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Collaborative planning for congenital heart diseases typically involves creating physical heart models through 3D printing, which are then examined by both surgeons and cardiologists. Recent developments in mobile augmented reality (AR) technologies have presented a viable alternative, known for their ease of use and portability. However, there is still a lack of research examining the utilization of multi-user mobile AR environments to support collaborative planning for cardiovascular surgeries. We created ARCollab, an iOS AR app designed for enabling multiple surgeons and cardiologists to interact with a patient's 3D heart model in a shared environment. ARCollab enables surgeons and cardiologists to import heart models, manipulate them through gestures and collaborate with other users, eliminating the need for fabricating physical heart models. Our evaluation of ARCollab's usability and usefulness in enhancing collaboration, conducted with three cardiothoracic surgeons and two cardiologists, marks the first human evaluation of a multi-user mobile AR tool for surgical planning. ARCollab is open-source, available at https://github.com/poloclub/arcollab.

Diffusion-based generative models' impressive ability to create convincing images has garnered global attention. However, their complex internal structures and operations often pose challenges for non-experts to grasp. We introduce Diffusion Explainer, the first interactive visualization tool designed to elucidate how Stable Diffusion transforms text prompts into images. It tightly integrates a visual overview of Stable Diffusion's complex components with detailed explanations of their underlying operations. This integration enables users to fluidly transition between multiple levels of abstraction through animations and interactive elements. Offering real-time hands-on experience, Diffusion Explainer allows users to adjust Stable Diffusion's hyperparameters and prompts without the need for installation or specialized hardware. Accessible via users' web browsers, Diffusion Explainer is making significant strides in democratizing AI education, fostering broader public access. More than 7,200 users spanning 113 countries have used our open-sourced tool at https://poloclub.github.io/diffusion-explainer/. A video demo is available at https://youtu.be/MbkIADZjPnA., Comment: 4 pages, 3 figures. arXiv admin note: substantial text overlap with arXiv:2305.03509

Surgical planning for congenital heart diseases requires a collaborative approach, traditionally involving the 3D-printing of physical heart models for inspection by surgeons and cardiologists. Recent advancements in mobile augmented reality (AR) technologies have offered a promising alternative, noted for their ease-of-use and portability. Despite this progress, there remains a gap in research exploring the use of multi-user mobile AR environments for facilitating collaborative cardiovascular surgical planning. We are developing ARCollab, an iOS AR application designed to allow multiple surgeons and cardiologists to interact with patient-specific 3D heart models in a shared environment. ARCollab allows surgeons and cardiologists to import heart models, perform gestures to manipulate the heart, and collaborate with other users without having to produce a physical heart model. We are excited by the potential for ARCollab to make long-term real-world impact, thanks to the ubiquity of iOS devices that will allow for ARCollab's easy distribution, deployment and adoption.

Diffusion-based generative models' impressive ability to create convincing images has garnered global attention. However, their complex structures and operations often pose challenges for non-experts to grasp. We present Diffusion Explainer, the first interactive visualization tool that explains how Stable Diffusion transforms text prompts into images. Diffusion Explainer tightly integrates a visual overview of Stable Diffusion's complex structure with explanations of the underlying operations. By comparing image generation of prompt variants, users can discover the impact of keyword changes on image generation. A 56-participant user study demonstrates that Diffusion Explainer offers substantial learning benefits to non-experts. Our tool has been used by over 10,300 users from 124 countries at https://poloclub.github.io/diffusion-explainer/., Comment: 5 pages, 7 figures

With recent advancements in diffusion models, users can generate high-quality images by writing text prompts in natural language. However, generating images with desired details requires proper prompts, and it is often unclear how a model reacts to different prompts or what the best prompts are. To help researchers tackle these critical challenges, we introduce DiffusionDB, the first large-scale text-to-image prompt dataset totaling 6.5TB, containing 14 million images generated by Stable Diffusion, 1.8 million unique prompts, and hyperparameters specified by real users. We analyze the syntactic and semantic characteristics of prompts. We pinpoint specific hyperparameter values and prompt styles that can lead to model errors and present evidence of potentially harmful model usage, such as the generation of misinformation. The unprecedented scale and diversity of this human-actuated dataset provide exciting research opportunities in understanding the interplay between prompts and generative models, detecting deepfakes, and designing human-AI interaction tools to help users more easily use these models. DiffusionDB is publicly available at: https://poloclub.github.io/diffusiondb., Comment: Accepted to ACL 2023 (nominated for best paper, top 1.6% of submissions, oral presentation). 17 pages, 11 figures. The dataset is available at https://huggingface.co/datasets/poloclub/diffusiondb. The code is at https://github.com/poloclub/diffusiondb. The interactive visualization demo is at https://poloclub.github.io/diffusiondb/explorer/

Discovering and making sense of relevant literature is fundamental in any scientific field. Node-link diagram-based visualization tools can aid this process; however, existing tools have been evaluated only on small scales. This paper evaluates Argo Scholar, an open-source visualization tool designed for interactive exploration of literature and easy sharing of exploration results. A large-scale user study of 122 participants from diverse backgrounds and experiences showed that Argo Scholar is effective at helping users find related work and understand paper connections, and incremental graph-based exploration is effective across diverse disciplines. Based on the user study and user feedback, we provide design considerations and feature suggestions for future work., Comment: VIS IEEE 22

Advanced surgical procedures for congenital heart diseases (CHDs) require precise planning before the surgeries. The conventional approach utilizes 3D-printing and cutting physical heart models, which is a time and resource intensive process. While rapid advances in augmented reality (AR) technologies have the potential to streamline surgical planning, there is limited research that evaluates such AR approaches with medical experts. This paper presents an evaluation with 6 experts, 4 cardiothoracic surgeons, and 2 cardiologists, from Children's Healthcare of Atlanta (CHOA) Heart Center to validate the usability and technical innovations of CardiacAR, a prototype mobile AR surgical planning application. Potential future improvements based on user feedback are also proposed to further improve the design of CardiacAR and broaden its access., Comment: IEEE VIS 2022. 2 pages, 1 figure

Discovering and making sense of relevant research literature is fundamental to becoming knowledgeable in any scientific discipline. Visualization can aid this process; however, existing tools' adoption and impact have often been constrained, such as by their reliance on small curated paper datasets that quickly become outdated or a lack of support for personalized exploration. We introduce Argo Scholar, an open-source, web-based visualization tool for interactive exploration of literature and easy sharing of exploration results. Argo Scholar queries and visualizes Semantic Scholar's live data of almost 200 million papers, enabling users to generate personalized literature exploration results in real-time through flexible, incremental exploration, a common and effective method for researchers to discover relevant work. Our tool allows users to easily share their literature exploration results as a URL or web-embedded IFrame application. Argo Scholar is open-sourced and available at https://poloclub.github.io/argo-scholar/., Comment: IEEE VIS 2021