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Cytology is commonly used for cervical cancer screening or triage. Here, the authors used deep learning to develop a liquid-based cytology screening approach to detect cervical cancer in both community and hospital-based settings.

Unfairness in AI healthcare models arise from biased correlations between sensitive attributes and disease data. Here, the authors show that the Attribute Neutral Framework reduces these biases and enhances fairness without compromising diagnostic accuracy.

Large-scale irrigation changes local rainfall patterns, leading to more afternoon rain events 10 to 50 km downwind of irrigated areas. These findings will help constrain climate and weather forecasting models and support regional water management.

In a large trial involving 180 classes in different contexts in China, interactions with a chatbot provided to parents increased uptake and appointments for human papillomavirus vaccination, with stronger increases in rural areas.

The practicality of memristor-based computation-in-memory (CIM) is limited by the specific hardware design and the manual parameters tuning process. Here, the authors develop a full-stack CIM system with both hardware and software design for improved flexibility and efficiency.

In-sensor computing offers a promising solution for image processing with reduced data transfer. Here, the authors report programmable and multifunctional van der Waals optoelectronic sensors, showing their application for snapshot compression and recognition of dynamic videos and 3D spectral data.

Cancer-targeted drug discovery can be achieved by transcriptomics screening on patients. Here this group reports a drug target screening model built upon triple-negative breast cancer (TNBC) cohort and drug database with the selected drug pair exhibiting effective pyroptosis induction and TNBC tumor growth inhibition.

Artificial Intelligence (AI) models for medical diagnosis often face challenges of generalizability and fairness. Here, the authors show that the Quasi-Pareto Improvement approach is widely applicable to improving AI models among less-prevalent subgroups, promoting equitable healthcare outcomes.

Ruppin and colleagues overview recent research on the use of AI frameworks in precision oncology, describe ten hallmarks of their contributions across cancer detection, therapy optimization and treatment discovery, and discuss key challenges in clinical implementation

Early detection and accurate diagnosis of focal liver lesions are crucial for effective treatment and prognosis. Here, the authors present a fully automated diagnostic system that leverages multi-phase CT scans and clinical features, for diagnosing liver lesions.

The clinical significance of inferring cell spatial profiles from histology images from cancer patients remains to be explored. Here, the authors develop a weakly-supervised deep-learning method, HistoCell, for the direct prediction of super-resolution cell spatial profiles from histology images at the single-nucleus-level.

Chiral materials with strong and tunable chiroptical activities are highly desirable. Here, the authors employ an AI-guided robotic platform to fully execute a cyclic process to perform inverse design and fabricate chiral films with target chiroptical performance.

A large-scale photonic accelerator comprising more than 16,000 components integrated on a single chip to process MAC operations is described, demonstrating ultralow latency and reduced computing time compared with a commercially available GPU.

Fibers derived from non-silk proteins hold potential for various biomedical applications, but mechanically-balanced and highly-biocompatible regenerated protein fibers are elusive. Here, the authors report an entanglement-reinforced strategy to fabricate keratin/albumin bio-fibers that show high strength and toughness, along with favorable biocompatibility, degradability and immunocompatibility.

Caesium cations promote the coagulation of 2D and 3D perovskite colloids, synchronizing their nucleation kinetics and enabling the formation of homogeneous 2D/3D heterostructured lead-free photovoltaics with a certified power conversion efficiency of 16.65%.

Systematic analysis from the Global Burden of Disease study reported that, despite global improvements from 1990 to 2021, dietary iron deficiency-associated disease burden remains high in women, children and residents in low socio-demographic index countries.

Somatically determined preferential allelic expression of select genes that when mutated cause inborn errors of immunity corresponds with disease phenotypes, suggesting that the penetrance and expressivity of monogenic disorders is also dependent on the ‘transcriptotype’.

Spotiphy (spot imager with pseudo-single-cell-resolution histology) is a computational toolkit that transforms sequencing-based spatially resolved transcriptomics data into whole-transcriptome images with single-cell resolution.

Technical advancements have significantly improved early diagnosis of cervical cancer, but accurate diagnosis is still difficult due to various practical factors. Here, the authors develop an artificial intelligence assistive diagnostic solution to improve cervical liquid-based thin-layer cell smear diagnosis according to clinical TBS criteria in a large multicenter study.

The genetic risk of clear cell renal cell carcinoma is complex and the genes related are unclear. Here, the authors propose a deep reinforcement based model to identify risk genes.

Nature Computational Science asked a group of scientists to discuss strategies for increasing the presence of Black, Indigenous, People of Color (BIPOC) researchers in computational science, as well as the various considerations to be made for improving education and methods design.

Artificial-intelligence tools are transforming data-driven science — better ethical standards and more robust data curation are needed to fuel the boom and prevent a bust.

An artificial intelligence (AI) system performs as well as or better than radiologists at detecting breast cancer from mammograms, and using a combination of AI and human inputs could help to improve screening efficiency.

Understanding the genetic factors that regulate panicle architecture is important for improving rice yields. In this study, Li et al. demonstrate that the gene PTB1is critical for regulating panicle seed setting rate and pollen tube growth.

Baseline performance, benchmarks, and guidance for LLMs in biomedicine are limited. The authors assess four LLMs on 12 tasks, establish baselines, examine hallucinations, and provide recommendations for optimal LLM use.

Yandao Huang and colleagues introduce a non-contact system that integrates fiber optic sensors with AI to achieve accurate, medical-grade ballistocardiography signal detection. This system allows for continuous nocturnal blood pressure monitoring, aiding in early screening and managing hypertension and other cardiovascular diseases.

The authors demonstrate an AI-enhanced imaging method combining lipid-specific dyes and super-resolution microscopy enables simultaneous tracking of up to 15 organelles in living cells, revealing dynamic interactions across diverse biological systems.

Multislice electron ptychography reveals ferroelectric microstructures with sub-ångström lateral resolution and nanometre depth resolution, directly imaging a ferroelectricity generated by anion displacements relative to the Nb sublattice.

Processing heterogeneous visual data in edge-side intelligent machines is complex and inefficient. Here, the authors propose a hardware-software co-designed system using random resistive memory, achieving significant energy efficiency and training cost reductions.

Generative artificial intelligence (GAI) is driving a surge in e-waste due to intensive computational infrastructure needs. This study emphasizes the necessity for proactive implementation of circular economy practices throughout GAI value chains.

Lactate dehydrogenase B (LDHB) is known to fuel cancer cells by converting lactate to pyruvate. Here, the authors identify that LDHB protects cancer cells from mitochondria-associated ferroptosis via ubiquinol-dependent lactate oxidation, which is independent of lactate’s role as a carbon source.

Collaborative augmented reconstruction (CAR) is a platform for large-scale reconstruction of neurons and other cells from multi-dimensional imaging datasets. It can be accessed from a variety of devices simultaneously for efficient and accurate reconstruction.

Monolithic 3D integration of electronics based on fully 2D materials is demonstrated in the performance of artificial intelligence tasks.

Socioeconomic status is multidimensional and can change over time. Xu, Lui, Ji et al. found dimension-specific, late-stage-specific and change-specific socioeconomic effects on brain, cognition, personality and emotion.

Failure to recognize samples from unseen classes is a major limitation of AI recognition and classification of retinal anomalies. Here, the authors present the Uncertainty-inspired Open Set learning model that categorises fundus images into pre-trained categories, and provides an uncertainty score that alerts the need for manual inspection when dealing with out-of-distribution images.

Duchenne muscular dystrophy (DMD) is a fatal genetic disorder caused by mutations in the DMD gene. Here, the authors develop MyoAAV-UA, a compact utrophin activator, as a promising universal treatment for DMD through a homologous protein activation approach.

Neural networks could learn new concepts quickly and from only a few examples by using a ferroelectric ternary content-addressable memory as an augmented memory.