Background:From traditional open surgery to laparoscopic surgery and robotassisted surgery, advances in robotics, machine learning, and imaging are pushing the surgical approach to-wards better clinical outcomes. Pre-clinical and clinical evidence suggests that automation may standardise techniques, increase efficiency, and reduce clinical complications.Methods: A PRISMA-guided search was conducted across PubMed and OVID.Results: Of the 89 screened articles, 51 met the inclusion criteria, with 10 included in the final review. Automatic data segmentation, trajectory planning, intraoperative registration, trajectory drilling, and soft tissue robotic surgery were discussed. Conclusion:Although automated surgical systems remain conceptual, several research groups have developed supervised autonomous robotic surgical systems with increasing consideration for ethico-legal issues for automation. Automation paves the way for precision surgery and improved safety and opens new possibilities for deploying more robust artificial intelligence models, better imaging modalities and robotics to improve clinical outcomes.

Electroencephalography (EEG) is one of the most widely used brain-activity recording methods in non-invasive brain-machine interfaces (BCIs). However, EEG data is highly nonlinear, and its datasets often suffer from issues such as data heterogeneity, label uncertainty and data/label scarcity. To address these, we propose a domain independent, end-to-end semi-supervised learning framework with contrastive learning and adversarial training strategies. Our method was evaluated in experiments with different amounts of labels and an ablation study in a motor imagery EEG dataset. The experiments demonstrate that the proposed framework with two different backbone deep neural networks show improved performance over their supervised counterparts under the same condition.

The integration of machine/deep learning and sensing technologies is transforming healthcare and medical practice. However, inherent limitations in healthcare data, namely scarcity, quality, and heterogeneity, hinder the effectiveness of supervised learning techniques which are mainly based on pure statistical fitting between data and labels. In this paper, we first identify the challenges present in machine learning for pervasive healthcare and we then review the current trends beyond fully supervised learning that are developed to address these three issues. Rooted in the inherent drawbacks of empirical risk minimization that underpins pure fully supervised learning, this survey summarizes seven key lines of learning strategies, to promote the generalization performance for real-world deployment. In addition, we point out several directions that are emerging and promising in this area, to develop data-efficient, scalable, and trustworthy computational models, and to leverage multi-modality and multi-source sensing informatics, for pervasive healthcare.

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