Detecting shortcut learning for fair medical AI using shortcut testing.

Machine learning (ML) holds great promise for improving healthcare, but it is critical to ensure that its use will not propagate or amplify health disparities. An important step is to characterize the (un)fairness of ML models-their tendency to perform differently across subgroups of the population-and to understand its underlying mechanisms. One potential driver of algorithmic unfairness, shortcut learning, arises when ML models base predictions on improper correlations in the training data.

Enhancing the reliability and accuracy of AI-enabled diagnosis via complementarity-driven deferral to clinicians.

Predictive artificial intelligence (AI) systems based on deep learning have been shown to achieve expert-level identification of diseases in multiple medical imaging settings, but can make errors in cases accurately diagnosed by clinicians and vice versa. We developed Complementarity-Driven Deferral to Clinical Workflow (CoDoC), a system that can learn to decide between the opinion of a predictive AI model and a clinical workflow. CoDoC enhances accuracy relative to clinician-only or AI-only baselines in clinical workflows that screen for breast cancer or tuberculosis (TB).

Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging.

Machine-learning models for medical tasks can match or surpass the performance of clinical experts. However, in settings differing from those of the training dataset, the performance of a model can deteriorate substantially. Here we report a representation-learning strategy for machine-learning models applied to medical-imaging tasks that mitigates such 'out of distribution' performance problem and that improves model robustness and training efficiency.

Does your dermatology classifier know what it doesn't know? Detecting the long-tail of unseen conditions.

Supervised deep learning models have proven to be highly effective in classification of dermatological conditions. These models rely on the availability of abundant labeled training examples. However, in the real-world, many dermatological conditions are individually too infrequent for per-condition classification with supervised learning.

Effects of cannabidiol on brain excitation and inhibition systems; a randomised placebo-controlled single dose trial during magnetic resonance spectroscopy in adults with and without autism spectrum disorder.

There is increasing interest in the use of cannabis and its major non-intoxicating component cannabidiol (CBD) as a treatment for mental health and neurodevelopmental disorders, such as autism spectrum disorder (ASD). However, before launching large-scale clinical trials, a better understanding of the effects of CBD on brain would be desirable. Preclinical evidence suggests that one aspect of the polypharmacy of CBD is that it modulates brain excitatory glutamate and inhibitory γ-aminobutyric acid (GABA) levels, including in brain regions linked to ASD, such as the basal ganglia (BG) and the dorsomedial prefrontal cortex (DMPFC).

Typical magnitude and spatial extent of crowding in autism.

Enhanced spatial processing of local visual details has been reported in individuals with autism spectrum conditions (ASC), and crowding is postulated to be a mechanism that may produce this ability. However, evidence for atypical crowding in ASC is mixed, with some studies reporting a complete lack of crowding in autism and others reporting a typical magnitude of crowding between individuals with and without ASC. Here, we aim to disambiguate these conflicting results by testing both the magnitude and the spatial extent of crowding in individuals with ASC (N = 25) and age- and IQ-matched controls (N = 23) during an orientation discrimination task.

Reduced perceptual exclusivity during object and grating rivalry in autism.

The dynamics of binocular rivalry may be a behavioral footprint of excitatory and inhibitory neural transmission in visual cortex. Given the presence of atypical visual features in Autism Spectrum Conditions (ASC), and the growing evidence in support of the idea of an imbalance in excitatory/inhibitory neural transmission in animal and genetic models of ASC, we hypothesized that binocular rivalry might prove a simple behavioral marker of such a transmission imbalance in the autistic brain. In support of this hypothesis, we previously reported a slower rate of rivalry in ASC, driven by longer transitional states between dominant percepts.

Slower rate of binocular rivalry in autism.

An imbalance between cortical excitation and inhibition is a central component of many models of autistic neurobiology. We tested a potential behavioral footprint of this proposed imbalance using binocular rivalry, a visual phenomenon in which perceptual experience is thought to mirror the push and pull of excitatory and inhibitory cortical dynamics. In binocular rivalry, two monocularly presented images compete, leading to a percept that alternates between them.

Tunnel vision: sharper gradient of spatial attention in autism.

Enhanced perception of detail has long been regarded a hallmark of autism spectrum conditions (ASC), but its origins are unknown. Normal sensitivity on all fundamental perceptual measures-visual acuity, contrast discrimination, and flicker detection-is strongly established in the literature. If individuals with ASC do not have superior low-level vision, how is perception of detail enhanced? We argue that this apparent paradox can be resolved by considering visual attention, which is known to enhance basic visual sensitivity, resulting in greater acuity and lower contrast thresholds.