health & medicine


Top 10 Limitations of Artificial Intelligence and Deep Learning - Amit Ray

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Artificial Intelligence (AI) has provided remarkable capabilities and advances in image understanding, voice recognition, face recognition, pattern recognition, natural language processing, game planning, military applications, financial modeling, language translation, and search engine optimization. In medicine, deep learning is now one of the most powerful and promising tool of AI, which can enhance every stage of patient care --from research, omics data integration, combating antibiotic resistance bacteria, drug design and discovery to diagnosis and selection of appropriate therapy. It is also the key technology behind self-driving car. However, deep learning algorithms of AI have several inbuilt limitations. To utilize the full power of artificial intelligence, we need to know its strength and weakness and the ways to overcome those limitations in near future.


Analyzing Brain Activity to Detect and Treat Patient Pain Even When Unconscious

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Researchers from MIT and elsewhere have developed a system that detects pain in patients by analyzing brain activity from a wearable neuroimaging device, which could help doctors diagnose and treat pain in unconscious and noncommunicative patients. Researchers from MIT and elsewhere have developed a system that measures a patient's pain level by analyzing brain activity from a portable neuroimaging device. The system could help doctors diagnose and treat pain in unconscious and noncommunicative patients, which could reduce the risk of chronic pain that can occur after surgery. Pain management is a surprisingly challenging, complex balancing act. Overtreating pain, for example, runs the risk of addicting patients to pain medication.


Global Artificial Intelligence In Behavioral And Mental Health Care Market 2019-2026 Top key players are AdvancedMD , Cerner , Core Solutions , Credible Behavioral Health , ICANotes , InSync Healthcare Solutions , iSalus Healthcare , Kareo , Meditab Software , Mentegram , Mindlinc – Business Intelligence

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The report provides a basic overview of the industry including definitions, classifications, applications and industry chain structure. The Artificial Intelligence In Behavioral And Mental Health Care market analysis is provided for the international market including development history, competitive landscape analysis, and major regions. Top Key players covered @ AdvancedMD, Cerner, Core Solutions, Credible Behavioral Health, ICANotes, InSync Healthcare Solutions, iSalus Healthcare, Kareo, Meditab Software, Mentegram, Mindlinc, Netsmart, Nextgen Healthcare, NextStep Solutions, Nuesoft Technologies, Qualifacts, Raintree Systems, Sigmund Software, The Echo Group, TheraNest, Valant, Welligent, WRS Health, and many more. The Global Artificial Intelligence In Behavioral And Mental Health Care Industry 2019 Market Research Report is a professional and in-depth study on the current state of the Artificial Intelligence In Behavioral And Mental Health Care market. Development policies and plans are discussed as well as manufacturing processes and cost structures.


Artelus is using AI to save people from going blind. Here's how

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Rajarajeshwari Kodhandapani has a dream – to screen one million people for diabetic retinopathy (DR) so they can get timely treatment and not risk going blind. She is one of the four co-founders of Artelus, along with tech veterans Vish Durga, Lalit Pant, and Pradeep Walia, who is also a serial entrepreneur. As a former business analyst, she never thought she would become an entrepreneur (though she did want to become a politician at one time). Now, she is part of Artelus, a company that builds advanced screening tools to allow doctors and hospitals to diagnose a greater number of patients in the same time for a variety of diseases. Today, she wants to reach the people they call the "forgotten billion" – those in rural areas who cannot afford healthcare.


FDA Clears GE Healthcare's AI Algorithms Embedded on Mobile X-Ray Device

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GE Healthcare announced the Food and Drug Administration's 510(k) clearance of Critical Care Suite, a collection of artificial intelligence (AI) algorithms embedded on a mobile X-ray device. Built-in collaboration with UC San Francisco (UCSF), using GE Healthcare's Edison platform, the AI algorithms help to reduce the turn-around time it can take for radiologists to review a suspected pneumothorax, a type of collapsed lung. Additional partners in the development of Critical Care Suite include St. Luke's University Health Network, Humber River Hospital, and CARING – Mahajan Imaging – India. A prioritized "STAT" X-ray can sit waiting for up to eight hours for a radiologist's review1. However, when a patient is scanned on a device with Critical Care Suite, the system automatically analyzes the images by simultaneously searching for a pneumothorax.


Talk Story: AI in Healthcare - XLR8HI

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Sandeep Reddy is a certified health informatician and Artificial Intelligence (AI) in Healthcare researcher based at the Deakin School of Medicine, Geelong. He has a medical and healthcare management background in addition to having completed AI and machine learning training through several sources. He is currently engaged in research about the safety and quality of the application of AI in healthcare delivery in addition to developing AI models to treat and manage chronic diseases. Also, he has authored several articles and books about the use of AI in Medicine. Further, he has set up local and international forums to promote the use of AI in Healthcare and is advising several start-ups about AI solutions for healthcare issues.


Could doctors use machine learning to detect heart attacks faster?

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But Dr Louise Cullen, an emergency physician at the Royal Brisbane and Women's Hospital and one of the study's authors, said there were arbitrary cut-offs for troponin levels considered to be an indicator of a heart attack. "We see people come to hospital with heart damage and high levels of troponin, some of them are having a heart attack and some have other causes," Dr Cullen said. "There's an arbitrary cut-off point for indicating a heart attack based on a so-called normal population. "The problem is we know the older you get and whether you're male or female makes a difference on what that value should be.


Could doctors use machine learning to detect heart attacks faster?

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But Dr Louise Cullen, an emergency physician at the Royal Brisbane and Women's Hospital and one of the study's authors, said there were arbitrary cut-offs for troponin levels considered to be an indicator of a heart attack. "We see people come to hospital with heart damage and high levels of troponin, some of them are having a heart attack and some have other causes," Dr Cullen said. "There's an arbitrary cut-off point for indicating a heart attack based on a so-called normal population. "The problem is we know the older you get and whether you're male or female makes a difference on what that value should be.


Machine Learning Improves the Diagnosis of Patients with Head and Neck Cancers

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Every year, more than 17,000 people in Germany are diagnosed with head and neck cancers. These include cancers of the oral cavity, larynx and nose, but can also affect other areas of the head and neck. Some head and neck cancer patients will also develop lung cancer. "In the large majority of cases, it is impossible to determine whether these represent pulmonary metastases of the patient's head and neck cancer or a second primary cancer, i.e. primary lung cancer," explains Prof. Dr. Frederick Klauschen of Charité's Institute of Pathology, who co-led the study alongside Prof. Dr. David Capper of Charité's Department of Neuropathology. "This distinction is hugely important in the treatment of people affected by these cancers," emphasizes Prof. Klauschen, adding: "While surgery may provide a cure in patients with localized lung cancers, patients with metastatic head and neck cancers fare significantly worse in terms of survival and will require treatments such as chemoradiotherapy."


Artificial Intelligence Takes On Big Pharma - Utah Business

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My nose is pressed to the glass as I watch thousands of human cellular samples whisk by, running through a large, elaborate machine. There are lab workers bustling around in neatly pressed lab coats, jotting down notes on their clipboards. It looks like a scene from a futuristic movie. This is real life, and the technology in use is poised to change the world of healthcare as we know it. "I never wanted to work for a pharmaceutical company," says Amanda Guisbond, the director of corporate communications at Recursion Pharmaceuticals.