Jochen K. Lennerz

Tom Bisson, Henriette Voelker, Sanddhya Jayabalan et al.

Large language model (LLM)-based AI agents are increasingly capable of complex clinical reasoning and may soon participate in medical decision-making with limited or no real-time human oversight. This shift raises fundamental questions about how the core principles of medical ethics (i.e., beneficence, nonmaleficence, autonomy, and justice) can be upheld when the clinical responsibility extends to autonomous systems. Here we propose an ethics-by-design framework for medical AI agents comprising six practical interventions: auditable ethical reasoning modules, explicit human override conditions, structured patient preference profiles, AI-specific ethics oversight tools, global benchmarking repositories for ethical scenarios, and regulatory sandboxes for real-world evaluation. Together, these mechanisms aim to operationalize ethical governance for emerging clinical AI agents. https://github.com/BissonTom/Ethical-Governance-of-Medical-AI-Agents

Puria Azadi Moghadam, Sanne Van Dalen, Karina C. Martin et al.

Visual microscopic study of diseased tissue by pathologists has been the cornerstone for cancer diagnosis and prognostication for more than a century. Recently, deep learning methods have made significant advances in the analysis and classification of tissue images. However, there has been limited work on the utility of such models in generating histopathology images. These synthetic images have several applications in pathology including utilities in education, proficiency testing, privacy, and data sharing. Recently, diffusion probabilistic models were introduced to generate high quality images. Here, for the first time, we investigate the potential use of such models along with prioritized morphology weighting and color normalization to synthesize high quality histopathology images of brain cancer. Our detailed results show that diffusion probabilistic models are capable of synthesizing a wide range of histopathology images and have superior performance compared to generative adversarial networks.

Christopher P. Bridge, Chris Gorman, Steven Pieper et al.

Machine learning is revolutionizing image-based diagnostics in pathology and radiology. ML models have shown promising results in research settings, but their lack of interoperability has been a major barrier for clinical integration and evaluation. The DICOM a standard specifies Information Object Definitions and Services for the representation and communication of digital images and related information, including image-derived annotations and analysis results. However, the complexity of the standard represents an obstacle for its adoption in the ML community and creates a need for software libraries and tools that simplify working with data sets in DICOM format. Here we present the highdicom library, which provides a high-level application programming interface for the Python programming language that abstracts low-level details of the standard and enables encoding and decoding of image-derived information in DICOM format in a few lines of Python code. The highdicom library ties into the extensive Python ecosystem for image processing and machine learning. Simultaneously, by simplifying creation and parsing of DICOM-compliant files, highdicom achieves interoperability with the medical imaging systems that hold the data used to train and run ML models, and ultimately communicate and store model outputs for clinical use. We demonstrate through experiments with slide microscopy and computed tomography imaging, that, by bridging these two ecosystems, highdicom enables developers to train and evaluate state-of-the-art ML models in pathology and radiology while remaining compliant with the DICOM standard and interoperable with clinical systems at all stages. To promote standardization of ML research and streamline the ML model development and deployment process, we made the library available free and open-source.