Bed-bound Lindsey Vonn reveals pain is'hard to manage' as she speaks out for the first time after FIFTH surgery on her broken leg'Fergie might end up having to tell her story to the police': 'Toxic' Sarah Ferguson is'broke and in a bad way' after Andrew's arrest...and looking to UAE for cash because'everyone is out to get her' The tide of sleaze rolling over Beatrice, Eugenie and Fergie is going to capsize them all. Moment Kate and William revealed their'true feelings' towards Andrew and Fergie: Princess'ignoring' Sarah and Prince'secretly scolding' his uncle... how Duchess of Kent's funeral said it all Kurt Cobain's uncle insists Nirvana legend was murdered and calls on cops to investigate clues that haunt him Kristi Noem's secret escape plan to ditch DHS revealed amid ICE raid fallout and'culture of fear' rumors Winter Olympics chiefs reach verdict on Jutta Leerdam's '$1m underwear-flashing gesture' after Jake Paul's fiancée faced covert marketing claims Country singer Conner Smith's charges DROPPED after he hit and killed a woman, 77, with his truck I ditched weight-loss shots for the new Wegovy pill and am astonished by the difference. The pounds are falling off, I have no side effects and it's cheaper The subtle early warning sign that revealed Eric Dane's illness - as Grey's Anatomy star dies of motor neurone disease Johnny Depp let Eric Dane live'rent-free in one of his LA homes' as he tried to ease Grey's Anatomy star's financial worries in the months before his death from ALS aged 53 Uproar as NYC's'communist' mayor announces crippling tax for ALL homeowners after promising to only go after billionaires Wall Street panics as America's growth stalls while everyday prices refuse to fall I stumbled across my wife's Pornhub search history and it's broken me. She told me it's'just a fantasy lots of women have' but now I fear I'll never be enough Non-binary activist wins compensation after taking year-and-a-half off work with stress because hair salon's online booking form only offered male or female cuts Courtney Love caught on camera fleeing shocking car collision... days after bombshell Kurt Cobain'homicide investigation' Trump-bashing Winter Olympics star Hunter Hess whines about'hardest weeks of his life' after being called a'real loser' by the president In a viral post on X, user @ShishirShelke1 shared their strange discovery about the clock app icon. Normally, the icon on the home screen shows the second hand smoothly gliding around the clock face.

To design an efficient intervention, decision-makers need not only to estimate the total effect of the intervention, but also understand the underlying causal mechanisms driving this effect.

We next discuss two lines of related literature.

However, the automation of MIS and RAMIS through the use of AI has been slow due to difficulties in data acquisition and curation, partially caused by the ethical considerations of training, testing and deploying AI models in medical environments.

A jury awarded $2 million to Fox Varian after doctors approved a double mastectomy at age 16. Former clinic insider Jamie Reed says the case validates long-held concerns.

In this paper, we propose a novel text promptable surgical instrument segmentation approach to overcome challenges associated with diversity and differentiation of surgical instruments in minimally invasive surgeries. We redefine the task as text promptable, thereby enabling a more nuanced comprehension of surgical instruments and adaptability to new instrument types. Inspired by recent advancements in vision-language models, we leverage pretrained image and text encoders as our model backbone and design a text promptable mask decoder consisting of attention-and convolution-based prompting schemes for surgical instrument segmentation prediction. Our model leverages multiple text prompts for each surgical instrument through a new mixture of prompts mechanism, resulting in enhanced segmentation performance. Additionally, we introduce a hard instrument area reinforcement module to improve image feature comprehension and segmentation precision. Extensive experiments on several surgical instrument segmentation datasets demonstrate our model's superior performance and promising generalization capability. To our knowledge, this is the first implementation of a promptable approach to surgical instrument segmentation, offering significant potential for practical application in the field of robotic-assisted surgery.

Video recordings of open surgeries are greatly required for education and research purposes. However, capturing unobstructed videos is challenging since surgeons frequently block the camera field of view. To avoid occlusion, the positions and angles of the camera must be frequently adjusted, which is highly labor-intensive. Prior work has addressed this issue by installing multiple cameras on a shadowless lamp and arranging them to fully surround the surgical area. This setup increases the chances of some cameras capturing an unobstructed view. However, manual image alignment is needed in post-processing since camera configurations change every time surgeons move the lamp for optimal lighting. This paper aims to fully automate this alignment task. The proposed method identifies frames in which the lighting system moves, realigns them, and selects the camera with the least occlusion to generate a video that consistently presents the surgical field from a fixed perspective. A user study involving surgeons demonstrated that videos generated by our method were superior to those produced by conventional methods in terms of the ease of confirming the surgical area and the comfort during video viewing. Additionally, our approach showed improvements in video quality over existing techniques. Furthermore, we implemented several synthesis options for the proposed view-synthesis method and conducted a user study to assess surgeons' preferences for each option.

Multimodal Large Language Models (MLLMs) have shown significant potential in surgical video understanding. With improved zero-shot performance and more effective human-machine interaction, they provide a strong foundation for advancing surgical education and assistance. However, existing research and datasets primarily focus on understanding surgical procedures and workflows, while paying limited attention to the critical role of anatomical comprehension. In clinical practice, surgeons rely heavily on precise anatomical understanding to interpret, review, and learn from surgical videos. To fill this gap, we introduce the Neurosurgical Anatomy Benchmark (NeuroABench), the first multimodal benchmark explicitly created to evaluate anatomical understanding in the neurosurgical domain. NeuroABench consists of 9 hours of annotated neurosurgical videos covering 89 distinct procedures and is developed using a novel multimodal annotation pipeline with multiple review cycles. The benchmark evaluates the identification of 68 clinical anatomical structures, providing a rigorous and standardized framework for assessing model performance. Experiments on over 10 state-of-the-art MLLMs reveal significant limitations, with the best-performing model achieving only 40.87% accuracy in anatomical identification tasks. To further evaluate the benchmark, we extract a subset of the dataset and conduct an informative test with four neurosurgical trainees. The results show that the best-performing student achieves 56% accuracy, with the lowest scores of 28% and an average score of 46.5%. While the best MLLM performs comparably to the lowest-scoring student, it still lags significantly behind the group's average performance. This comparison underscores both the progress of MLLMs in anatomical understanding and the substantial gap that remains in achieving human-level performance.

Intraday surgical scheduling is a multi-objective decision problem under uncertainty-balancing elective throughput, urgent and emergency demand, delays, sequence-dependent setups, and overtime. We formulate the problem as a cooperative Markov game and propose a multi-agent reinforcement learning (MARL) framework in which each operating room (OR) is an agent trained with centralized training and decentralized execution. All agents share a policy trained via Proximal Policy Optimization (PPO), which maps rich system states to actions, while a within-epoch sequential assignment protocol constructs conflict-free joint schedules across ORs. A mixed-integer pre-schedule provides reference starting times for electives; we impose type-specific quadratic delay penalties relative to these references and a terminal overtime penalty, yielding a single reward that captures throughput, timeliness, and staff workload. In simulations reflecting a realistic hospital mix (six ORs, eight surgery types, random urgent and emergency arrivals), the learned policy outperforms six rule-based heuristics across seven metrics and three evaluation subsets, and, relative to an ex post MIP oracle, quantifies optimality gaps. Policy analytics reveal interpretable behavior-prioritizing emergencies, batching similar cases to reduce setups, and deferring lower-value electives. We also derive a suboptimality bound for the sequential decomposition under simplifying assumptions. We discuss limitations-including OR homogeneity and the omission of explicit staffing constraints-and outline extensions. Overall, the approach offers a practical, interpretable, and tunable data-driven complement to optimization for real-time OR scheduling.