The aim of this review was to collate current evidence wherein digitalisation, through the incorporation of video technology and artificial intelligence (AI), is being applied to the practice of surgery. Applications are vast, and the literature investigating the utility of surgical video and its synergy with AI has steadily increased over the last 2 decades. This type of technology is widespread in other industries, such as autonomy in transportation and manufacturing.Articles were identified primarily using the PubMed and MEDLINE databases. The MeSH terms used were "surgical education", "surgical video", "video labelling", "surgery", "surgical workflow", "telementoring", "telemedicine", "machine learning", "deep learning" and "operating room". Given the breadth of the subject and the scarcity of high-level data in certain areas, a narrative synthesis was selected over a meta-analysis or systematic review to allow for a focussed discussion of the topic.Three main themes were identified and analysed throughout this review, (1) the multifaceted utility of surgical video recording, (2) teleconferencing/telemedicine and (3) artificial intelligence in the operating room.Evidence suggests the routine collection of intraoperative data will be beneficial in the advancement of surgery, by driving standardised, evidence-based surgical care and personalised training of future surgeons. However, many barriers stand in the way of widespread implementation, necessitating close collaboration between surgeons, data scientists, medicolegal personnel and hospital policy makers. 

/pdf/evolution-of-the-digital-operating-room-the-place-of-video-3soqw91s.pdf

Evolution of the digital operating room: the place of video technology in surgery

Deep-learning CT reconstruction in clinical scans of the abdomen: a systematic review and meta-analysis

In anticipation of space exploration where astronauts are traveling away from Earth, and for longer durations with an increasing communication lag, artificial intelligence (AI) frameworks such as large language learning models (LLMs) that can be trained on Earth can provide real-time answers. This emerging technology may be helpful for acute medical emergencies, particularly in austere and distant space environments. In this manuscript, we provide an overview of generative pre-trained transformer (GPT) technology, a rapidly emerging AI technology, and implications, considerations, and limitations of such technology for space health. 

/pdf/generative-pre-trained-transformers-gpt-and-space-health-a-udmshul4.pdf

Generative Pre-Trained Transformers (GPT) and Space Health: A Potential Frontier in Astronaut Health During Exploration Missions

Surface electromyography (sEMG) is a technique for measuring and analyzing the electrical signals of muscle activity using electrodes placed on the skin’s surface. The aim of this paper was to outline the history of the development and use of surface electromyography in dentistry, to show where research and technical solutions relating to surface electromyography currently lie, and to make recommendations for further research. sEMG is a diagnostic technique that has found significant application in dentistry. The historical section discusses the evolution of sEMG methods and equipment, highlighting how technological advances have influenced the accuracy and applicability of this method in dentistry. The need for standardization of musculoskeletal testing methodology is highlighted and the needed increased technical capabilities of sEMG equipment and the ability to specify parameters (e.g., sampling rates, bandwidth). A higher sampling rate (the recommended may be 2000 Hz or higher in masticatory muscles) allows more accurate recording of changes in the signal, which is essential for accurate analysis of muscle function. Bandwidth is one of the key parameters in sEMG research. Bandwidth determines the range of frequencies effectively recorded by the sEMG system (the recommended frequency limits are usually between 20 Hz and 500 Hz in masticatory muscles). In addition, the increased technical capabilities of sEMG equipment and the ability to specify electromyographic parameters demonstrate the need for a detailed description of selected parameters in the methodological section. This is necessary to maintain the reproducibility of sEMG testing. More high-quality clinical trials are needed in the future.

Surface Electromyography in Dentistry—Past, Present and Future

Neuroendocrine neoplasms (NEN) of the small bowel are typically slow growing lesions that remain asymptomatic until reaching an advanced stage. Imaging modalities for lesion detection, staging, and follow-up in patients with known or suspected NEN include CT enterography (CTE), MR enterography (MRE), as well as PET/CT using a somatostatin receptor analog. FDG PET/CT may have a role in the evaluation of poorly differentiated NENs. Liver MRI, ideally with a hepatocyte-specific contrast agent, should be used when in the evaluation of hepatic metastases. Imaging informs decisions regarding both surgical approaches as well as systematic therapy (specifically, peptide receptor radionuclide therapy). This AJR Expert Panel Narrative Review describes the multimodality imaging features of small bowel NENs; explores the optimal imaging modalities for their diagnosis, staging, and follow-up; and discusses how imaging may be used to guide therapy. 

Imaging of Small-Bowel Neuroendocrine Neoplasms: AJR Expert Panel Narrative Review

Artificial intelligence (AI) is the most revolutionizing development in the health care industry in the current decade, with diagnostic imaging having the greatest share in such development. Machine learning and deep learning (DL) are subclasses of AI that show breakthrough performance in image analysis. They have become the state of the art in the field of image classification and recognition. Machine learning deals with the extraction of the important characteristic features from images, whereas DL uses neural networks to solve such problems with better performance. In this review, we discuss the current applications of machine learning and DL in the field of diagnostic radiology.Deep learning applications can be divided into medical imaging analysis and applications beyond analysis. In the field of medical imaging analysis, deep convolutional neural networks are used for image classification, lesion detection, and segmentation. Also used are recurrent neural networks when extracting information from electronic medical records and to augment the use of convolutional neural networks in the field of image classification. Generative adversarial networks have been explicitly used in generating high-resolution computed tomography and magnetic resonance images and to map computed tomography images from the corresponding magnetic resonance imaging. Beyond image analysis, DL can be used for quality control, workflow organization, and reporting.In this article, we review the most current AI models used in medical imaging research, providing a brief explanation of the various models described in the literature within the past 5 years. Emphasis is placed on the various DL models, as they are the most state-of-art in imaging analysis. 

Artificial Intelligence in Diagnostic Radiology: Where Do We Stand, Challenges, and Opportunities

Routine computed tomography (CT) scans are thought to have poor performance for detection of gastrointestinal (GI) neuroendocrine neoplasms (NENs), which leads to delayed workup. Detection of even 1 bowel tumor can guide diagnostic workup and management. The purposes of this study were to assess the accuracy of multidetector computed tomography (MDCT) and to compare negative versus positive enteric contrast in detecting at least 1 GI tumor per patient with suspected or confirmed diagnosis of a NEN.This retrospective study included 107 patients with intravenous and oral contrast (65 positive, 40 negative, and 2 no oral contrast) abdominopelvic MDCT. Two abdominal radiologists independently analyzed the CTs for detection and localization of bowel NENs. Surgical pathology was considered the reference standard. Analyses included κ and summary statistics, McNemar test, Pearson χ2 test, and Fisher exact test.Among the 107 CT scans, there were 30 pathology negative studies and 77 studies with positive pathology for GI NEN. Interreader agreement for CT evaluation was substantial (κ = 0.61). At least 1 GI NEN per patient was detected with 51% to 53% sensitivity, 87% to 93% specificity, 91% to 95% positive predictive value (PPV), 42% negative predictive value, and 63% accuracy for each reader, and 57% accuracy when only the concordant (ie, matching) results of the 2 readers were considered. Computed tomography scans with negative enteric contrast had significantly higher sensitivity for concordant results than CTs with positive enteric contrast (58% vs 30%, P = 0.01). Specificity (100% vs 95%, P = 0.5), PPV (100% vs 93%, P = 0.49), negative predictive value (39% vs 39%, P = 0.99), and accuracy (67% vs 51%, P = 0.10) were not significantly different for negative versus positive enteric contrast for the concordant results. There was no significant difference in GI NEN localization between the readers.Routine MDCT with either positive or negative enteric contrast can detect at least 1 GI tumor per patient with more than 90% PPV and more than 50% accuracy in patients suspected of GI NEN. Using negative enteric contrast improves sensitivity for GI NEN versus positive enteric contrast. In addition, there is high accuracy in localizing the bowel tumor with positive or negative enteric contrast, which may guide surgery. Radiologists should have heightened awareness that evaluating such scans closely may lead to detection of primary bowel NENs at a higher rate than previously reported. 

Performance of Multidetector Computed Tomography and Negative Versus Positive Enteric Contrast for Evaluation of Gastrointestinal Neuroendocrine Neoplasms

From the Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX. Correspondence to: Corey T. Jensen, MD, Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, 1400 Pressler St, Unit 1473, Houston, TX 77030-4009 (e-mail: [email protected]). C.T.J. received an in-kind grant from General Electric for the evaluation of deep learning image reconstruction in computed tomography. 

Image Quality Evaluation in Dual Energy CT of the Chest, Abdomen and Pelvis in Obese Patients with Deep Learning Image Reconstruction

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Artificial Intelligence in Diagnostic Radiology: Where Do We Stand, Challenges, and Opportunities

Performance of Multidetector Computed Tomography and Negative Versus Positive Enteric Contrast for Evaluation of Gastrointestinal Neuroendocrine Neoplasms

Image Quality Evaluation in Dual Energy CT of the Chest, Abdomen and Pelvis in Obese Patients with Deep Learning Image Reconstruction