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Background: Lung cancer is one of the most fatal cancers worldwide, and malignant tumors are characterized by the growth of abnormal cells in the tissues of lungs. Usually, symptoms of lung cancer do not appear until it is already at an advanced stage. The proper segmentation of cancerous lesions in CT images is the primary method of detection towards achieving a completely automated diagnostic system., Method: In this work, we developed an improved hybrid neural network via the fusion of two architectures, MobileNetV2 and UNET, for the semantic segmentation of malignant lung tumors from CT images. The transfer learning technique was employed and the pre-trained MobileNetV2 was utilized as an encoder of a conventional UNET model for feature extraction. The proposed network is an efficient segmentation approach that performs lightweight filtering to reduce computation and pointwise convolution for building more features. Skip connections were established with the Relu activation function for improving model convergence to connect the encoder layers of MobileNetv2 to decoder layers in UNET that allow the concatenation of feature maps with different resolutions from the encoder to decoder. Furthermore, the model was trained and fine-tuned on the training dataset acquired from the Medical Segmentation Decathlon (MSD) 2018 Challenge., Results: The proposed network was tested and evaluated on 25% of the dataset obtained from the MSD, and it achieved a dice score of 0.8793, recall of 0.8602 and precision of 0.93. It is pertinent to mention that our technique outperforms the current available networks, which have several phases of training and testing.

Wearable bioelectronics and therapeutics are a rapidly evolving area of research, with researchers exploring new materials that offer greater flexibility and sophistication. Conductive hydrogels have emerged as a promising material due to their tunable electrical properties, flexible mechanical properties, high elasticity, stretchability, excellent biocompatibility, and responsiveness to stimuli. This review presents an overview of recent breakthroughs in conductive hydrogels, including their materials, classification, and applications. By providing a comprehensive review of current research, this paper aims to equip researchers with a deeper understanding of conductive hydrogels and inspire new design approaches for various healthcare applications.

Artificial intelligence (AI) has the potential to make substantial progress toward the goal of making healthcare more personalized, predictive, preventative, and interactive. We believe AI will continue its present path and ultimately become a mature and effective tool for the healthcare sector. Besides this AI-based systems raise concerns regarding data security and privacy. Because health records are important and vulnerable, hackers often target them during data breaches. The absence of standard guidelines for the moral use of AI and ML in healthcare has only served to worsen the situation. There is debate about how far artificial intelligence (AI) may be utilized ethically in healthcare settings since there are no universal guidelines for its use. Therefore, maintaining the confidentiality of medical records is crucial. This study enlightens the possible drawbacks of AI in the implementation of healthcare sector and their solutions to overcome these situations., Competing Interests: Conflict of interestAuthors have no conflict of interest., (© The Author(s), under exclusive licence to Springer Science+Business Media, LLC 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)