Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 2 appraisals
Journal of medical systems
RetCond: A Conditional Diffusion Model for Self-Explanatory Multi-Class Fundus Image Classification
Vision impairment remains a major global health challenge, and early diagnosis of retinal diseases is essential to prevent avoidable vision loss. Color fundus photography is widely used for retinal assessment, but manual interpretation is time-consuming and prone to inter-clinician variability. Although deep learning has improved automated retinal disease detection, most classification models lack transparency. We introduce RetCond, a self-explanatory generative classifier designed to provide both accurate predictions and counterfactual visual explanations. RetCond is a diffusion model repurposed as a classifier whose generative capabilities enable counterfactual synthesis for built-in explanations. These counterfactual images depict the retinal changes associated with alternative diagnoses, offering insight into the model's decisions. We curated a diverse dataset of 19,565 images containing five retinal disease conditions to train and evaluate the model. Performance is assessed using standard classification metrics and by qualitatively and quantitatively evaluating the self-explanatory component. Comparisons are made against strong discriminative baselines. RetCond achieves a classification accuracy of 96.98%, comparable to RETFound (96.99%). In contrast to standard models, RetCond produces counterfactual images that highlight condition-specific retinal features, confirming its learned concepts and demonstrating its self-explanatory properties. These results show that RetCond matches state-of-the-art discriminative performance while addressing limitations of post-hoc interpretability techniques in standard deep learning models. RetCond, therefore, constitutes a contribution toward more trustworthy AI solutions in ophthalmology.
23 May 2026
Read appraisal →Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Goals and the structure of experience
Purposeful behaviour is a hallmark of natural and artificial intelligence. Its acquisition is often believed to rely on world models, comprising both descriptive (what is) and prescriptive (what is desirable) aspects that identify and evaluate state of affairs in the world, respectively. Canonical computational accounts of purposeful behaviour, such as reinforcement learning, posit distinct components of a world model comprising a state representation (descriptive aspect) and a reward function (prescriptive aspect). However, an alternative possibility, which has not yet been computationally formulated, is that these two aspects instead co-emerge interdependently from an agent's goal. Here, we describe a computational framework of goal-directed state representation in cognitive agents, in which the descriptive and prescriptive aspects of a world model co-emerge from agent-environment interaction sequences, or experiences. Drawing on Buddhist epistemology, we introduce a construct of goal-directed, or telic, states, defined as classes of goal-equivalent experience distributions. Telic states provide a parsimonious account of goal-directed learning in terms of the statistical divergence between behavioural policies and desirable experience features. We review empirical and theoretical literature supporting this novel perspective and discuss its potential to provide a unified account of behavioural, phenomenological and neural dimensions of purposeful behaviours across diverse substrates. This article is part of the theme issue 'World models in natural and artificial intelligence'.
15 May 2026
Read appraisal →