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.

Flexible robots hold great promise for enhancing minimally invasive surgery (MIS) by providing superior dexterity, precise control, and safe tissue interaction. Yet, translating these advantages into endoscopic interventions within open cavities remains challenging. The lack of anatomical constraints and the inherent flexibility of such devices complicate their control, while the limited field of view of endoscopes restricts situational awareness. We present a robotic platform designed to overcome these challenges and demonstrate its potential in fetoscopic laser coagulation, a complex MIS procedure typically performed only by highly experienced surgeons. Our system combines a magnetically actuated flexible endoscope with teleoperated and semi-autonomous navigation capabilities for performing targeted laser ablations. To enhance surgical awareness, the platform reconstructs real-time mosaics of the endoscopic scene, providing an extended and continuous visual context. The ability of this system to address the key limitations of MIS in open spaces is validated in vivo in an ovine model.

Doctors from Scotland and the US have completed what is thought to be a world-first stroke procedure using a robot. Prof Iris Grunwald, of the University of Dundee, performed the remote thrombectomy - the removal of blood clots after a stroke - on a human cadaver that had been donated to medical science. The professor was at Ninewells Hospital in Dundee, while the body she was operating on while using the machine was across the city at the university. Hours later, Ricardo Hanel - a neurosurgeon in Florida - used the technology to carry out the first transatlantic surgery from his Jacksonville base on a human body in Dundee over 4,000 miles (6,400km) away. The team has called it a potential game changer if it becomes approved for use on patients.

Understanding how different stakeholders perceive risks in AI systems is essential for their responsible deployment. This paper presents a framework for stakeholder-grounded risk assessment by using LLMs, acting as judges to predict and explain risks. Using the Risk Atlas Nexus and GloVE explanation method, our framework generates stakeholder-specific, interpretable policies that shows how different stakeholders agree or disagree about the same risks. We demonstrate our method using three real-world AI use cases of medical AI, autonomous vehicles, and fraud detection domain. We further propose an interactive visualization that reveals how and why conflicts emerge across stakeholder perspectives, enhancing transparency in conflict reasoning. Our results show that stakeholder perspectives significantly influence risk perception and conflict patterns. Our work emphasizes the importance of these stakeholder-aware explanations needed to make LLM-based evaluations more transparent, interpretable, and aligned with human-centered AI governance goals.

Robotic assistance in minimally invasive surgery can greatly enhance surgical precision and reduce surgeon fatigue. This paper presents a focused investigation on the kinematic and ergonomic design principles for a laparoscopic surgical robotic arm aimed at high-precision tasks. We propose a 7-degree-of-freedom (7-DOF) robotic arm system that incorporates a remote center of motion (RCM) at the instrument insertion point and ergonomic considerations to improve surgeon interaction. The design is implemented on a general-purpose robotic platform, and a series of simulated surgical tasks were performed to evaluate targeting accuracy, task efficiency, and surgeon comfort compared to conventional manual laparoscopy. Experimental results demonstrate that the optimized robotic design achieves significantly improved targeting accuracy (error reduced by over 50%) and shorter task completion times, while substantially lowering operator muscle strain and discomfort. These findings validate the importance of kinematic optimization (such as added articulations and tremor filtering) and human-centered ergonomic design in enhancing the performance of robot-assisted surgery. The insights from this work can guide the development of next-generation surgical robots that improve surgical outcomes and ergonomics for the operating team.

Abstract--The introduction of a teleoperated surgical robotic system designed for minimally invasive procedures enables the emulation of two distinct control modes through a dedicated input device of the surgical console: (1) Inside Control Mode, which emulates tool manipulation near the distal end (i.e., as if the surgeon was holding the tip of the instrument inside the patient's body), and (2) Outside Control Mode, which emulates manipulation near the proximal end (i.e., as if the surgeon was holding the tool externally). The overarching aim of this reported research is to study and compare the surgeon's performance utilizing these two control modes of operation along with various scaling factors in a simulated vitreoretinal surgical setting. The console of Intraocular Robotic Interventional Surgical System (IRISS) was utilized but the surgical robot itself and the human eye anatomy was simulated by a virtual environment (VR) projected microscope view of an intraocular setup to a VR headset. Five experienced vitreoretinal surgeons and five subjects with no surgical experience used the system to perform fundamental tool/tissue tasks common to vitreoretinal surgery including: (1) touch and reset; (2) grasp and drop; (3) inject; (4) circular tracking. The results indicate that Inside Control outperforms Outside Control across multiple tasks and performance metrics. Higher scaling factors (20 and 30) generally provided better performance, particularly for reducing trajectory errors and tissue damage. This improvement suggests that larger scaling factors enable more precise control, making them the preferred option for fine manipulation tasks. However, task completion time was not consistently reduced across all conditions, indicating that surgeons may need to balance speed and accuracy/precision based on specific surgical requirements. By optimizing control dynamics and user interface, robotic teleoperation has the potential to reduce complications, enhance surgical dexterity, and expand the accessibility of high-precision procedures to a broader range of practitioners. In Minimally Invasive Surgery (MIS), surgical instruments are introduced into the body through small ports established at the skin surface or, in the case of ophthalmic procedures, through specific ocular tissues such as the sclera, cornea, or conjunctiva. Unlike open surgery, where the surgeon may manipulate the tool from any position along its shaft--including proximally or distally--MIS confines the surgeon's interaction to the proximal end of the tool, which remains external to the patient's body, while the distal end performs the intervention through the fixed port.

The persistent gap between the growing global surgical demand and the trained surgical workforce [1] highlights the need to develop scalable solutions that can enhance training paradigms and optimize workflow management [2]. Computer-assisted surgery (CAS) systems are one approach to address this challenge, with applications in preoperative planning [3], intraoperative guidance [4], and standardized postoperative assessment [5, 6]. The development and validation of these advanced CAS capabilities fundamentally depend on access to large-scale, deeply annotated surgical video datasets that capture procedural phases, instrument-tissue interactions, and technical skill cues [7, 8]. Phacoemulsification cataract surgery is the most common ophthalmic procedure worldwide and the primary intervention for avoidable blindness [9, 10]. This makes it a critical domain for developing data-driven CAS with potential applications in clinical workflows and training [11, 12]. Publicly available datasets for developing CAS in cataract surgery, such as Cataract-1K [13] and CaDIS [14], are limited by their single-center origin and limited annotation scopes [15]. The absence of a multi-source dataset with comprehensive and multi-layered annotations, including objective skill assessments, has limited the development of generalizable multi-task deep learning models [11]. To address this gap, we present the Cataract-LMM (Large-scale, Multi-source, Multi-task) Dataset, a dataset of 3,000 phacoemulsification procedures recorded at two distinct clinical centers (Farabi and Noor Eye Hospitals, Tehran, Iran) between December 2021 and March 2025. The dataset is enriched with four complementary layers of annotations on subsets of the data: 1. Temporal Phase Labels (Phase): Frame-wise annotations for 13 surgical phases across 150 videos to support automated workflow recognition.

Abstract--Vision language models (VLMs), such as CLIP and OpenCLIP, can encode and reflect stereotypical associations between medical professions and demographic attributes learned from web-scale data. We present an evaluation protocol for healthcare settings that quantifies associated biases and assesses their operational risk. Our methodology (i) defines a taxonomy spanning clinicians and allied healthcare roles (e.g., surgeon, cardiologist, dentist, nurse, pharmacist, technician), (ii) curates a profession-aware prompt suite to probe model behavior, and (iii) benchmarks demographic skew against a balanced face corpus. Empirically, we observe consistent demographic biases across multiple roles and vision models. Our work highlights the importance of bias identification in critical domains such as healthcare as AI-enabled hiring and workforce analytics can have downstream implications for equity, compliance, and patient trust. Vision language models (VLMs) constitute a class of AI architectures that learn joint representation by aligning visual perception with natural language semantics [1]. Typically, an image encoder is paired with a text encoder and trained to inhabit a shared embedding space that supports cross-modal correspondence between images and linguistic descriptions. One such instance is OpenAI's CLIP (Contrastive Language Image Pretraining) which is optimized on roughly 400 million image-text pairs and exhibits strong zero-shot ability for The code can be found at https://github.com/zohaibhasan066/ VLMs enable a broad spectrum of multimodal functionalities, including image captioning, visual question answering, and bidirectional text-image retrieval with downstream applications in search, recommendation, and human-computer interaction.

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Purpose: Delineating tumor boundaries during breast-conserving surgery is challenging as tumors are often highly mobile, non-palpable, and have irregularly shaped borders. To address these challenges, we introduce a cooperative robotic guidance system that applies haptic feedback for tumor localization. In this pilot study, we aim to assess if and how this system can be successfully integrated into breast cancer care. Methods: A small haptic robot is retrofitted with an electrocautery blade to operate as a cooperatively controlled surgical tool. Ultrasound and electromagnetic navigation are used to identify the tumor boundaries and position. A forbidden region virtual fixture is imposed when the surgical tool collides with the tumor boundary. We conducted a study where users were asked to resect tumors from breast simulants both with and without the haptic guidance. We then assess the results of these simulated resections both qualitatively and quantitatively. Results: Virtual fixture guidance is shown to improve resection margins. On average, users find the task to be less mentally demanding, frustrating, and effort intensive when haptic feedback is available. We also discovered some unanticipated impacts on surgical workflow that will guide design adjustments and training protocol moving forward. Conclusion: Our results suggest that virtual fixtures can help localize tumor boundaries in simulated breast-conserving surgery. Future work will include an extensive user study to further validate these results and fine-tune our guidance system.