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<p class="MsoNormal" style="mso-margin-top-alt: auto; mso-margin-bottom-alt: auto;"><span style="font-size: 12.0pt; line-height: 107%; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; color: black; mso-themecolor: text1;">Next-Gen Healthcare: AI-Powered Medical Innovations surveys how AI is transforming medicine from core methods to clinical deployment, emphasizing that technological power must be balanced with resilience, safety, and ethics. Part I covers the foundations of trustworthy medical AI, including adversarial defenses, cross-population generalization, optimized fine-tuning, interpretability for high-stakes decisions, and frontiers like quantum neural networks, ending with large language models focused on error detection, privacy, and patient safety.</span></p><p class="MsoNormal" style="mso-margin-top-alt: auto; mso-margin-bottom-alt: auto; line-height: normal;"><span style="font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; color: black; mso-themecolor: text1;">Part II&#xa0; applies AI methods to real-world domains: early Alzheimer’s and dementia diagnosis, oncology via lymphoma response prediction, chronic disease management with diabetes stratification and retinopathy detection, and extensions into dermatology, obesity, and mental health. The book’s introduce safeguards first, then generalizable and cutting-edge methods, ending with disease-focused AI applications. Its philosophy that safety underpins innovation and clinical impact, offering both a map of today’s medical AI and a guide to future opportunities.</span></p>

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Next-Gen Healthcare

  • 书籍

  • 发表日期：

    2026年

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摘要

Next-Gen Healthcare: AI-Powered Medical Innovations surveys how AI is transforming medicine from core methods to clinical deployment, emphasizing that technological power must be balanced with resilience, safety, and ethics. Part I covers the foundations of trustworthy medical AI, including adversarial defenses, cross-population generalization, optimized fine-tuning, interpretability for high-stakes decisions, and frontiers like quantum neural networks, ending with large language models focused on error detection, privacy, and patient safety.

Part II  applies AI methods to real-world domains: early Alzheimer’s and dementia diagnosis, oncology via lymphoma response prediction, chronic disease management with diabetes stratification and retinopathy detection, and extensions into dermatology, obesity, and mental health. The book’s introduce safeguards first, then generalizable and cutting-edge methods, ending with disease-focused AI applications. Its philosophy that safety underpins innovation and clinical impact, offering both a map of today’s medical AI and a guide to future opportunities.