Surgical simulations offer a significant advantage by eliminating the need for patient involvement in skills practice, providing trainees with essential technical lessons before performing procedures on humans [24]. However, current computer-based simulations have lots of drawbacks, such as unrealistic visual appearance, lacking variability, and complex creation procedures taking into account the varying anatomical properties, all of which lead to diminishing the quality of surgical training. Therefore, AI-generated surgical simulations promise significant advancements in medical education since the underlying machine-learning models can learn the anatomical and visual characteristics of surgeries as well as their interactions with surgical tools from real-world data. Similar to recent works on image-guided surgery by Ramalhinho et al. [18] and Schneider et al. [23], our work focuses on laparoscopic surgery. We propose an approach for generating realistic laparoscopic videos conditioned on both text prompts and surgical tool positions. This lays the groundwork for a dynamic and interactive surgical training platform that mimics real-world scenarios. With this approach, we achieve state-of-the-art realism with an FID score of 33.43 and a pixel-wise F1 score of 0.72 for the control of tool positions. Moreover, we successfully generate coherent videos of single surgical actions.

Neural implicit k-space representations have shown promising results for dynamic MRI at high temporal resolutions. Yet, their exclusive training in k-space limits the application of common image regularization methods to improve the final reconstruction. In this work, we introduce the concept of parallel imaging-inspired self-consistency (PISCO), which we incorporate as novel self-supervised k-space regularization enforcing a consistent neighborhood relationship. At no additional data cost, the proposed regularization significantly improves neural implicit k-space reconstructions on simulated data. Abdominal in-vivo reconstructions using PISCO result in enhanced spatio-temporal image quality compared to state-of-the-art methods.

Background: With the ever-increasing amount of medical imaging data, the demand for algorithms to assist clinicians has amplified. Unsupervised anomaly detection (UAD) models promise to aid in the crucial first step of disease detection. While previous studies have thoroughly explored fairness in supervised models in healthcare, for UAD, this has so far been unexplored. Methods: In this study, we evaluated how dataset composition regarding subgroups manifests in disparate performance of UAD models along multiple protected variables on three large-scale publicly available chest X-ray datasets. Our experiments were validated using two state-of-the-art UAD models for medical images. Finally, we introduced a novel subgroup-AUROC (sAUROC) metric, which aids in quantifying fairness in machine learning. Findings: Our experiments revealed empirical "fairness laws" (similar to "scaling laws" for Transformers) for training-dataset composition: Linear relationships between anomaly detection performance within a subpopulation and its representation in the training data. Our study further revealed performance disparities, even in the case of balanced training data, and compound effects that exacerbate the drop in performance for subjects associated with multiple adversely affected groups. Interpretation: Our study quantified the disparate performance of UAD models against certain demographic subgroups. Importantly, we showed that this unfairness cannot be mitigated by balanced representation alone. Instead, the representation of some subgroups seems harder to learn by UAD models than that of others. The empirical fairness laws discovered in our study make disparate performance in UAD models easier to estimate and aid in determining the most desirable dataset composition.