Background: Artificial intelligence (AI) now plays a critical role in almost every area of our daily lives and academic disciplines due to the growth of computing power, advances in methods and techniques, and the explosion of the amount of data; medicine is not an exception. Rather than replacing clinicians, AI is augmenting the intelligence of clinicians in diagnosis, prognosis, and treatment decisions. Summary: Kidney disease is a substantial medical and public health burden globally, with both acute kidney injury and chronic kidney disease bringing about high morbidity and mortality as well as a huge economic burden. Even though the existing research and applied works have made certain contributions to more accurate prediction and better understanding of histologic pathology, there is a lot more work to be done and problems to solve. Key Messages: AI applications of diagnostics and prognostics for high-prevalence and high-morbidity types of nephropathy in medical-resource-inadequate areas need special attention; high-volume and high-quality data need to be collected and prepared; a consensus on ethics and safety in the use of AI technologies needs to be built. Artificial intelligence (AI) now plays a critical role in almost every area of our daily lives and academic disciplines; medicine is not an exception.
While researchers are trained to do research, there is little training for peer review. Several initiatives and experiments have looked to address this challenge. Recently, the ICML 2020 conference adopted a method to select and then mentor junior reviewers, who would not have been asked to review otherwise, with a motivation of expanding the reviewer pool to address the large volume of submissions.43 An analysis of their reviews revealed that the junior reviewers were more engaged through various stages of the process as compared to conventional reviewers. Moreover, the conference asked meta reviewers to rate all reviews, and 30% of reviews written by junior reviewers received the highest rating by meta reviewers, in contrast to 14% for the main pool. Training reviewers at the beginning of their careers is a good start but may not be enough. There is some evidence8 that quality of an individual's review falls over time, at a slow but steady rate, possibly because of increasing time constraints or in reaction to poor-quality reviews they themselves receive. While researchers are trained to do research, there is little training for peer review … Training reviewers at the beginning of their careers is a good start but may not be enough.
As an Applied Data Scientist at Civis, I implemented the latest data science research to solve real-world problems. We recently worked with a global tool manufacturing company to reduce churn among their most loyal customers. A newly proposed tool, called SHAP (SHapley Additive exPlanation) values, allowed us to build a complex time-series XGBoost model capable of making highly accurate predictions for which customers were at risk, while still allowing for an individual-level interpretation of the factors that made each of these customers more or less likely to churn. To understand why this is important, we need to take a closer look at the concepts of model accuracy and interpretability. Until recently, we always had to choose between an accurate model that was hard to interpret, or a simple model that was easy to explain but sacrificed some accuracy.
Resonance, a powerful and pervasive phenomenon, appears to play a major role in human interactions. This article investigates the relationship between the physical mechanism of resonance and the human experience of resonance, and considers possibilities for enhancing the experience of resonance within human–robot interactions. We first introduce resonance as a widespread cultural and scientific metaphor. Then, we review the nature of “sympathetic resonance” as a physical mechanism. Following this introduction, the remainder of the article is organized in two parts. In part one, we review the role of resonance (including synchronization and rhythmic entrainment) in human cognition and social interactions. Then, in part two, we review resonance-related phenomena in robotics and artificial intelligence (AI). These two reviews serve as ground for the introduction of a design strategy and combinatorial design space for shaping resonant interactions with robots and AI. We conclude by posing hypotheses and research questions for future empirical studies and discuss a range of ethical and aesthetic issues associated with resonance in human–robot interactions.
Artificial intelligence (AI) may be able to identify alcoholics at risk of relapsing after treatment, researchers say. Patients often return to heavy drinking during and after treatment, and may require multiple tries before they can achieve long-term abstinence from unhealthy alcohol use. AI may allow care providers and patients to predict drinking relapses and adjust treatment before they occur, Yale University researchers found. In a new study, the investigators used clinical data and a form of AI called machine learning to develop models to predict relapses among patients in an outpatient treatment program. Data from more than 1,300 U.S. adults in a 16-week clinical trial of treatments in 11 centers were used to develop and test the predictive models.
Perovskites are a family of materials that are currently the leading contender to potentially replace today's silicon-based solar photovoltaics. They hold the promise of panels that are far thinner and lighter, that could be made with ultra-high throughput at room temperature instead of at hundreds of degrees, and that are cheaper and easier to transport and install. But bringing these materials from controlled laboratory experiments into a product that can be manufactured competitively has been a long struggle. Manufacturing perovskite-based solar cells involves optimizing at least a dozen or so variables at once, even within one particular manufacturing approach among many possibilities. But a new system based on a novel approach to machine learning could speed up the development of optimized production methods and help make the next generation of solar power a reality.
The Food and Drug Administration has approved the first artificial intelligence software to be used to interpret dental x-rays, allowing dentists to better screen for oral pathologies. Pearl's Second Opinion is the first and only FDA-cleared AI radiologic detection aid for dentists at the chairside, and it can assist dentists to discover a variety of common dental diseases such as tooth decay, calculus, and root abscesses. Pearl gathered over 100 million dental x-rays from dental practices and academic institutes to create Second Opinion. The AI platform highlights anomalies in x-rays and also acts as a patient communication tool, allowing dentists to exhibit alternative models of a patient's teeth and highlight trouble regions. Pearl's announcement is a significant step forward in the field of technology-assisted dentistry.
The area of data science is large and fast expanding. It's no surprise that so many people want to learn more about it! But what is data science, and what do you need to know if you want to work in this field? One of the most important things to understand about data science is that it is a very hands-on and ever-changing discipline. It's critical to keep learning new things in order to stay current with the latest trends and practices in the field.
Communication is a natural part of our everyday lives. People interact using voice and text, forming sentences to express what they desire. And yet, most of the search and discovery patterns out there rely on menu items and filter facets. Building on our mission at Booking.com: "Making it easier for everyone to experience the world", the ML & AI Product teams based in Tel Aviv decided to challenge the conventional search patterns by allowing the most natural way for everyone to communicate: using their voice. This is the story of how we built a native in-app voice assistant at Booking.com, and as far as I know, the first voice search available today by a global online travel company.
Automated deep learning analysis of fundus photographs showed high diagnostic accuracy in determining primary open-angle glaucoma, with increased ability to detect glaucoma earlier than human readers. A deep learning (DL) algorithm was trained, validated and tested on the fundus stereophotographs of participants enrolled in the Ocular Hypertension Treatment Study (OHTS), a randomized clinical trial evaluating the safety and efficacy of IOP-lowering medications in preventing progression from ocular hypertension to primary open-angle glaucoma (POAG). Assessment of optic disc and visual field changes in the OHTS was performed by two reading centers and a masked committee of glaucoma specialists, "a demanding, laborious and complicated task," according to the authors. The OHTS data set consisted of fundus photographs from 1,636 participants, of which 1,147 were included in the training set, 167 in the validation set and 322 in the test set. The DL model detected conversion to POAG with high diagnostic accuracy, suggesting that artificial intelligence can offer a reliable tool to automate the determination of glaucoma for clinical trial management, simplifying the process of human interpretation and, possibly, making it more standardized, objective and accurate.