| Judul | AI-Assisted Medical Diagnostics / Milan Toma |
| Pengarang | Milan Toma |
| Penerbitan | 4052 Basel, Switzerland : MDPI, 2026 |
| Deskripsi Fisik | 260 :ill |
| ISBN | 978-3-7258-9058-3 |
| Subjek | Medicine & Pharmacology; Artificial intelligence--Medical applications |
| Catatan | "Summary This Reprint highlights the potential of artificial intelligence in medical diagnostics and healthcare delivery. It features research at the intersection of AI-driven computational methods and clinical practice, focusing on innovative solutions that improve diagnostic accuracy, patient monitoring, and therapeutic decision-making across various specialties. The collection covers diverse applications of machine learning and deep learning in healthcare. Featured studies show how AI algorithms enhance early disease detection, enable personalized risk assessment, and support clinicians in making more informed decisions. Practical implementations include advanced diagnostic tools for ophthalmology, diabetes prediction, neurological assessment, and real-time patient monitoring systems for home care and dementia management. A key emphasis is on explainable AI and the importance of transparency, trustworthiness, and accountability in clinical systems. The Reprint bridges theoretical frameworks with real-worl |
| Bentuk Karya | Tidak ada kode yang sesuai |
| Target Pembaca | Tidak ada kode yang sesuai |
| Lokasi Akses Online |
https://www.mdpi.com/books/reprint/13524-ai-assisted-medical-diagnostics |
| No Barcode | No. Panggil | Akses | Lokasi | Ketersediaan |
|---|---|---|---|---|
| 450626192 | 616.075 028 5 Mil a | Baca Online | Perpustakaan Pusat - Online Resources Ebook |
Tersedia |
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| 505 | # | # | $a "Summary This Reprint highlights the potential of artificial intelligence in medical diagnostics and healthcare delivery. It features research at the intersection of AI-driven computational methods and clinical practice, focusing on innovative solutions that improve diagnostic accuracy, patient monitoring, and therapeutic decision-making across various specialties. The collection covers diverse applications of machine learning and deep learning in healthcare. Featured studies show how AI algorithms enhance early disease detection, enable personalized risk assessment, and support clinicians in making more informed decisions. Practical implementations include advanced diagnostic tools for ophthalmology, diabetes prediction, neurological assessment, and real-time patient monitoring systems for home care and dementia management. A key emphasis is on explainable AI and the importance of transparency, trustworthiness, and accountability in clinical systems. The Reprint bridges theoretical frameworks with real-world applications, exploring how rule-based and modern AI approaches combine to create interpretable, reliable diagnostic tools. Crucially, the Reprint also addresses the challenges and limitations of AI in healthcare, providing critical evaluation of current technologies and examining diagnostic variability. It underscores the need for rigorous validation and responsible implementation, ensuring readers appreciate both AI’s promise and the requirements for safe clinical adoption." |
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