Hasil untuk "Medicine"

A. Agustí, E. Bel, M. Thomas et al.

T. Greenhalgh, J. Howick, N. Maskrey

Trisha Greenhalgh and colleagues argue that, although evidence based medicine has had many benefits, it has also had some negative unintended consequences. They offer a preliminary agenda for the movement’s renaissance, refocusing on providing useable evidence that can be combined with context and professional expertise so that individual patients get optimal treatment

Michelle Whirl‐Carrillo, E. McDonagh, J. Hebert et al.

D. Giljohann, D. Seferos, Weston L. Daniel et al.

B. V. Slaughter, S. Khurshid, O. Fisher et al.

Toru Abolhassani, Masaki Aburjai, O. Anant et al.

R. Steinman, J. Banchereau

M. Weinstein, J. Siegel, M. Gold et al.

Anthony Alan Aderem, Frederick W. Alt, K. Austen et al.

W. Kraemer, K. Adams, E. Cafarelli et al.

C. Badiu

A. Yetisen, J. L. Martinez-Hurtado, B. Ünal et al.

Wearables as medical technologies are becoming an integral part of personal analytics, measuring physical status, recording physiological parameters, or informing schedule for medication. These continuously evolving technology platforms do not only promise to help people pursue a healthier life style, but also provide continuous medical data for actively tracking metabolic status, diagnosis, and treatment. Advances in the miniaturization of flexible electronics, electrochemical biosensors, microfluidics, and artificial intelligence algorithms have led to wearable devices that can generate real‐time medical data within the Internet of things. These flexible devices can be configured to make conformal contact with epidermal, ocular, intracochlear, and dental interfaces to collect biochemical or electrophysiological signals. This article discusses consumer trends in wearable electronics, commercial and emerging devices, and fabrication methods. It also reviews real‐time monitoring of vital signs using biosensors, stimuli‐responsive materials for drug delivery, and closed‐loop theranostic systems. It covers future challenges in augmented, virtual, and mixed reality, communication modes, energy management, displays, conformity, and data safety. The development of patient‐oriented wearable technologies and their incorporation in randomized clinical trials will facilitate the design of safe and effective approaches.

G. Ferraioli, V. Wong, L. Castéra et al.

The World Federation for Ultrasound in Medicine and Biology has produced these guidelines for the use of elastography techniques in liver diseases. For each available technique, the reproducibility, results and limitations are analyzed, and recommendations are given. This set of guidelines updates the first version, published in 2015. Since the prior guidelines, there have been several advances in technology. The recommendations are based on the international published literature, and the strength of each recommendation is judged according to the Oxford Centre for Evidence-Based Medicine. The document has a clinical perspective and is aimed at assessing the usefulness of elastography in the management of liver diseases.

Fei Wang, L. Casalino, Dhruv Khullar

Tim Hulsen, S. Jamuar, A. Moody et al.

For over a decade the term “Big data” has been used to describe the rapid increase in volume, variety and velocity of information available, not just in medical research but in almost every aspect of our lives. As scientists, we now have the capacity to rapidly generate, store and analyse data that, only a few years ago, would have taken many years to compile. However, “Big data” no longer means what it once did. The term has expanded and now refers not to just large data volume, but to our increasing ability to analyse and interpret those data. Tautologies such as “data analytics” and “data science” have emerged to describe approaches to the volume of available information as it grows ever larger. New methods dedicated to improving data collection, storage, cleaning, processing and interpretation continue to be developed, although not always by, or for, medical researchers. Exploiting new tools to extract meaning from large volume information has the potential to drive real change in clinical practice, from personalized therapy and intelligent drug design to population screening and electronic health record mining. As ever, where new technology promises “Big Advances,” significant challenges remain. Here we discuss both the opportunities and challenges posed to biomedical research by our increasing ability to tackle large datasets. Important challenges include the need for standardization of data content, format, and clinical definitions, a heightened need for collaborative networks with sharing of both data and expertise and, perhaps most importantly, a need to reconsider how and when analytic methodology is taught to medical researchers. We also set “Big data” analytics in context: recent advances may appear to promise a revolution, sweeping away conventional approaches to medical science. However, their real promise lies in their synergy with, not replacement of, classical hypothesis-driven methods. The generation of novel, data-driven hypotheses based on interpretable models will always require stringent validation and experimental testing. Thus, hypothesis-generating research founded on large datasets adds to, rather than replaces, traditional hypothesis driven science. Each can benefit from the other and it is through using both that we can improve clinical practice.

Robert Fallar, Valerie Parkas, R. Karani

K. W. Chan, V. Wong, S. Tang

As of 22 February 2020, more than 77662 cases of confirmed COVID-19 have been documented globally with over 2360 deaths. Common presentations of confirmed cases include fever, fatigue, dry cough, upper airway congestion, sputum production, shortness of breath, myalgia/arthralgia with lymphopenia, prolonged prothrombin time, elevated C-reactive protein, and elevated lactate dehydrogenase. The reported severe/critical case ratio is approximately 7-10% and median time to intensive care admission is 9.5-10.5 days with mortality of around 1-2% varied geographically. Similar to outbreaks of other newly identified virus, there is no proven regimen from conventional medicine and most reports managed the patients with lopinavir/ritonavir, ribavirin, beta-interferon, glucocorticoid and supportive treatment with remdesivir undergoing clinical trial. In China, Chinese medicine is proposed as a treatment option by national and provincial guidelines with substantial utilization. We reviewed the latest national and provincial clinical guidelines, retrospective cohort studies, and case series regarding the treatment of COVID-19 by add-on Chinese medicine. We have also reviewed the clinical evidence generated from SARS and H1N1 management with hypothesized mechanisms and latest in silico findings to identify candidate Chinese medicines for the consideration of possible trials and management. Given the paucity of strongly evidence-based regimens, the available data suggest that Chinese medicine could be considered as an adjunctive therapeutic option in the management of COVID-19.

Fernando Soto, Jie Wang, Rajib Ahmed et al.

Advances in medical robots promise to improve modern medicine and the quality of life. Miniaturization of these robotic platforms has led to numerous applications that leverages precision medicine. In this review, the current trends of medical micro and nanorobotics for therapy, surgery, diagnosis, and medical imaging are discussed. The use of micro and nanorobots in precision medicine still faces technical, regulatory, and market challenges for their widespread use in clinical settings. Nevertheless, recent translations from proof of concept to in vivo studies demonstrate their potential toward precision medicine.

K. N. Vokinger, S. Feuerriegel, A. Kesselheim

Several sources of bias can affect the performance of machine learning systems used in medicine and potentially impact clinical care. Here, we discuss solutions to mitigate bias across the different development steps of machine learning-based systems for medical applications. Vokinger et al. discuss potential sources of bias in machine learning systems used in medicine. The authors propose solutions to mitigate bias across the different stages of model development, from data collection and preparation to model evaluation and application.