Derek Leben, in his new book AI Fairness: Designing Equal Opportunity Algorithms (MIT Press, 2025), tackles this pressing issue, offering a philosophical framework to evaluate and mitigate the inherent biases of AI. Leben draws inspiration from the work of the philosopher John Rawls, proposing a theory of algorithmic justice built upon core principles including autonomy, equal treatment, and equal impact. These principles, he argues, should guide the design and deployment of AI systems, ensuring they meet a "minimally acceptable level of accuracy," avoid irrelevant attributes, and provide equal opportunity.

The NextGeneration EU-backed partnership ENABLE- 6G, dedicated to unlocking the potential of next-generation mobile networks, has officially concluded, marking a significant milestone in Europe’s journey towards 6G. Launched with the ambition of exploring and advancing technologies for future mobile connectivity, ENABLE- 6G brought together leading institutions and experts to develop critical capabilities in low-power communication, sensing, reconfigurable intelligent surfaces (RIS), and privacy-preserving machine learning.

With the increasing use of artificial intelligence (AI) in diagnostics, AI algorithms have shown great potential in aiding diagnostics. As more of these algorithms are developed, there is overwhelming enthusiasm for implementing digital and artificial intelligence-based pathology (DAIP), but doubts and pitfalls are also emerging. However, few original or review articles address the limitations and practical aspects of implementing DAIP. In this review, we briefly examine the evidence related to the benefits and pitfalls of DAIP implementation and argue that DAIP is not suitable for every clinical laboratory