Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 3 appraisals
Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Levels of analysis for large language models
Modern artificial intelligence systems, such as large language models, are increasingly powerful but also increasingly hard to understand. Recognizing this problem as analogous to the historical difficulties in understanding the human mind, we argue that methods developed in cognitive science can be useful for understanding large language models. We propose a framework for applying these methods based on the levels of analysis that David Marr proposed for studying information processing systems. By revisiting established cognitive science techniques relevant to each level and illustrating their potential to yield insights into the behaviour and internal organization of large language models, we aim to provide a toolkit for making sense of these new kinds of minds. This article is part of the theme issue 'World models in natural and artificial intelligence'.
15 May 2026
Read appraisal →Diagnostic and interventional radiology (Ankara, Turkey)
Foundation models for radiology: fundamentals, applications, opportunities, challenges, risks, and prospects.
Foundation models (FMs) represent a significant evolution in artificial intelligence (AI), impacting diverse fields. Within radiology, this evolution offers greater adaptability, multimodal integration, and improved generalizability compared with traditional narrow AI. Utilizing large-scale pre-training and efficient fine-tuning, FMs can support diverse applications, including image interpretation, report generation, integrative diagnostics combining imaging with clinical/laboratory data, and synthetic data creation, holding significant promise for advancements in precision medicine. However, clinical translation of FMs faces several substantial challenges. Key concerns include the inherent opacity of model decision-making processes, environmental and social sustainability issues, risks to data privacy, complex ethical considerations, such as bias and fairness, and navigating the uncertainty of regulatory frameworks. Moreover, rigorous validation is essential to address inherent stochasticity and the risk of hallucination. This international collaborative effort provides a comprehensive overview of the fundamentals, applications, opportunities, challenges, and prospects of FMs, aiming to guide their responsible and effective adoption in radiology and healthcare.
10 May 2026
Read appraisal →Neural networks : the official journal of the International Neural Network Society
Alleviating noise memorization for adversarially robust few-shot learning
Few-Shot Learning (FSL) enables models to learn from just a few examples of new classes by leveraging knowledge from base classes. While FSL has made significant strides, its vulnerability to adversarial attacks-especially with limited data-has been overlooked. To address this, adversarial training is often used to build more robust models. However, we found that this approach can lead the model to memorize adversarial noise, which harms its ability to generalize. Hard labels exacerbate this issue by pushing the model toward perfect accuracy on adversarial examples, while also making it less robust to small changes in weights. To solve these problems, we propose Alleviation of Noise Memorization (ANM), a method that includes Adaptive Label Smoothing for more flexible supervision and Robust Weight Learning to enhance model stability. Our extensive experiments show that ANM effectively reduces noise memorization and improves generalization, outperforming current benchmarks.
6 May 2026
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