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Neuromorphic computing shows promise for advancing computing efficiency and capabilities of AI applications using brain-inspired principles. However, the neuromorphic research field currently lacks standardized benchmarks, making it difficult to accurately measure technological advancements, compare performance with conventional methods, and identify promising future research directions. This article presents NeuroBench, a benchmark framework for neuromorphic algorithms and systems, which is collaboratively designed from an open community of researchers across industry and academia. NeuroBench introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent and hardware-dependent settings. For latest project updates, visit the project website ( neurobench.ai )., Competing Interests: Competing interests: The Authors declare the following competing interests: The design of the benchmark framework reflects the Authors’ research interests, which includes non-financial and financial commercial interests. These interests do not affect our results in any way, nor the value of our contributions. The NeuroBench benchmark framework was developed collaboratively and specifically to allow for as objective and applicable comparison as possible. The benchmark harness and framework are open-source and intended to be further extended by the community over time., (© 2025. The Author(s).)

Introduction: Biliary tract cancer (BTC) comprises a clinically diverse and genetically heterogeneous group of tumors along the intra- and extrahepatic biliary system (intrahepatic and extrahepatic cholangiocarcinoma) and gallbladder cancer with the common feature of a poor prognosis, despite increasing molecular knowledge of associated genetic aberrations and possible targeted therapies. Therefore, the search for even more precise and individualized therapies is ongoing and preclinical tumor models are central to the development of such new approaches., Areas Covered: The models described in the current review include simple and advanced in vitro and in vivo models, including cell lines, 2D monolayer, spheroid and organoid cultures, 3D bioprinting, patient-derived xenografts, and more recently, machine-perfusion platform-based models of resected liver specimens. All these models have individual advantages, disadvantages and limitations that need to be considered depending on the desired application., Expert Opinion: In addition to potential cost limitations, availability of BTC cell types, time required for model establishment and growth success rate, the individual models differently reflect relevant characteristics such as tumor heterogeneity, spatial tumor-stroma microenvironment interactions, metabolic and nutritional gradients and immunological interactions. Therefore, a consequent combination of different models may be required to improve clinical study outcomes by strengthening the preclinical data basis.