Physiological signals obtained from electroencephalography (EEG), electromyography (EMG), and electrocardiography (ECG) provide valuable clinical information but pose challenges for analysis due to their high-dimensional nature. Traditional machine learning techniques, relying on hand-crafted features from fixed analysis windows, can lead to the loss of discriminative information. Recent studies have demonstrated the effectiveness of deep convolutional neural networks (CNNs) for robust automated feature learning from raw physiological signals. However, standard CNN architectures require two-dimensional image data as input. This has motivated research into innovative signal-to-image (STI) transformation techniques to convert one-dimensional time series into images preserving spectral, spatial, and temporal characteristics. This paper reviews recent advances in strategies for physiological signal-to-image conversion and their applications using CNNs for automated processing tasks. A systematic analysis of EEG, EMG, and ECG signal transformation and CNN-based analysis techniques spanning diverse applications, including brain-computer interfaces, seizure detection, motor control, sleep stage classification, arrhythmia detection, and more, are presented. Key insights are synthesized regarding the relative merits of different transformation approaches, CNN model architectures, training procedures, and benchmark performance. Current challenges and promising research directions at the intersection of deep learning and physiological signal processing are discussed. This review aims to catalyze continued innovations in effective end-to-end systems for clinically relevant information extraction from multidimensional physiological data using convolutional neural networks by providing a comprehensive overview of state-of-the-art techniques.

This narrative review evaluates the evolution and clinical impact of robotic-assisted surgery across the principal surgical specialties, including urology, cardiothoracic surgery, neurology, gynaecology, orthopaedics, and gastrointestinal surgery. PubMed, Scopus, Web of Science, IEEE Xplore, and Google Scholar were searched for English-language publications from January 2000 to August 2026. The retrieved literature was synthesised narratively by specialty; no standardised quality appraisal, risk-of-bias assessment, meta-analysis, or certainty-of-evidence grading was undertaken, and all quantitative estimates are reported as published by the cited sources. Studies that met the inclusion criteria were analysed across procedure-specific robotic techniques, clinical and technical outcomes, and specialty-specific applications. The findings demonstrate substantial but uneven advancement across specialties. In urology, robotic-assisted laparoscopic prostatectomy reduced intraoperative blood loss by 60–70% relative to open surgery and shortened hospital stay, without demonstrated oncological superiority. National registry data from the United Kingdom (2024), France (2022), Taiwan (2011–2024), and China (2024) report urology at 43–61% of national robotic procedure volume, in datasets that differ in scope, denominator, and observation period, with general surgery dominant in United States da Vinci platform volume. In neurology, a 2024 meta-analysis of 15 studies (732 patients) reported a pooled vector error of 1.09 mm for robot-assisted deep brain stimulation. In orthopaedics, 2023–2025 meta-analyses confirm improved mechanical alignment in robotic total knee arthroplasty but equivalent functional outcomes and implant survivorship. In gastrointestinal surgery, a meta-analysis of 72 studies (30,081 patients) reported lower conversion, reduced blood loss, and improved overall survival for robotic gastrectomy at the cost of longer operative time. Across specialties, robotic assistance most consistently reduces blood loss and, in anatomically demanding fields, conversion to open surgery, and improves ergonomics, although the size of these benefits varies by procedure; however, longer operative times, higher costs, and the absence of haptic feedback remain unresolved, and the evidence base for long-term oncological and functional benefit is still maturing.