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Article summary:
1. This article explores two medical decision-making tasks, namely COVID-19 detection and lung area segmentation detection, using chest radiography images.
2. It examines different machine learning techniques such as federated learning, semi-supervised learning, transfer learning, and multi-task learning to explore the issue.
3. Results show that Raspberry Pi-centric devices provide better performance in lung segmentation detection, and server-centric experiments provide better results in COVID-19 detection.
Article analysis:
The article is generally reliable and trustworthy due to its use of evidence from research studies and its clear explanation of the methods used for the study. The authors have provided a detailed description of the methods used for the study as well as a thorough discussion of the results obtained from their experiments. Furthermore, they have discussed potential applications of IoMT systems in healthcare settings and how these systems could benefit all stakeholders involved.
However, there are some points that could be improved upon in terms of trustworthiness and reliability. For example, while the authors discuss potential applications of IoMT systems in healthcare settings, they do not provide any evidence or data to support their claims about how these systems could benefit all stakeholders involved. Additionally, while they discuss potential risks associated with IoMT systems such as privacy concerns, they do not provide any evidence or data to support their claims about how these risks can be mitigated or managed effectively. Finally, while the authors discuss different machine learning techniques such as federated learning and multi-task learning to explore the issue at hand, they do not provide any evidence or data to support their claims about which technique is more effective for this particular application.