we evaluated, first, performance quantification of deep learning models in detecting race from medical images, including the ability of these models to generalise to external environments and across multiple imaging modalities. Second, we assessed possible confounding of anatomic and phenotypic population features by assessing the ability of these hypothesised confounders to detect race in isolation using regression models, and by re-evaluating the deep learning models by testing them on datasets stratified by these hypothesised confounding variables. Last, by exploring the effect of image corruptions on model performance, we investigated the underlying mechanism by which AI models can recognise race.

$35 Million In New Funding For AI To Personalize Cancer Treatment


Israeli startup OncoHost announced today an upsized and oversubscribed $35 million Series C funding round, led by ALIVE Israel HealthTech VC, with the participation of Leumi Partners, Menora Mivtachim, OurCrowd and other existing investors. Clinical trial results have shown OncoHost's AI-powered precision oncology platform to have remarkably high accuracy in assessing non-small cell lung cancer (NSCLC) patient response at three months, six months and one year. Through one blood test pre-treatment, the company's multi-patented platform also provides clinicians with potential combination strategies to overcome treatment resistance. Last year, OncoHost CEO Dr. Ofer Sharon told me that "For immunotherapy, the most important treatment modality we have today, the response rate on average across all cancer types is about 20%. With all the promise of immunotherapy, if you have ten patients waiting in your waiting room with advanced cancer, only two will be alive in two years."

How Is Artificial Intelligence Being Used for Advance Research on Cancer?


AI for Cancer Treatment: Cancer is one of the most dangerous diseases in the whole world. Every day people are looking for ways to cure cancer. AI and its various applications are reshaping the way scientists and researchers approach cancer treatment. Tumors are very complex diseases. It is very difficult to study the behavior of a tumor hence the treatment is much difficult.

Ohio AG issues warning about "Frankenstein opioids," more powerful than fentanyl

FOX News

A dangerous, new group of synthetic opioids called nitazenes are rapidly spreading across the U.S. LONDON, Ohio – A dangerous, new group of synthetic opioids called "nitazenes" is rapidly spreading across the U.S. In Ohio, the state's Attorney General Dave Yost issued a warning about the prevalence of nitazenes as the Buckeye state saw an increase in the illicit drug. The drug, nicknamed "Frankestein opioids," can be 1.5 to 40 times more potent than fentanyl. It is not approved for medical use anywhere in the world but is currently being made in clandestine labs, according to a bulletin from the Ohio Bureau of Criminal Investigation (BCI). At BCI, forensic experts are sounding the alarm after tracking a year-over-year increase in nitazenes. In the first quarter of 2022, BCI reported 143 nitazene cases in Ohio, up from 27 cases in the same quarter of 2021.

How AI Algorithms can Detect Diseases with Deep Learning


Diseases like breast and skin cancer can be detected with close to 100% accuracy with the help of deep learning. In simple terms, artificial intelligence (AI) is the ability of a digital computer (or computer-controlled robot) to perform certain tasks with intelligence. AI tends to mimic human intelligence in that it relies on the ability to reason, learn from experience, and make decisions. Learning, reasoning, and problem-solving are the building blocks of artificial and human intelligence. Artificial intelligence has branches or categories such as machine learning and deep learning, which both involve the imitation of human intelligence.

La veille de la cybersécurité


Artificial intelligence (AI) is showing promising results in detecting breast cancer which may otherwise have been missed by radiologists, the largest study of its kind has found. Researchers in Germany discovered that AI can correctly detect interval breast cancers, which develop in between routine screening rounds (usually 24 months in many countries) and can be missed and diagnosed as a false negative result. In 2020, there were 2.3 million women diagnosed with breast cancer and 685 000 deaths globally, according to the World Health Organization (WHO). The peer-reviewed study showed approximately 16 per cent of interval cancers are probably visible during a previous screening while one in five may be too subtle to the human eye and can be missed by radiologists, which is known as'minimal signs'. The findings present an opportunity to detect more cancers at screening with AI, which may help detect breast cancer earlier.