Artificial intelligence and thyroid disease management: considerations for thyroid function tests

Artificial intelligence (AI) is transforming healthcare and offers new tools in clinical research, personalized medicine, and medical diagnostics. Thyroid function tests represent an important asset for physicians in the diagnosis and monitoring of pathologies. Artificial intelligence tools can clearly assist physicians and specialists in laboratory medicine to optimize test prescription, tests interpretation, decision making, process optimization, and assay design. Our article is reviewing several of these aspects. As thyroid AI models rely on large data sets, which often requires distributed learning from multi-center contributions, this article also briefly discusses this issue

Software Architecture Design for Federated Learning Systems

The advancements in deep learning and machine learning as the subdomain of AI have been demonstrated in multiple industries. However, the requirement for data by deep machine learning models has raised data privacy concerns. For instance, the EU's General Data Protection Regulation (GDPR) stipulates a range of data protection measures, causing data hungriness issues. Furthermore, trustworthy and responsible AI have emerged as hot topics recently thanks to the new ethical, legal, social, and technological challenges brought on by the technology. All of that led to the need for decentralised machine learning approaches. Federated learning is an emerging privacy-preserving AI technique that trains models locally and formulates a global model without transferring local data externally. Being widely distributed with different components and stakeholders, federated learning requires software system design thinking and software engineering considerations. Nonetheless, the different software engineering challenges and the software architectural approaches of federated learning have not previously been conceptualised systematically in the software architecture literature. This thesis aims to address the software engineering research gap of federated learning systems and to provide system-level solutions to achieve trustworthy and responsible federated learning by design. We first report the findings of a systematic literature review on federated learning from its software engineering perspective. Based on the study, the software architecture design concerns in building federated learning systems have been largely ignored. Thus, we present a collection of architectural patterns for the design challenges of federated learning systems and a set of decision models to assist software architects in pattern selection and perform architecture validations. The evaluation results show that the approaches are feasible and useful in serving as a guideline for federated learning software architecture design. We propose FLRA, a reference architecture for federated learning systems, and adopt the FLRA as the design basis to enhance trust for federated learning software architecture. Finally, we evaluated the designed federated learning architecture. The evaluation results show that the approach is feasible to enable accountability and improve fairness. Ultimately, the proposed system-level solution can achieve trustworthy and responsible federated learning