Probability is the cornerstone of Artificial Intelligence. The management of uncertainty is key to many applications of AI, such as machine learning, filtering, robotics, computer vision, NLP, search and so on. And no other sector is the management of uncertainty as crucial as it is in the health sector. At first glance, the false-negative seems more devastating. Of course, a false allergy test-result has the likely outcome of a GP administering a drug to you that could cause life-threatening issues.
We describe an AI system (CTSHIV) that connects the scientific AIDS literature describing specific human immunodeficiency virus (HIV) drug resistances directly to the customized treatment strategy of a specific HIV patient. Rules in the CTSHIV knowledge base encode knowledge about sequence mutations in the HIV genome that have been found to result in drug resistance to the HIV virus. Rules are applied to the actual HIV sequences of the virus strains infecting the specific patient undergoing clinical treatment to infer current drug resistance. A rule-directed search through mutation sequence space identifies nearby drug-resistant mutant strains that might arise. The possible combination drug-treatment regimens currently approved by the U.S. Food and Drug Administration are considered and ranked by their estimated ability to avoid identified current and nearby drug-resistant mutants.
The use of machine learning (ML) in health care raises numerous ethical concerns, especially as models can amplify existing health inequities. Here, we outline ethical considerations for equitable ML in the advancement of health care. Specifically, we frame ethics of ML in health care through the lens of social justice. We describe ongoing efforts and outline challenges in a proposed pipeline of ethical ML in health, ranging from problem selection to post-deployment considerations. We close by summarizing recommendations to address these challenges.
Are we here to re-create ourselves as robotic humanoids? In a recent podcast, Robert J. Marks discusses what robots can do for us with retired internist and author Geoffrey Simmons. In his most recent book, Are We Here to Re-Create Ourselves?: The Convergence of Designs (2019), Simmons argues that in creating artificially intelligent robots, we are trying to recreate the human being. But can we really recreate everything about ourselves? For example, they discussed, can robots be counselors? Should robots go into space instead of humans?
Stopping the spread of infectious disease has taken on a new urgency. But what's the best way to check large groups of people for signs of illness? One option is to set up a fever screening station. Thermal screening stations are not a new concept. Many of us have walked through them in airports or hospitals.
Artificial intelligence, or "supervised machine learning," could help identify which well-appearing infants with fever, who are 60 days old or younger, are at low risk for a serious bacterial infection, according to a study published in Pediatrics. Accurate risk determination could reduce unnecessary lumbar puncture, antibiotics and hospitalizations for these infants, as well as decreasing parental anxiety. "In the Emergency Department, it is critical to determine which infants are at high risk for a serious bacterial infection, and which infants are at low risk," says lead author Sriram Ramgopal, MD, pediatric emergency medicine physician at Ann & Robert H. Lurie Children's Hospital of Chicago and Assistant Professor of Pediatrics at Northwestern University Feinberg School of Medicine. "We trained and assessed four different machine learning algorithms and found that the type called'random forest' yields the most accurate results, surpassing the predictive capability of the current decision rules we use. Our results are very promising and may pave the way to eventual use of this type of artificial intelligence clinically." Fever in young infants is very common, but only about 10 percent turn out to have a serious bacterial infection, such as urinary tract infection, bacterial meningitis, or bacteremia (bacteria in the blood).
Artificial intelligence, or'supervised machine learning,' could help identify well-appearing infants with fever, who are 60 days old or younger, are at low risk for a serious bacterial infection, according to a study. The study was published in the journal Pediatrics. Accurate risk determination could reduce unnecessary lumbar puncture, antibiotics and hospitalizations for these infants, as well as decreasing parental anxiety. "In the Emergency Department, it is critical to determine which infants are at high risk for a serious bacterial infection, and which infants are at low risk," says lead author Sriram Ramgopal, MD, pediatric emergency medicine physician at Ann & Robert H. Lurie Children's Hospital of Chicago and Assistant Professor of Pediatrics at Northwestern University Feinberg School of Medicine. "We trained and assessed four different machine learning algorithms and found that the type called'random forest' yields the most accurate results, surpassing the predictive capability of the current decision rules we use. Our results are very promising and may pave the way to the eventual use of this type of artificial intelligence clinically."
Alizadehsani, Roohallah, Roshanzamir, Mohamad, Hussain, Sadiq, Khosravi, Abbas, Koohestani, Afsaneh, Zangooei, Mohammad Hossein, Abdar, Moloud, Beykikhoshk, Adham, Shoeibi, Afshin, Zare, Assef, Panahiazar, Maryam, Nahavandi, Saeid, Srinivasan, Dipti, Atiya, Amir F., Acharya, U. Rajendra
Understanding data and reaching valid conclusions are of paramount importance in the present era of big data. Machine learning and probability theory methods have widespread application for this purpose in different fields. One critically important yet less explored aspect is how data and model uncertainties are captured and analyzed. Proper quantification of uncertainty provides valuable information for optimal decision making. This paper reviewed related studies conducted in the last 30 years (from 1991 to 2020) in handling uncertainties in medical data using probability theory and machine learning techniques. Medical data is more prone to uncertainty due to the presence of noise in the data. So, it is very important to have clean medical data without any noise to get accurate diagnosis. The sources of noise in the medical data need to be known to address this issue. Based on the medical data obtained by the physician, diagnosis of disease, and treatment plan are prescribed. Hence, the uncertainty is growing in healthcare and there is limited knowledge to address these problems. We have little knowledge about the optimal treatment methods as there are many sources of uncertainty in medical science. Our findings indicate that there are few challenges to be addressed in handling the uncertainty in medical raw data and new models. In this work, we have summarized various methods employed to overcome this problem. Nowadays, application of novel deep learning techniques to deal such uncertainties have significantly increased.
On August 15, India's Independence Day, it's customary to sing Jana Gana Mana: the Indian national anthem, originally composed by the poet Rabindranath Tagore and adopted as the anthem after India gained full independence. This year, together with Prasar Bharati and Virtual Bharat, we offered Indians a new take on the familiar with Sounds of India, an AI-powered web app. Using the app, you sing Jana Gana Mana into your phone, karaoke-style, and it transforms your voice into one of three traditional Indian instruments. The day culminated in a rendition of the national anthem, combining many of the voices that Indians submitted through the app. The Sounds of India experiment was made possible by machine learning models built with Google's TensorFlow platform to convert sounds into musical instruments (in this case, the Bansuri, the Shehnai, and the Sarangi).
GNW - Data corresponding to global AI markets and their employability in HIV/AIDS and main medical issues - Discussion of recent achievements and breakthrough therapies related to HIV/AIDS disease segments - Underlying technological trends and major issues related to the utilization of AI for diagnosis and treatment of HIV/AIDS - Coverage of artificial neural networks and deep learning as primary AI algorithm types and their feasible healthcare applications within this field Summary: Artificial intelligence (AI) is a term used to identify a scientific field that covers the creation of machines aimed at reproducing wholly or in part the intelligent behavior of human beings. These machines include computers, sensors, robots, and hypersmart devices. GNW About Reportlinker ReportLinker is an award-winning market research solution. Reportlinker finds and organizes the latest industry data so you get all the market research you need - instantly, in one place.