Human neural organoids have become powerful systems for investigating neural development, circuit formation and disease mechanisms in a species-specific context. Traditional animal models and two-dimensional cultures have long informed our understanding of neural activity, but they cannot reproduce the genetic, developmental and architectural complexity of the human brain. Neural organoids bridge this gap by enabling direct measurement of neuronal activity at cellular, ensemble and network scales. Functional interrogation now spans calcium and voltage imaging, patch clamp electrophysiology, and multielectrode array recordings, enabled by bioengineering advances that improve organoid viability and electrode–tissue coupling. Multi-regional assembloid models and xenotransplantation approaches further extend functional maturation and enable investigation of inter-regional connectivity. The integration of computational frameworks with functional measurements also enables extraction of quantitative features such as excitability, synchrony and network topology, linking molecular programs to emergent circuit behaviour. Furthermore, emerging studies demonstrate that neural organoids can exhibit synaptic plasticity, adaptive computation and goal-directed learning in closed-loop paradigms, opening research directions beyond spontaneous activity characterization. Functional organoids can also expand disease modelling by revealing mechanisms of network dysfunction across neurodevelopmental, neuropsychiatric and neurodegenerative disorders. Nevertheless, the absence of standardized benchmarks, reference datasets and reproducible analysis pipelines continues to limit comparability across studies. This Review synthesizes current technologies and analytical strategies for probing activity in neural organoids; evaluates their translational relevance and limitations; and discusses how interdisciplinary integration of stem cell biology, bioengineering, systems neuroscience and computational modelling could establish robust frameworks for mechanistic and ethically grounded exploration of human brain function. Neural organoids can be used to characterize functional phenotypes across cellular and network scales, extending their applicability beyond studies of development and molecular identity. This Review examines optical and electrophysiological approaches for measuring neural organoid activity; presents a multiscale framework for evaluating network function; and discusses practical considerations for experimental design, data analysis and interpretation.
Congenital and genetic disorders often cause death or lifelong disability, and treatments after birth are frequently unable to reverse the effects of established diseases. In utero therapies, which intervene during fetal development, offer an opportunity to treat conditions early, as demonstrated by existing fetal surgical therapies. Advances in bioengineering have expanded these approaches and improved their precision and potential effectiveness. However, challenges related to the safe delivery of therapies and the careful consideration of risks to both the mother and the fetus remain. In this Review, we examine emerging bioengineering strategies for fetal treatments, including tissue engineering, stem cell transplantation and gene-based approaches. We further discuss how these methods could overcome existing limitations and improve the translation of in utero therapies into clinical use. The fetal environment is characterized by rapid regeneration, superior wound healing and a relatively tolerogenic immune system compared with the postnatal state, making it an ideal setting to treat many congenital and genetic diseases. This Review discusses in utero bioengineering strategies to address unmet clinical needs and conditions that have no, or inadequate, treatments.
China’s rapid progress in in vivo chimeric antigen receptor (CAR)-T cell therapy reflects strengths of its biotechnology sector, which brings together research institutions, industry and hospitals to advance this emerging treatment. Drawing on our experience, we discuss how this expansion occurred and how speed and scale can be converted into original, clinically differentiated therapies.
Clinical microbiology needs an artificial intelligence system that predicts pathogen evolution and resistance to enable prevention. Laboratories should connect forecasts to confirmatory experiments and defined actions, then test them prospectively across time and populations, as well as govern false alarms, model updates and inequitable performance as carefully as diagnostic errors.
Randomized controlled trials remain the gold standard for evaluating the benefits and risks of new interventions, yet they face persistent challenges including high costs constraining sample sizes, complex eligibility criteria, recruitment difficulties, prolonged follow-up periods to capture hard end points, and the growing prevalence of poorly designed studies that create noise in literature. This Review proposes an artificial intelligence (AI)-enabled clinical trial engineering framework, applicable across medical specialties. This framework builds on four stages: assembling and harmonizing multimodal data to develop and validate tools for enabling trials; matching fit-for-purpose tools to the research question and target outcome; AI-supported trial conduct, including patient-to-trial matching, surrogate end points, externally matched comparator arms, digital twins, and automated data collection and curation; and faster, evidence-based go/no-go decisions that flag non-promising drugs early. Three overarching principles operate throughout: fit-for-purpose validation, continuous regulatory engagement and human oversight. This Review illustrates the framework through representative case studies, with the iBox surrogate in transplantation, the annualized relapse rate in multiple sclerosis, heart digital twins for ventricular tachycardia, AI-assisted histology (AIM-MASH) in hepatology and a large language model for patient-to-trial matching. It further maps the evolving regulatory landscape for AI-enabled clinical trials (EMA, FDA and the EU AI Act). Applied judiciously, AI-enabled clinical trials could shorten timelines, reduce costs and accelerate both the identification of effective therapies and the earlier elimination of futile ones. Artificial intelligence is poised to reshape clinical trials, addressing challenges in design, recruitment, monitoring and conduct. This Review outlines a framework built on deeply phenotyped cohorts, validated surrogate end points, digital twins and agentic AI systems that could shorten timelines and support the trial life cycle, highlighting opportunities to accelerate drug development while maintaining rigorous regulatory and benefit–risk assessment.
