Humanoid robotics, robotic prosthetics, genomics and RNA technologies, nanomedicine, and advanced microscopy were the key themes presented today at the Italy Pavilion at the EXPO 2025 Osaka in Japan during the workshop "Health Technologies: How Science is Transforming Healthcare" organized by the Italian Institute of Technology (IIT) during the health week and moderated by the science journalist Akihiko Mori. The event brought together IIT scientists and experts from Italian and Japanese research institutes and companies with whom IIT collaborates on future medical technologies, including Nikon Instruments, RIKEN, Kanazawa University and Bracco. The workshop began with opening remarks from Mario Andrea Vattani, Italy's Commissioner General for Expo 2025 Osaka. This was followed by a discussion on the future of humanoid robotics in an aging society, featuring IIT Scientific Director Giorgio Metta and Hiroshi Ishiguro, a professor at Osaka University and the creator of one of the Expo's three thematic areas.

Designed proteins are anticipated to have groundbreaking impact on a range of issues from treating disease to tackling environmental problems. With a DKK 700 million grant from the Novo Nordisk Foundation and headed by Professor Dek Woolfson, a new Center for Protein Design (CPD) at the University of Copenhagen has ambitions to match this potential. The CPD will spearhead developments in protein design and its applications through strong interdisciplinary collaborations across the university and partnerships in Denmark and internationally.

Laboratory medicine is an essential part of the diagnostic process, supporting clinical decisions, guiding and addressing therapy. The recent COVID-19 pandemic illustrated well the key role of laboratory medicine in the diagnosis, management and prognosis of SARS-CoV-2 infected patients. Technological advances improved the laboratory diagnosis and patients’ management and others appear very promising as clustered regularly interspaced short palindromic repeats (CRISPR) or artificial intelligence (AI). This review describes the current diagnostic assays routinely used in laboratory as well as the novel technologies not in routine yet but that represent future directions and will probably dominate the laboratory in the next years. Serology is important for detecting antibodies and/or antigens of the infectious pathogens or for epidemiological purposes, while real-time PCR with its high sensitivity and specificity has a key role in pathogen detection in different biological matrices and in monitoring the therapy. Nanochip-based technologies make possible delivering a laboratory report at the patient’s bed or in settings where a laboratory-based hospital is not available. Next generation sequencing (NGS) is a massively high throughput parallel sequencing technology that allows the simultaneous sequence of billions of DNA fragments in a short time frame. This technology can be used to detect drug-associated mutations, minority species within an infected patient or for pathogen identification. CRISPR-based technology is a fast and accurate diagnostic method that can be applied to different human diseases including infectious diseases. Artificial intelligence is increasingly used in laboratory medicine. In clinical microbiology, it is used to build up diagnosis analyzing genomic information or mass spectra from isolated bacteria, for predicting antibiotic sensitivity or for processing in a short time a large number of images with meaningful results. Thus, the laboratory is becoming increasingly automated and interwoven with sophisticated software or algorithms that will increase the sensitivity and specificity of diagnoses, besides reducing time to results.

This work marks the first practical use of boson sampling, long seen as a key demonstration of quantum computing’s potential to outperform classical methods.

The researchers used computer simulations to model a quantum optical experiment that recognizes images using just three photons, successfully identifying images from several well-known datasets.

This paves the way towards future applications of quantum AI in complex image recognition, and represents a step toward low-resource, energy-efficient quantum computing.