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Arrow Electronics
White Papers
Connected Medical Devices Transform Patient Care
The March/April 2025 issue of Electronic Products Magazine examines the technologies transforming modern healthcare. Covering advancements in medical imaging and diagnostic devices, along with the influence of AI, sensors, and edge computing, this issue provides essential insights for engineers developing next-generation medical solutions. Featuring expert commentary, product highlights, and design strategies, this edition empowers innovators with the necessary tools and context to accelerate development in diagnostics, therapy, and patient monitoring systems.
KI in der medizinischen Bildgebung: Bewältigung wichtiger diagnostischer Herausforderungen
Daten des Gesundheitswesens machen derzeit 30 % des weltweiten Datenvolumens, wobei Prognosen bis 2025 einen Anstieg auf 36 % erwarten lassen. Die wichtigsten Quellen dieser multimodalen medizinischen Daten sind die medizinische Bildgebung (MRT, CT, PET), die Genomik, die Proteomik, die Sensordaten sogenannter Wearables und unstrukturierte elektronische Gesundheitsaufzeichnungen (Electronic Health Records, EHRs). Die inhärente Heterogenität und hohe Dimensionalität dieser Daten stellen erhebliche Herausforderungen bei der klinischen Interpretation dar, insbesondere wenn es darum geht, umsetzbare Erkenntnisse aus allen Modalitäten zu gewinnen.
Intelligenza Artificiale Nell’imaging Medico: Come Superare Le Principali Sfide Della Diagnostica
Attualmente, i dati sanitari rappresentano il 30% del volume di dati mondiale, con proiezioni che suggeriscono un aumento fino al 36% entro il 2025. Questi dati medici multimodali provengono principalmente dall'imaging medico (RM, TC, PET), dalla genomica, dalla proteomica, da sensori indossabili e da registrazioni di dati clinici non strutturate. L'eterogeneità intrinseca e l'elevata dimensionalità di questi dati presentano sfide significative nell'interpretazione clinica, soprattutto quando si mira a estrarre informazioni utili tra le diverse modalità.
AI in Medical Imaging: Overcoming Key Diagnostic Challenges
This whitepaper examines how engineers can utilize AI and Generative AI in oncology imaging systems to enhance early detection, automate analysis, and facilitate predictive diagnostics. It addresses challenges such as image variability, data fusion, and computational complexity, while detailing system-level architecture and implementation considerations. Engineers will gain practical insights into scalable hardware design, deep learning model integration, and the future of AI-augmented radiology.