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Abstract Background The large language model ChatGPT can now accept image input with the GPT4-vision (GPT4V) version. We aimed to compare the performance of GPT4V to pretrained U-Net and vision transformer (ViT) models for the identification of the progression of multiple sclerosis (MS) on magnetic resonance imaging (MRI). Methods Paired coregistered MR images with and without progression were provided as input to ChatGPT4V in a zero-shot experiment to identify radiologic progression. Its performance was compared to pretrained U-Net and ViT models. Accuracy was the primary evaluation metric and 95% confidence interval (CIs) were calculated by bootstrapping. We included 170 patients with MS (50 males, 120 females), aged 21–74 years (mean 42.3), imaged at a single institution from 2019 to 2021, each with 2–5 MRI studies (496 in total). Results One hundred seventy patients were included, 110 for training, 30 for tuning, and 30 for testing; 100 unseen paired images were randomly selected from the test set for evaluation. Both U-Net and ViT had 94% (95% CI: 89–98%) accuracy while GPT4V had 85% (77–91%). GPT4V gave cautious nonanswers in six cases. GPT4V had precision (specificity), recall (sensitivity), and F1 score of 89% (75–93%), 92% (82–98%), 91 (82–97%) compared to 100% (100–100%), 88 (78–96%), and 0.94 (88–98%) for U-Net and 94% (87–100%), 94 (88–100%), and 94 (89–98%) for ViT. Conclusion The performance of GPT4V combined with its accessibility suggests has the potential to impact AI radiology research. However, misclassified cases and overly cautious non-answers confirm that it is not yet ready for clinical use. Relevance statement GPT4V can identify the radiologic progression of MS in a simplified experimental setting. However, GPT4V is not a medical device, and its widespread availability highlights the need for caution and education for lay users, especially those with limited access to expert healthcare. Key Points Without fine-tuning or the need for prior coding experience, GPT4V can perform a zero-shot radiologic change detection task with reasonable accuracy. However, in absolute terms, in a simplified “spot the difference” medical imaging task, GPT4V was inferior to state-of-the-art computer vision methods. GPT4V’s performance metrics were more similar to the ViT than the U-net. This is an exploratory experimental study and GPT4V is not intended for use as a medical device. Graphical Abstract

Abstract Few metrics exist to describe phenotypic diversity within ophthalmic imaging datasets, with researchers often using ethnicity as a surrogate marker for biological variability. We derived a continuous, measured metric, the retinal pigment score (RPS), that quantifies the degree of pigmentation from a colour fundus photograph of the eye. RPS was validated using two large epidemiological studies with demographic and genetic data (UK Biobank and EPIC-Norfolk Study) and reproduced in a Tanzanian, an Australian, and a Chinese dataset. A genome-wide association study (GWAS) of RPS from UK Biobank identified 20 loci with known associations with skin, iris and hair pigmentation, of which eight were replicated in the EPIC-Norfolk cohort. There was a strong association between RPS and ethnicity, however, there was substantial overlap between each ethnicity and the respective distributions of RPS scores. RPS decouples traditional demographic variables from clinical imaging characteristics. RPS may serve as a useful metric to quantify the diversity of the training, validation, and testing datasets used in the development of AI algorithms to ensure adequate inclusion and explainability of the model performance, critical in evaluating all currently deployed AI models. The code to derive RPS is publicly available at: https://github.com/uw-biomedical-ml/retinal-pigmentation-score .

Plants display a range of temporal patterns of inter-annual reproduction, from relatively constant seed production to mast seeding, the synchronized and highly variable interannual seed production of plants within a population. Previous efforts have compiled global records of seed production in long-lived plants to gain insight into seed production, forest and animal population dynamics, and the effects of global change on masting. Existing datasets focus on seed production dynamics at the population scale but are limited in their ability to examine community-level mast seeding dynamics across different plant species at the continental scale. We harmonized decades of plant reproduction data for 141 woody plant species across nine Long-Term Ecological Research (LTER) or long-term ecological monitoring sites from a wide range of habitats across the United States. Plant reproduction data are reported annually between 1957 and 2021 and based on either seed traps or seed and/or cone counts on individual trees. A wide range of woody plant species including trees, shrubs, and lianas are represented within sites allowing for direct community-level comparisons among species. We share code for filtering of data that enables the comparison of plot and individual tree data across sites. For each species, we compiled relevant life history attributes (e.g., seed mass, dispersal syndrome, seed longevity, sexual system) that may serve as important predictors of mast seeding in future analyses. To aid in phylogenetically informed analyses, we also share a phylogeny and phylogenetic distance matrix for all species in the dataset. These data can be used to investigate continent-scale ecological properties of seed production, including individual and population variability, synchrony within and across species, and how these properties of seed production vary in relation to plant species traits and environmental conditions. In addition, these data can be used to assess how annual variability in seed production is associated with climate conditions and how that varies across populations, species, and regions. The dataset is released under a CC0 1.0 Universal public domain license.

Care-experienced young people face significant levels of mental distress yet the nature and lived reality of this is poorly understood and undermines the degree to which professional caregivers can provide effective support. This is exacerbated by the lack of 'voice' and control care-experienced young people have as active producers of knowledge through research. Using a small-scale, knowledge exchange project, we sought to address these concerns by employing coproduction and participatory action research with care-experienced young people to articulate their experiences of mental distress and the kind of supports they need. Their insights were used to develop and co-deliver an online training programme to professional care givers. An arts-based methodology involving sound was utilised to enhance learning and emotional connection. The findings illustrate that: professional caregivers lack knowledge about mental health and need adequate training to support the mental health of care-experienced young people; communication and relationship-building skills are central to improving mental wellbeing; systemic change is also required; and hearing directly from young people in mental health training and amplifying their voice through the creative use of sound increases impact. These findings endorse creative, coproduced training as a valuable means of effecting the type of culture and systemic change required to significantly improve the support for care-experienced young people's mental health and wellbeing. [ABSTRACT FROM AUTHOR]

Background: The large language model ChatGPT can now accept image input with the GPT4-vision (GPT4V) version. We aimed to compare the performance of GPT4V to pretrained U-Net and vision transformer (ViT) models for the identification of the progression of multiple sclerosis (MS) on magnetic resonance imaging (MRI). Methods: Paired coregistered MR images with and without progression were provided as input to ChatGPT4V in a zero-shot experiment to identify radiologic progression. Its performance was compared to pretrained U-Net and ViT models. Accuracy was the primary evaluation metric and 95% confidence interval (CIs) were calculated by bootstrapping. We included 170 patients with MS (50 males, 120 females), aged 21–74 years (mean 42.3), imaged at a single institution from 2019 to 2021, each with 2–5 MRI studies (496 in total). Results: One hundred seventy patients were included, 110 for training, 30 for tuning, and 30 for testing; 100 unseen paired images were randomly selected from the test set for evaluation. Both U-Net and ViT had 94% (95% CI: 89–98%) accuracy while GPT4V had 85% (77–91%). GPT4V gave cautious nonanswers in six cases. GPT4V had precision (specificity), recall (sensitivity), and F1 score of 89% (75–93%), 92% (82–98%), 91 (82–97%) compared to 100% (100–100%), 88 (78–96%), and 0.94 (88–98%) for U-Net and 94% (87–100%), 94 (88–100%), and 94 (89–98%) for ViT. Conclusion: The performance of GPT4V combined with its accessibility suggests has the potential to impact AI radiology research. However, misclassified cases and overly cautious non-answers confirm that it is not yet ready for clinical use. Relevance statement: GPT4V can identify the radiologic progression of MS in a simplified experimental setting. However, GPT4V is not a medical device, and its widespread availability highlights the need for caution and education for lay users, especially those with limited access to expert healthcare. Key Points: Without fine-tuning or the need for prior coding experience, GPT4V can perform a zero-shot radiologic change detection task with reasonable accuracy. However, in absolute terms, in a simplified "spot the difference" medical imaging task, GPT4V was inferior to state-of-the-art computer vision methods. GPT4V's performance metrics were more similar to the ViT than the U-net. This is an exploratory experimental study and GPT4V is not intended for use as a medical device. [ABSTRACT FROM AUTHOR]

This study looks at the case of industrial energy transition campaigning by a traditional conservation group in a regional city that hosts coal- fired power and emissions-intensive industry. We ask, what tensions do metropolitan campaigners find when communicating the idea of just transition and building alliances in regional places? How do they deal with those tensions? What does this mean for the task of developing 'useable useful theories of environmental justice' ? Our results illustrate the need to navigate three principal tensions: inclusivity, recognition, and equity. The case of the Australian Conservation Foundation in Gladstone reveals that as these tensions get worked through, the campaign goal is reframed as something other than 'just transition' and more akin to a local environmental justice campaigning. Ultimately, the ENGO now wrestles with the local difficulties of building energy justice politics on the scale and time-frame that climate science implies. [ABSTRACT FROM AUTHOR]

RETFound is a retinal image-based foundational artificial intelligence (AI) model that can be fine-tuned to downstream tasks. However, its generalizability to Asian populations remains unclear. In this study, we fine-tuned RETFound on an Asian-specific dataset. We then evaluated the performance of RETFound versus a conventional Vision Transformer model (pretrained on ImageNet) in diagnosing glaucoma and coronary heart disease and predicting the 3-year risk of stroke in an Asian population. When fine-tuned on a "full" dataset, RETFound showed no significant improvement compared with a conventional Vision Transformer model (area under the curves [AUCs] of 0.863, 0.628, and 0.557 vs. 0.853, 0.621, and 0.543, respectively; all P=0.2). Furthermore, in scenarios with limited training data (finetuned on =25% of the full dataset), RETFound showed a slight advantage (up to a maximum AUC increase of 0.03). However, these improvements were not statistically significant (all P=0.2). These findings indicate the challenges foundational AI models face in adapting to diverse demographics, emphasizing the need for more diverse data in current foundation models and the importance of global collaboration on foundation model research. [ABSTRACT FROM AUTHOR]

To determine whether oral micronutrient supplementation slows geographic atrophy (GA) progression in age-related macular degeneration (AMD). Post hoc analysis of Age-Related Eye Disease Study (AREDS) and AREDS2, multicenter randomized placebo-controlled trials of oral micronutrient supplementation, each with 2 × 2 factorial design. A total of 392 eyes (318 participants) with GA in AREDS and 1210 eyes (891 participants) with GA in AREDS2. The AREDS participants were randomly assigned to oral antioxidants (500 mg vitamin C, 400 IU vitamin E, 15 mg β-carotene), 80 mg zinc, combination, or placebo. The AREDS2 participants were randomly assigned to 10 mg lutein/2 mg zeaxanthin, 350 mg docosahexaenoic acid/650 mg eicosapentaenoic acid, combination, or placebo. Consenting AREDS2 participants were also randomly assigned to alternative AREDS formulations: original; no beta-carotene; 25 mg zinc instead of 80 mg; both. (1) Change in GA proximity to central macula over time and (2) change in square root GA area over time, each measured from color fundus photographs at annual visits and analyzed by mixed-model regression according to randomized assignments. In AREDS eyes with noncentral GA (n = 208), proximity-based progression toward the central macula was significantly slower with randomization to antioxidants versus none, at 50.7 μm/year (95% confidence interval [CI], 38.0–63.4 μm/year) versus 72.9 μm/year (95% CI, 61.3–84.5 μm/year; P = 0.012), respectively. In AREDS2 eyes with noncentral GA, in participants assigned to AREDS antioxidants without β-carotene (n = 325 eyes), proximity-based progression was significantly slower with randomization to lutein/zeaxanthin versus none, at 80.1 μm/year (95% CI, 60.9–99.3 μm/year) versus 114.4 μm/year (95% CI, 96.2–132.7 μm/year; P = 0.011), respectively. In AREDS eyes with any GA (n = 392), area-based progression was not significantly different with randomization to antioxidants versus none (P = 0.63). In AREDS2 eyes with any GA, in participants assigned to AREDS antioxidants without β-carotene (n = 505 eyes), area-based progression was not significantly different with randomization to lutein/zeaxanthin versus none (P = 0.64). Oral micronutrient supplementation slowed GA progression toward the central macula, likely by augmenting the natural phenomenon of foveal sparing. Proprietary or commercial disclosure may be found after the references. [ABSTRACT FROM AUTHOR]