Quantum dots represent a transformative nanotechnology platform with significant potential in medical applications. These semiconductor nanocrystals function as fluorescent biological labels, enabling advanced diagnostic and therapeutic capabilities across multiple disease areas, particularly oncology and neurodegenerative disorders such as Alzheimer's. Recent research demonstrates their utility in cancer detection, tumor imaging, and targeted therapy approaches. The technology has evolved substantially since early fluorescent imaging applications, with ongoing developments in quantum computing enhancing diagnostic precision. However, clinical translation faces challenges related to toxicology, physicochemical properties, and environmental factors affecting biocompatibility. Current investigations focus on optimizing quantum dot formulations for improved safety profiles and therapeutic efficacy, positioning this emerging technology as a promising frontier in precision medicine and personalized healthcare solutions.

Researchers have developed an innovative droplet microfluidic platform to accelerate antibiotic discovery from complex environmental microbial communities. The approach combines bacterial cultivation in picoliter droplets with multiplexed phenotypic screening using dual fluorescently labeled reporter strains representing both Gram-positive and Gram-negative bacteria. By simultaneously monitoring independent survival signals, the system efficiently detects and isolates bioactive compounds with diverse inhibition profiles. Proof-of-concept studies successfully identified a model Streptomyces strain and screened soil-derived microbial communities, demonstrating the platform's practical utility. This advancement addresses the critical gap in antibiotic discovery pipelines, offering a powerful tool to combat rising antimicrobial resistance through rapid screening of environmental samples for novel bioactive compounds with varied mechanisms of action.

Urinary tract infections caused by E. coli represent a significant clinical burden, compounded by rising antimicrobial resistance that demands rapid pathogen identification and susceptibility testing. This study presents an innovative smartphone-integrated electrochemical platform capable of detecting E. coli within thirty minutes and simultaneously assessing susceptibility to four antibiotics in five hours—substantially faster than conventional methods requiring two to five days. The portable device utilizes an indium tin oxide-based immunosensor functionalized with gold nanoparticles and monoclonal antibodies, achieving sensitive detection across clinically relevant bacterial concentrations. Integration of microfluidic reservoirs and on-chip heating enables controlled bacterial incubation with real-time electrochemical analysis, offering a compact, multiplexed diagnostic solution to address antimicrobial resistance and improve clinical outcomes for UTI patients.

Biosensors represent a transformative approach to detecting multiple respiratory viral pathogens simultaneously, offering significant advantages over traditional diagnostic methods. This comprehensive review examines the latest advancements in multiplex biosensor technology, published in Archives of Microbiology in June 2026. The article explores how biosensors enable rapid, accurate, and cost-effective identification of various respiratory viruses in clinical settings. By integrating multiple detection capabilities into single platforms, these tools enhance diagnostic efficiency and support timely clinical decision-making. The review provides valuable insights for researchers and healthcare professionals seeking innovative solutions to improve respiratory pathogen detection and disease management protocols.

Cancer represents a significant global health challenge, claiming approximately ten million lives annually with disproportionate mortality in resource-limited regions lacking adequate diagnostic infrastructure. Traditional oncology approaches, relying on invasive tissue-based diagnosis and imaging, are constrained by time intensity and inability to capture tumor heterogeneity. The observation that early-stage cancers demonstrate substantially improved survival rates has catalyzed a shift toward precision oncology, integrating molecular profiling with clinical and imaging data. This transformation is driven by two converging technological advances: highly sensitive, non-invasive biomarker platforms and exponential artificial intelligence development. Digital biomarkers, derived from biological samples, medical imaging, or wearable devices, enable continuous non-invasive monitoring through liquid biopsy and imaging analysis. Machine learning integration with these biomarkers transitions oncology from reactive to proactive, extracting actionable insights from complex biological data to enhance early detection and treatment selection.

Quantum nanomaterials, particularly semiconductor and carbon quantum dots, are transforming precision medicine through their exceptional optical, electronic, and magnetic properties derived from quantum confinement effects. These materials enable diagnostic and therapeutic applications at the molecular and subcellular level, offering size-dependent fluorescence, sharp emission spectra, and ultra-high sensitivity. In diagnostics, quantum dots facilitate early disease detection through sensitive biomarker analysis, supporting personalized medicine approaches. Therapeutically, they serve as drug and gene carriers, photosensitizers for photodynamic and photothermal therapies, and multifunctional theranostic platforms integrating both imaging and treatment modalities. Their unique properties position quantum nanomaterials as invaluable tools for advancing modern medical practice.

# Summary Researchers have developed an innovative single-tube asymmetric ERA-CRISPR/Cas12a diagnostic system that represents a significant advancement in molecular detection technology. This integrated approach leverages the synergistic benefits of enzymatic amplification and CRISPR-based detection to substantially improve diagnostic sensitivity while simultaneously minimizing false-positive results. The streamlined single-tube format enhances practical applicability and reduces operational complexity. By addressing key limitations of existing CRISPR-based diagnostic methods, this development substantially expands their utility across diverse clinical and research applications, particularly in resource-limited settings where simplicity and reliability are paramount.

Nucleic acid-based biosensing represents a transformative frontier in diagnostic technology, offering unprecedented sensitivity and specificity for detecting biological targets. This comprehensive review examines cutting-edge advances in leveraging DNA and RNA molecules as biosensing elements, exploring innovative approaches that enhance detection capabilities across medical, environmental, and industrial applications. The integration of nanotechnology with nucleic acid systems enables miniaturized devices with improved performance metrics. These developments promise to revolutionize disease diagnosis, pathogen detection, and biomarker identification, while reducing analysis time and costs. The open-access publication contributes valuable insights to the scientific community, advancing understanding of nucleic acid biosensing's potential to address critical healthcare and research challenges.

Pancreatic ductal adenocarcinoma remains a particularly lethal cancer due to diagnostic delays and nonspecific symptoms. Liquid biopsy shows promise as a noninvasive screening tool, yet sample heterogeneity poses significant challenges. Machine learning and artificial intelligence techniques offer solutions by identifying high-value biomarkers from complex datasets. This systematic review examined 18 studies following PRISMA-ScR guidelines, analyzing AI-powered liquid biopsy approaches for early PDAC detection. Blood samples dominated the research (15 studies), while random forests and support vector machines emerged as the most utilized algorithms. Deep learning methods received limited exploration. Significant limitations include inconsistent reporting of model performance metrics and small cohort sizes, highlighting the need for standardized methodologies in this emerging field.

AOA Dx has developed a promising lipid-protein biomarker diagnostic for early ovarian cancer detection, addressing a significant gap in women's health innovation. Co-founder Anna Jeter highlights that women's health has been historically underinvestigated despite representing half the population, with ovarian cancer diagnostics remaining largely unchanged since the introduction of CA-125 in 1987. Early detection is critical, as over 90% of women survive stage 1 ovarian cancer, yet approximately 80% of cases are diagnosed at advanced stages. Through a partnership with McGill University, the company investigated multi-omics approaches to identify biological signals missed by existing diagnostics, representing a meaningful advancement in oncology diagnostics and precision medicine for women's health.

The FDA regulates artificial intelligence and machine learning-based Software as Medical Devices (SaMD) under a standardized risk-based framework applicable to all medical devices. In 2024, the FDA cleared 168 AI/ML-enabled devices, all classified as Class II (moderate-risk), predominantly through the 510(k) pathway. These SaMD applications span diagnostics, quantification, and triage functions, yet none have achieved Class III high-risk status to date. The FDA's regulatory approach prioritizes premarket safety and effectiveness review of AI algorithms while transitioning toward comprehensive product lifecycle management, including postmarket algorithm modifications. Software functioning in clinical diagnosis, treatment, or patient management qualifies as regulated medical devices unless explicitly exempted, and follows established authorization pathways based on assessed risk levels.

The FDA has established a comprehensive regulatory framework for artificial intelligence and machine learning-enabled software as medical devices, with over 1,400 such devices currently authorized for U.S. marketing as of December 2025. Most devices enter through the 510(k) pathway as Class II devices, while some utilize De Novo or premarket approval routes. The agency's newly finalized framework for Predetermined Change Control Plans represents a significant shift, requiring companies to transition from passive monitoring to proactive planning strategies. This evolving regulatory approach reflects the FDA's deliberate effort to create coherent governance mechanisms that address the unique challenges posed by AI/ML technologies throughout the complete product lifecycle, ensuring both innovation and safety in the medical device sector.

Organoids represent miniature three-dimensional tissue and organ models derived from stem cells that replicate structural and functional characteristics of their in vivo counterparts. Since the first intestinal organoid's development in 2009, organoid technology has advanced significantly, encompassing diverse tissue types including brain, liver, kidney, pancreas, retina, prostate, and mammary gland organoids. This comprehensive review traces organoid development milestones from 2009 to 2023, documenting breakthroughs in both human and animal tissue engineering. The progression demonstrates organoid models' expanding potential for disease modeling and investigational drug development, establishing them as valuable tools in pharmaceutical regulation and translational research applications.

Artificial intelligence is rapidly transforming clinical practice, with algorithms now detecting cancer in medical images, predicting patient deterioration, and automating clinical documentation across hospitals and general practices. This chapter examines whether AI represents genuine healthcare transformation or overpromised technology by exploring technical foundations of artificial intelligence and machine learning, analyzing specific clinical applications, addressing implementation challenges, navigating the evolving regulatory landscape, and confronting ethical considerations. The content builds on health data analytics fundamentals to explain how AI systems are trained and validated, using the analogy of how junior doctors learn to recognize patterns in medical imaging under experienced supervision to illustrate machine learning principles applicable to healthcare settings.

Drug screening based on in-vitro primary tumor cell culture has demonstrated potential in personalized cancer diagnosis. However, the limited number of tumor cells, especially from patients with early stage cancer, has hindered the widespread application of this technique. Hence, we developed a digital microfluidic system for drug screening using primary tumor cells and established a working protocol for precision medicine. Smart control logic was developed to increase the throughput of the system and decrease its footprint to parallelly screen three drugs on a 4 × 4 cm2 chip in a device measuring 23 × 16 × 3.5 cm3. We validated this method in an MDA-MB-231 breast cancer xenograft mouse model and liver cancer specimens from patients, demonstrating tumor suppression in mice/patients treated with drugs that were screened to be effective on individual primary tumor cells.

Analyser des cellules directement dans le corps d’un patient afin d’établir, de manière peu invasive, un diagnostic de cancer. Cet examen du futur réalisé à l’aide de capteurs multifonctions placés à l’extrémité d’une fibre optique est l’objectif de recherche d’une équipe multidisciplinaire.

Biosensor technologies are increasingly seen as important tools in healthcare diagnostics, especially for point-of-care (POC) and decentralized testing. A systematic comparison of sensing methods, the scattered integration of nanomaterials into unified designs, and the neglect of real-world usability factors like device miniaturization, user interface integration, and data sharing continue to be issues despite rapid advancements. This review critically examines recent progress in electrochemical, fluorescence, and colorimetric methods, with a special focus on signal amplification strategies using nanomaterials and their feasibility for real-world use.

Salivary biomarkers enable non-invasive diagnosis and monitoring of oral diseases. Saliva omits and point-of-care tools improve sensitivity and clinical feasibility. Multi-omics and artificial intelligence drive future precision oral diagnostics.

This research explores the development and application of flexible polymeric materials and wearable biosensors for advanced diabetes mellitus diagnostics and continuous monitoring. The study addresses the critical need for non-invasive, real-time glucose monitoring solutions by leveraging innovative material science and sensor technology. Flexible polymeric substrates offer biocompatibility and mechanical adaptability, enabling seamless integration into wearable devices worn on the skin. These smart biosensors demonstrate potential for improving patient outcomes through enhanced diagnostic accuracy and personalized disease management. The investigation represents a significant advancement in point-of-care diagnostics and remote patient monitoring, with implications for transforming diabetes care delivery and enabling better disease surveillance in clinical and home settings.

This review examines nucleic acid-based point-of-care diagnostics for infectious disease detection, addressing the critical need for decentralized, accessible testing solutions. The article comprehensively explores three operational components: nucleic acid extraction, amplification, and detection across diverse platforms including lateral flow assays, biochips, and biosensors. It evaluates extraction strategies such as magnetic bead-based, paper-based, and integrated microfluidic approaches that prioritize simplicity and equipment-free operation. The review analyzes key amplification methodologies including PCR, NASBA, RPA, and LAMP, highlighting their modifications for rapid, low-power, and portable applications. These technologies collectively represent advancements in delivering sensitive, selective, and economically viable diagnostic testing outside traditional laboratory settings, enhancing global infectious disease surveillance and response capabilities.