José Antonio Marmolejo-Saucedo (ed.); Pandian Vasant (ed.); Igor Litvinchev (ed.); Roman Rodriguez-Aguilar (ed.); Martinez-R Springer (2021) Saatavuus: Tilaustuote Pehmeäkantinen kirja
José Antonio Marmolejo-Saucedo (ed.); Pandian Vasant (ed.); Igor Litvinchev (ed.); Roman Rodríguez-Aguilar (ed.); Saucedo-Ma Springer (2021) Saatavuus: Tilaustuote Pehmeäkantinen kirja
José Antonio Marmolejo-Saucedo (ed.); Idalia Flores De La Mota (ed.); Roman Rodríguez-Aguilar (ed.); Lilia Marmolejo-Saucedo Springer (2024) Saatavuus: Tulossa! Kovakantinen kirja
Pandian Vasant (ed.); Igor Litvinchev (ed.); Jose Antonio Marmolejo-Saucedo (ed.); Roman Rodriguez-Aguilar (ed.); Martinez-R Springer (2020) Saatavuus: Tilaustuote Kovakantinen kirja
Pandian Vasant; Gerhard-Wilhelm Weber; J. Joshua Thomas; José Antonio Marmolejo-Saucedo; Roman Rodriguez-Aguilar John Wiley & Sons Inc (2022) Saatavuus: Tilaustuote Kovakantinen kirja
Pandian Vasant (ed.); Igor Litvinchev (ed.); Jose Antonio Marmolejo-Saucedo (ed.); Roman Rodriguez-Aguilar (ed.); Martinez-R Springer (2021) Saatavuus: Tilaustuote Pehmeäkantinen kirja
Pandian Vasant (ed.); Gerhard-Wilhelm Weber (ed.); José Antonio Marmolejo-Saucedo (ed.); Elias Munapo (ed.); J. Josh Thomas Springer (2022) Saatavuus: Tilaustuote Pehmeäkantinen kirja
Taylor & Francis Ltd Sivumäärä: 208 sivua Asu: Kovakantinen kirja Julkaisuvuosi: 2024, 23.04.2024 (lisätietoa) Kieli: Englanti
This book highlights the use of explainable artificial intelligence (XAI) for healthcare problems, in order to improve trustworthiness, performance and sustainability levels in the context of applications.
Explainable Artificial Intelligence (XAI) in Healthcare adopts the understanding that AI solutions should not only have high accuracy performance, but also be transparent, understandable and reliable from the end user's perspective. The book discusses the techniques, frameworks, and tools to effectively implement XAI methodologies in critical problems of healthcare field. The authors offer different types of solutions, evaluation methods and metrics for XAI and reveal how the concept of explainability finds a response in target problem coverage. The authors examine the use of XAI in disease diagnosis, medical imaging, health tourism, precision medicine and even drug discovery. They also point out the importance of user perspectives and value of the data used in target problems. Finally, the authors also ensure a well-defined future perspective for advancing XAI in terms of healthcare.
This book will offer great benefits to students at the undergraduate and graduate levels and researchers. The book will also be useful for industry professionals and clinicians who perform critical decision-making tasks.