Advances in DNA nanoengineering promise the development of new computing devices within biological systems, with applications in nanoscale sensing, diagnostics and therapeutics.

Federated learning can be used to train medical AI models on sensitive personal data while preserving important privacy properties; however, the sensitive nature of the data makes it difficult to evaluate approaches reproducibly on real data. The MedPerf project presented by Karargyris et al. provides the tools and infrastructure to distribute models to healthcare facilities, such that they can be trained and evaluated in realistic settings.

With the advances in neural language models, the question arises if some models align better with human processing than others. Golan et al. identify sentences that language models disagree about and use them to compare the shortcomings of different language models.

Deep learning applied to live-cell images of patient-derived neurons aids predicting underlying mechanisms and gains insights into neurodegenerative diseases, facilitating the understanding of mechanistic heterogeneity. D’Sa and colleagues use patient-derived stem cell models, high-throughput imaging and machine learning algorithms to investigate Parkinson’s disease subtyping.

Local methods of explainable artificial intelligence identify where important features or inputs occur, while global methods try to understand what features or concepts have been learned by a model. The authors propose a concept-level explanation method that bridges the local and global perspectives, enabling more comprehensive and human-understandable explanations.

With the advances in neural language models, the question arises if some models align better with human processing than others. Golan et al. identify sentences that language models disagree about and use them to compare the shortcomings of different language models.

An outstanding challenge in materials science is doing large-scale simulations with complex electron interactions. Deng and colleagues introduce a universal graph neural network-based interatomic potential integrating atomic magnetic moments as charge constraints, which allows for capturing subtle chemical properties in several lithium-based solid-state materials

Generating novel molecules that bind to specific protein targets is a challenging but important task in computational drug design. Zhang and colleagues present a molecular generation method based on hierarchical auto-regression.

For virtual protein docking, an accurate scoring function is necessary that evaluates how likely a protein conformation is. Stebliankin and colleagues present a method based on vision transformers that provides a more accurate score by evaluating individual binding interfaces as multi-channel images.

Achieving sequential robotic actions involving different manipulation skills is an open challenge that is critical to enable robots to interact meaningfully with their physical environment. Triantafyllidis and colleagues present a hierarchical learning framework based on an ensemble of specialized neural networks to solve complex long-horizon manipulation tasks.

Advances in DNA nanoengineering promise the development of new computing devices within biological systems, with applications in nanoscale sensing, diagnostics and therapeutics.

Machine learning and quantum computing approaches are converging, fuelling considerable excitement over quantum devices and their capabilities. However, given the current hardware limitations, it is important to push the technology forward while being realistic about what quantum computers can do, now and in the near future.

Medical artificial intelligence needs governance to ensure safety and effectiveness, not just centrally (for example, by the US Food and Drug Administration) but also locally to account for differences in care, patients and system performance. Practical collaborative governance will enable health systems to carry out these challenging governance tasks, supported by central regulators.