Artificial Intelligence in Medicine

نقد و بررسی اجمالیArtificial Intelligence in Medicineکتاب افست زبان اصلی هوش مصنوعی در پزشکی چاپ رنگی | کاغذ تحریر 80 گرم | قطع رحلی در قلمرو همیشه در حال تکامل علوم پزشکی، هوش مصنوعی در پزشکی به عنوان یک راهنمای پیشرو ظاهر می‌شود و کاوش گسترده‌ای از قدرت تحول‌دهنده هوش مصنوعی (AI) ارائه می‌دهد. این کتاب که توسط متخصصان برجسته در این زمینه نوشته شده است، تلاش می کند تا شکاف بین درک نظری و کاربرد عملی را پر کند و سفری جامع را از طریق اصول اساسی، برنامه های کاربردی پیشرفته و تأثیر بالقوه هوش مصنوعی در چشم انداز پزشکی ارائه دهد.این کتاب سفری را از اصول بنیادین به برنامه‌های کاربردی پیشرفته آغاز می‌کند و دیدگاهی جامع در مورد ادغام هوش مصنوعی در جنبه‌های مختلف پزشکی ارائه می‌کند. با هدف روشنی برای پاسخگویی به محققان و پزشکان، این دامنه از تکنیک های اساسی هوش مصنوعی تا کاربردهای نوآورانه آنها در تشخیص بیماری، پیش بینی و مراقبت از بیمار گسترش می یابد.In the ever-evolving realm of healthcare, Artificial Intelligence in Medicine emerges as a trailblazing guide, offering an extensive exploration of the transformative power of Artificial Intelligence (AI). Crafted by leading experts in the field, this book sets out to bridge the gap between theoretical understanding and practical application, presenting a comprehensive journey through the foundational principles, cutting-edge applications, and the potential impact of AI in the medical landscape.This book embarks on a journey from foundational principles to advanced applications, presenting a holistic perspective on the integration of AI into diverse aspects of medicine. With a clear aim to cater to both researchers and practitioners, the scope extends from fundamental AI techniques to their innovative applications in disease detection, prediction, and patient care.Distinguished by its practical orientation, each chapter presents actionable workflows, making theoretical concepts directly applicable to real-world medical scenarios. This unique approach sets the book apart, making it an invaluable resource for learners and practitioners alike.PART 1. Foundations of AI in healthcare 1. Exploring deep learning approaches for cardiac arrhythmia diagnosis2. Neural networks and LDA-based machine learning framework for the early detection of breast cancer3. Advanced deep learning algorithms for early ocular disease detection using fundus imagesPART 2. Disease detection and diagnosis4. A vision transformer-based approach for brain tumor detection5. Early detection of skin cancer through human-computer collaboration6. Improved mass detection in mammogram images with Dual Tree Complex Wavelet Transform and Fourier Descriptors7. A deep learning-based model for early detection of COVID-19 using chest X-ray images8. Detection of seizure activity in fMRI images using deep learning techniquesPART 3. Disease prediction and public health9. Improving prediction accuracy for neo-adjuvant chemotherapy response in breast cancer through 3D image segmentation and deep learning techniques10. A machine learning predictive framework for diabetes management using blood parameters11. A combined neuro-fuzzy and Naive Bayes approach for swine flu disease prediction12. Enhancing decision-making in maternal public healthcare using a knowledge discovery-based predictive analytics frameworkPART 4. Patient care and enhancements13. Enhancing patient care and treatment through explainable AI: A gap analysis14. Improved medical image captioning for chest X-rays using a hybrid VGG-ELECTRA model15. Diagnosing Parkinson’s disease using a deep learning model based on electromyography sensors16. Enhancing heart disease prediction with Hybridized KNN-MOPSO algorithm