Related references
Note: Only part of the references are listed.
Letter
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(2023)
Editorial Material
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Summary: This Viewpoint explores the potential advantages and disadvantages of implementing artificial intelligence in surgery, specifically focusing on computer vision, digital transformation at the point of care, and electronic health records data.
Letter
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Review
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Tyler J. Loftus et al.
Summary: This study summarizes the state-of-the-art artificial intelligence-enabled decision support in surgery and quantifies deficiencies in scientific rigor and reporting. The results show that these models are limited by reliance on internal validation, small sample sizes, and failure to report confidence intervals and clinical implementation.
Article
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Kavya Pathak et al.
Summary: Home hospital is a care delivery model that provides hospital-grade care to patients in their homes, and it has become increasingly common in medical settings. Although surgical uptake has been limited, home hospital programs have shown to be safe and effective in various medical contexts, with increased usage during the COVID-19 pandemic. Surgical Home Hospital (SHH) programs may have important benefits for surgical patients, and various technologies, such as risk prediction models and remote patient monitoring platforms, can enable the delivery of hospital care in the home. The importance of data interoperability, access for all patients, and clinical workflow design are also highlighted in implementing SHH programs.
NPJ DIGITAL MEDICINE
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Summary: The adoption and implementation of digital health tools in large, complex health systems is a challenge. This article proposes nine dimensions for evaluating clinically validated digital health tools and suggests strategies for selecting and planning implementation in this setting.
NPJ DIGITAL MEDICINE
(2022)
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Benjamin J. Mandl et al.
Summary: During the COVID-19 pandemic, effective public communication by leaders is crucial for guiding public responses. In analyzing over 1500 pandemic-related speeches delivered by all 50 US state governors during the initial months, we found that as COVID-19 cases increased, governors used stricter language, employed negation to defend actions and highlight uncertainty, and used more extreme adjectives. Furthermore, as cases reached their highest levels, governors used shorter words with fewer syllables.
NPJ DIGITAL MEDICINE
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Joao Jorge et al.
Summary: This study aimed to evaluate the feasibility of introducing a non-contact video camera monitoring system into an acute clinical setting. The results showed that non-contact monitoring accurately assessed heart rate and respiratory rate, detected changes in vital signs, and helped track early signs of physiological deterioration during post-operative care.
NPJ DIGITAL MEDICINE
(2022)
Review
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Ben Li et al.
Summary: This study conducted a systematic review and critical appraisal of machine learning applications in vascular surgery. The results showed that machine learning techniques are widely used in this field for diagnosis, prognosis, and image segmentation. However, most studies were retrospective and single center, and the quality of the studies and adherence to reporting standards were found to be inadequate.
NPJ DIGITAL MEDICINE
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Article
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Yuanfang Ren et al.
Summary: In this study, automated real-time predictions of postoperative complications with mobile device outputs had good performance in clinical settings with prospective validation, matching surgeons' predictive accuracy.
Article
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Recai Yilmaz et al.
Summary: Technical ability plays a crucial role in procedural-based medicine, and continuous assessment of psychomotor performance is essential. This study introduces a deep learning application, the Intelligent Continuous Expertise Monitoring System (ICEMS), which can assess surgical bimanual performance with high frequency intervals and successfully differentiate between different levels of trainees.
NPJ DIGITAL MEDICINE
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NPJ DIGITAL MEDICINE
(2022)
Review
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Kyle Lam et al.
Summary: This study systematically reviewed the literature on the use of machine learning (ML) for surgical skill assessment and found that Hidden Markov Models (HMM), Support Vector Machines (SVM), and Artificial Neural Networks (ANN) were the most commonly used ML methods. Some studies used kinematic data, video or image data for assessment, and evaluated benchtop tasks, simulator tasks, and real-life surgery. Although there were variations between studies, accuracy rates of over 80% were achieved. Barriers to progress in the field included a focus on basic tasks, lack of standardization between studies, and lack of datasets. Future research should go beyond the assessment of basic tasks, focus on real-life surgery, and provide interpretable feedback with clinical value for surgeons.
NPJ DIGITAL MEDICINE
(2022)
Article
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Makoto Mori et al.
Summary: Understanding postoperative recovery is crucial for improving post-acute phase care. This study utilized a digital platform to measure recovery after cardiac surgery and found that postoperative complications may impact overall recovery, while slow progress in other domains of recovery does not necessarily indicate a lack of overall progress.
NPJ DIGITAL MEDICINE
(2022)
Article
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Runzhuo Ma et al.
Summary: The performance of a surgery has a significant impact on patient outcomes, but objective quantification of performance has been a challenge. Researchers identified specific gestures in a robot-assisted prostatectomy and found that certain gestures were associated with better 1-year erectile function recovery. They also developed machine learning models using these gesture sequences to predict erectile function recovery.
NPJ DIGITAL MEDICINE
(2022)
Article
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Carlo Biffi et al.
Summary: In this study, a novel intelligent medical device was developed to operate seamlessly in real-time using conventional white light endoscopy video stream without the need for virtual chromoendoscopy. The device has the potential to support non-expert endoscopists in systematically reaching the performance level of expert endoscopists in optical characterization.
NPJ DIGITAL MEDICINE
(2022)
Editorial Material
Health Care Sciences & Services
Marium M. Raza et al.
Summary: Innovations in robotics, virtual and augmented reality, and artificial intelligence are rapidly being adopted in the field of digital surgery. However, the ethical issues associated with digital surgery, such as privacy, consent, and litigation, are not well understood. A recent study defines the term digital surgery and highlights the specific ethical concerns in this field.
NPJ DIGITAL MEDICINE
(2022)
Article
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John Halamka et al.
Summary: Discrimination against population subgroups hinders their ability to receive optimal surgical care. Artificial intelligence algorithms can potentially address this issue by detecting bias in current medical decision-making datasets, but further investment and development are needed.
NPJ DIGITAL MEDICINE
(2022)
Review
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Pietro Mascagni et al.
Summary: Computer vision, the application of algorithms to analyze and interpret visual data, has become a critical technology in studying the intraoperative phase of care, supporting safer surgery, and expanding access to surgical care. However, there are currently no widely used computer vision tools for diagnostic or therapeutic applications in surgery.
NPJ DIGITAL MEDICINE
(2022)
Editorial Material
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O'Brien Niki et al.
Summary: Cyber-attacks on healthcare institutions have increased, impacting staff wellbeing. This commentary highlights the importance of improving cybersecurity education for all healthcare staff through online resources and simulation.
Article
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Summary: The study utilized artificial intelligence technology to design and validate a nonlinear risk calculator for trauma patients, indicating that risk modeling is not linear. By using the Optimal Classification Trees algorithm, the Trauma Outcome Predictor application was developed and accurately predicted mortality and complications in trauma patients.
JOURNAL OF TRAUMA AND ACUTE CARE SURGERY
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Review
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Qian Zhou et al.
Summary: The study found that the evidence of the impact of TS, ML, and DL tools in clinical practice was limited, with DL applications not yet fully spread in medicine. In the future, DL may integrate more complex clinical problems than ML and TS tools. Rigorous studies are required before the clinical application of these tools.
NPJ DIGITAL MEDICINE
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Article
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Kenneth A. McLean et al.
Summary: Smartphone-delivered wound assessment tool may expedite diagnosis of SSI after emergency abdominal surgery, with higher odds of diagnosis within 7 postoperative days for patients in the smartphone group. However, there was no significant difference in the 30-day SSI rate between trial arms. Patients using the tool had reduced community care attendance.
NPJ DIGITAL MEDICINE
(2021)
Review
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Stephen R. Knight et al.
Summary: This review highlights the suboptimal reporting quality of mobile and wearable DHI following surgery, with limited consideration for data security, patient engagement, and cost analysis. Urgent improvement in reporting is needed to unlock the potential of DHI in enhancing postoperative patient care.
NPJ DIGITAL MEDICINE
(2021)
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Chemistry, Analytical
Vini Vijayan et al.
Summary: Wearable sensor technology is increasingly utilized in a variety of applications, including monitoring patient health, assisting with disease diagnosis, and improving patient outcomes. These sensors can detect and quantify specific movements, offering advantages over traditional medical assessments that may not accurately reflect a patient's functional abilities.
Article
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Christine K. Lee et al.
Summary: The study demonstrates that by combining Generalized Additive Models with Neural Networks (GAM-NN), the model performs better in both training and testing sets, with higher area under the curve but lower average precision. This model leverages the neural network's ability to learn nonlinear patterns, is easily interpretable, and maintains model performance.
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Hyo-Eun Kim et al.
LANCET DIGITAL HEALTH
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Zachary Sanford et al.
GLOBAL SPINE JOURNAL
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Article
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Kristin M. Corey et al.
Review
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Daniel J. Riskin et al.
