Deep learning (DL), a branch of artificial intelligence (AI), has made significant strides in the medical field. It utilizes artificial neural networks (ANN) to learn from large amounts of data and extract relevant information for various tasks. DL has found applications in imaging diagnosis, clinical and drug research, disease classification and prediction, personalized therapy design, and public health monitoring. The advantages of DL over traditional data analysis methods include improved performance and automation. It also provides evidence-based clinical decision support tools to healthcare professionals.

However, DL presents challenges and limitations. One challenge is the need for quality and representative data. ANNs can fail to generalize when trained on data that does not accurately reflect the problem being addressed. In the medical field, privacy laws like the General Data Protection Regulation (GDPR) restrict the use of clinical data without patient consent. Even with consent, data must be anonymized and ethical approval obtained before use.

Federated learning (FL) offers a solution to these challenges. FL is a privacy-preserving and GDPR-compliant strategy for distributed machine learning. It allows a federation of clients to learn a model without exchanging data. This enables the utilization of vast and diverse medical data available from different sources, increasing the statistical power and generalizability of ML models while addressing privacy, security, and data governance concerns. FL has been successfully applied in various clinical fields, including imaging diagnosis, drug research, and genomics.

Although FL enables data sharing, the lack of explainability in ML models, like ANNs, is a limitation. Explainable AI (XAI) solutions provide tools to interpret and understand ML algorithms. Data type-specific solutions, such as Grad-CAM for image classification, and data type-independent solutions like LIME or NAMs, can be used to enhance interpretability.

Making ML models interpretable is a step towards Trustworthy AI, which ensures reliability and ethicality. XAI helps build robust and ethically sound AI systems.

The CADUCEO project, focused on digestive system diseases, proposes a federated platform that employs FL algorithms. This platform allows medical centers to share knowledge without compromising patient privacy. The project also introduces machine learning algorithms for automated image processing, data augmentation, and diagnosis support.

In conclusion, DL has the potential to improve medical operations in terms of efficiency and treatment quality. With FL and XAI, the challenges associated with data sharing and model interpretability can be addressed, leading to advancements in medical AI applications.

Note: The rest of the article includes details on the materials and methods used, results, functionalities, use cases, and future work.

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Deep Learning in Medical Applications: Challenges, Solutions, and ... - Fagen wasanni