This paper presents a sub–0.1-mm ³ , fully integrated, injectable ultrasonic wireless device for biomedical temperature sensing.

Diseases not only manifest as internal structural and functional abnormalities, but also have facial characteristics and appearance deformities. Specific facial phenotypes are potential diagnostic markers, especially for endocrine and metabolic syndromes, genetic disorders, facial neuromuscular diseases, etc. The technology of facial recognition (FR) has been developed for more than a half century, but research in automated identification applied in clinical medicine has exploded only in the last decade. Artificial-intelligence-based FR has been found to have superior performance in diagnosis of diseases. This interdisciplinary field is promising for the optimization of the screening and diagnosis process and assisting in clinical evaluation and decision-making. However, only a few instances have been translated to practical use, and there is need of an overview for integration and future perspectives. This review mainly focuses on the leading edge of technology and applications in varieties of disease, and discusses implications for further exploration.

Accurate monitoring of physiological temperature is important for many biomedical applications, including monitoring of core body temperature, detecting tissue pathologies, and evaluating surgical procedures involving thermal treatment such as hyperthermia therapy and tissue ablation. Many of these applications can benefit from replacing external temperature probes with injectable wireless devices. Here we present such a device for real-time in vivo temperature monitoring that relies on“chip-as-system” integration. With an on-chip piezoelectric transducer and measuring only 380 μm × 300 μm × 570 μm, the 0.065-mm3 monolithic device, in the form of a mote, harvests ultrasound energy for power and transmits temperature data through acoustic backscattering. Containing a low-power temperature sensor implemented with a subthreshold oscillator and consuming 0.813 nW at 37 °C, the mote achieves line sensitivity of 0.088 °C/V, temperature error of +0.22/-0.28 °C, and a resolution of 0.0078 °C rms. A long-term measurement with the mote reveals an Allan deviation floor of <138.6 ppm, indicating the feasibility of using the mote for continuous physiological temperature monitoring.

Background: Systematic review is an indispensable tool for optimal evidence collection and evaluation in evidence-based medicine. However, the explosive increase of the original literatures makes it difficult to accomplish critical appraisal and regular update. Artificial intelligence (AI) algorithms have been applied to automate the literature screening procedure in medical systematic reviews. In these studies, different algorithms were used and results with great variance were reported. It is therefore imperative to systematically review and analyse the developed automatic methods for literature screening and their effectiveness reported in current studies. Methods: An electronic search will be conducted using PubMed, Embase, ACM Digital Library, and IEEE Xplore Digital Library databases, as well as literatures found through supplementary search in Google scholar, on automatic methods for literature screening in systematic reviews. Two reviewers will independently conduct the primary screening of the articles and data extraction, in which nonconformities will be solved by discussion with a methodologist. Data will be extracted from eligible studies, including the basic characteristics of study, the information of training set and validation set, and the function and performance of AI algorithms, and summarised in a table. The risk of bias and applicability of the eligible studies will be assessed by the two reviewers independently based on Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). Quantitative analyses, if appropriate, will also be performed. Discussion: Automating systematic review process is of great help in reducing workload in evidence-based practice. Results from this systematic review will provide essential summary of the current development of AI algorithms for automatic literature screening in medical evidence synthesis and help to inspire further studies in this field. Systematic review registration: PROSPERO CRD42020170815 (28 April 2020).