LEADER 04333nam 22006495 450 001 9911010529303321 005 20250618124744.0 010 $a3-031-90174-6 024 7 $a10.1007/978-3-031-90174-4 035 $a(CKB)39331811400041 035 $a(MiAaPQ)EBC32162276 035 $a(Au-PeEL)EBL32162276 035 $a(DE-He213)978-3-031-90174-4 035 $a(OCoLC)1525850320 035 $a(EXLCZ)9939331811400041 100 $a20250618d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aArtificial Intelligence-Empowered Bio-medical Applications $eChallenges, Solutions and Development Guidelines /$fby Dimitrios P. Panagoulias, George A. Tsihrintzis, Maria Virvou 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (320 pages) 225 1 $aLearning and Analytics in Intelligent Systems,$x2662-3455 ;$v49 311 08$a3-031-90173-8 327 $aIntroduction to AI-empowered Medical Soft [1] ware: Recent Advances and Challen -- Personalized Nutrition Applications using Biomarkers and Machine Learning -- Blood Exam Classification for Predicting Defin [1] ing Factors in Metabolic Syndrome Diagnosis using Sup [1] port Vector Machine -- Extreme Value Analysis applied in Dietary Data -- Iterative Microservices Approach for Explain [1] able and Reliable AI in Medical Application -- Challenges in Regulating and Validating AI [1] Driven Healthcar -- Framework for AI Explainability Leveraging User Acceptance and Health Literacy Models. 330 $aThe book delves into advancements in personalized medicine, highlighting the transition from generalized treatments to tailored strategies through AI and machine learning. It first emphasizes the role of biomarkers in training predictive models and neural networks, enhancing disease diagnosis and patient management. It then explores AI-driven healthcare systems, particularly the use of microservices to improve scalability and management. Additionally, it examines regulatory challenges, the need for AI explainability, and the PINXEL framework, which defines explainability requirements using the technology acceptance model (TAM) and the diffusion of innovation theory (DOI). Furthermore, the book evaluates the capabilities of large language models, including ChatGPT and GPT-4V, in medical applications, with a focus on diagnosis and structured assessments in general pathology. Lastly, it introduces an AI-powered system for primary care diagnosis that integrates language models, machine learning, and rule-based systems. The interactive AI assistants ?Med/Primary AI assistant? and ?Dermacen Analytica? leverage natural language processing, image analysis, and multi-modal AI to enhance patient interactions and provide healthcare professionals with high-accuracy, personalized diagnostic support. By taking a holistic approach, the book underscores the integration of AI into healthcare, aiming to support medical professionals in patient diagnosis and management with precision and adaptability. 410 0$aLearning and Analytics in Intelligent Systems,$x2662-3455 ;$v49 606 $aBiomedical engineering 606 $aEngineering$xData processing 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aBiomedical Engineering and Bioengineering 606 $aData Engineering 606 $aComputational Intelligence 606 $aArtificial Intelligence 615 0$aBiomedical engineering. 615 0$aEngineering$xData processing. 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 14$aBiomedical Engineering and Bioengineering. 615 24$aData Engineering. 615 24$aComputational Intelligence. 615 24$aArtificial Intelligence. 676 $a610.28 700 $aPanagoulias$b Dimitrios P$01828714 701 $aTsihrintzis$b George A$0739887 701 $aVirvou$b Maria$0720958 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911010529303321 996 $aArtificial Intelligence-Empowered Bio-Medical Applications$94397628 997 $aUNINA