29 citations · 104 across the 23 of their papers we have counts for
24 papers
Current validation practice undermines surgical AI development
Annika Reinke, Ziying O. Li, Minu D. Tizabi +98
Surgical data science (SDS) is rapidly advancing, yet clinical adoption of artificial intelligence (AI) in surgery remains limited, with inadequate validation as an important contr…
MedBrowseComp: Benchmarking Medical Deep Research and Computer Use
Shan Chen, Pedro Moreira, Yuxin Xiao +6
Large language models (LLMs) are increasingly envisioned as decision-support tools in clinical practice, yet safe clinical reasoning demands integrating heterogeneous knowledge bas…
SRT-H: A Hierarchical Framework for Autonomous Surgery via Language Conditioned Imitation Learning
Ji Woong Kim, Juo-Tung Chen, Pascal Hansen +11
Research on autonomous surgery has largely focused on simple task automation in controlled environments. However, real-world surgical applications demand dexterous manipulation ove…
TxGemma: Efficient and Agentic LLMs for Therapeutics
Eric Wang, Samuel Schmidgall, Paul F. Jaeger +6
Therapeutic development is a costly and high-risk endeavor that is often plagued by high failure rates. To address this, we introduce TxGemma, a suite of efficient, generalist larg…
Surgical Gaussian Surfels: Highly Accurate Real-time Surgical Scene Rendering using Gaussian Surfels
Idris O. Sunmola, Zhenjun Zhao, Samuel Schmidgall +4
Accurate geometric reconstruction of deformable tissues in monocular endoscopic video remains a fundamental challenge in robot-assisted minimally invasive surgery. Although recent…
Agent Laboratory: Using LLM Agents as Research Assistants
Samuel Schmidgall, Yusheng Su, Ze Wang +7
Historically, scientific discovery has been a lengthy and costly process, demanding substantial time and resources from initial conception to final results. To accelerate scientifi…
Tracking Tumors under Deformation from Partial Point Clouds using Occupancy Networks
Pit Henrich, Jiawei Liu, Jiawei Ge +5
To track tumors during surgery, information from preoperative CT scans is used to determine their position. However, as the surgeon operates, the tumor may be deformed which presen…
Surgical Robot Transformer (SRT): Imitation Learning for Surgical Tasks
Ji Woong Kim, Tony Z. Zhao, Samuel Schmidgall +4
We explore whether surgical manipulation tasks can be learned on the da Vinci robot via imitation learning. However, the da Vinci system presents unique challenges which hinder str…
AgentClinic: a multimodal agent benchmark to evaluate AI in simulated clinical environments
Samuel Schmidgall, Rojin Ziaei, Carl Harris +3
Evaluating large language models (LLM) in clinical scenarios is crucial to assessing their potential clinical utility. Existing benchmarks rely heavily on static question-answering…
General surgery vision transformer: A video pre-trained foundation model for general surgery
Samuel Schmidgall, Ji Woong Kim, Jeffrey Jopling +1
The absence of openly accessible data and specialized foundation models is a major barrier for computational research in surgery. Toward this, (i) we open-source the largest datase…
Addressing cognitive bias in medical language models
Samuel Schmidgall, Carl Harris, Ime Essien +7
There is increasing interest in the application large language models (LLMs) to the medical field, in part because of their impressive performance on medical exam questions. While…
General-purpose foundation models for increased autonomy in robot-assisted surgery
Samuel Schmidgall, Ji Woong Kim, Alan Kuntz +2
The dominant paradigm for end-to-end robot learning focuses on optimizing task-specific objectives that solve a single robotic problem such as picking up an object or reaching a ta…
Language models are susceptible to incorrect patient self-diagnosis in medical applications
Rojin Ziaei, Samuel Schmidgall
Large language models (LLMs) are becoming increasingly relevant as a potential tool for healthcare, aiding communication between clinicians, researchers, and patients. However, tra…
Synaptic motor adaptation: A three-factor learning rule for adaptive robotic control in spiking neural networks
Samuel Schmidgall, Joe Hays
Legged robots operating in real-world environments must possess the ability to rapidly adapt to unexpected conditions, such as changing terrains and varying payloads. This paper in…
Brain-inspired learning in artificial neural networks: a review
Samuel Schmidgall, Jascha Achterberg, Thomas Miconi +4
Artificial neural networks (ANNs) have emerged as an essential tool in machine learning, achieving remarkable success across diverse domains, including image and speech generation,…
Biological connectomes as a representation for the architecture of artificial neural networks
Samuel Schmidgall, Catherine Schuman, Maryam Parsa
Grand efforts in neuroscience are working toward mapping the connectomes of many new species, including the near completion of the Drosophila melanogaster. It is important to ask w…
Learning to learn online with neuromodulated synaptic plasticity in spiking neural networks
Samuel Schmidgall, Joe Hays
We propose that in order to harness our understanding of neuroscience toward machine learning, we must first have powerful tools for training brain-like models of learning. Althoug…
Stable Lifelong Learning: Spiking neurons as a solution to instability in plastic neural networks
Samuel Schmidgall, Joe Hays
Synaptic plasticity poses itself as a powerful method of self-regulated unsupervised learning in neural networks. A recent resurgence of interest has developed in utilizing Artific…
Self-Replicating Neural Programs
Samuel Schmidgall
In this work, a neural network is trained to replicate the code that trains it using only its own output as input. A paradigm for evolutionary self-replication in neural programs i…
Evolutionary Self-Replication as a Mechanism for Producing Artificial Intelligence
Samuel Schmidgall, Joseph Hays
Can reproduction alone in the context of survival produce intelligence in our machines? In this work, self-replication is explored as a mechanism for the emergence of intelligent b…
SpikePropamine: Differentiable Plasticity in Spiking Neural Networks
Samuel Schmidgall, Julia Ashkanazy, Wallace Lawson +1
The adaptive changes in synaptic efficacy that occur between spiking neurons have been demonstrated to play a critical role in learning for biological neural networks. Despite this…
Self-Constructing Neural Networks Through Random Mutation
Samuel Schmidgall
The search for neural architecture is producing many of the most exciting results in artificial intelligence. It has increasingly become apparent that task-specific neural architec…
Locked fronts in a discrete time discrete space population model
Matt Holzer, Zachary Richey, Wyatt Rush +1
A model of population growth and dispersal is considered where the spatial habitat is a lattice and reproduction occurs generationally. The resulting discrete dynamical systems exh…
Adaptive Reinforcement Learning through Evolving Self-Modifying Neural Networks
Samuel Schmidgall
The adaptive learning capabilities seen in biological neural networks are largely a product of the self-modifying behavior emerging from online plastic changes in synaptic connecti…