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When will the mist clear? On the Interpretability of Machine Learning for Medical Applications: a survey
Antonio-Jesús Banegas-Luna, Jorge Peña-García, Adrian Iftene, Fiorella Guadagni, Patrizia Ferroni, Noemi Scarpato, Fabio Massimo Zanzotto, Andrés Bueno-Crespo, Horacio Pérez-Sánchez
Mapping out the philosophical questions of AI and clinical practice in diagnosing and treating mental disorders.
In Journal of evaluation in clinical practice
Uusitalo Susanne, Tuominen Jarno, Arstila Valtteri
diagnosis, medical ethics, philosophy of medicine, progress
Diagnostic accuracy of a novel third generation esophageal capsule as a noninvasive detection method for Barrett's Esophagus: A pilot study.
In Journal of gastroenterology and hepatology ; h5-index 51.0
BACKGROUND AND AIM :
Duvvuri Abhiram, Desai Madhav, Vennelaganti Sreekar, Higbee April, Gorrepati Venkat Subhash, Dasari Chandra, Chandrasekar Viveksandeep Thoguluva, Vennalaganti Prashanth, Kohli Divyanshoo, Sathyamurthy Anjana, Rai Tarun, Sharma Prateek
Exploring the Potential of Artificial Intelligence and Machine Learning to Combat COVID-19 and Existing Opportunities for LMIC: A Scoping Review.
In Journal of primary care & community health
Naseem Maleeha, Akhund Ramsha, Arshad Hajra, Ibrahim Muhammad Talal
COVID-19, artificial intelligence, low middle-income countries, machine learning, pandemic
In Journal of biomedical optics
Nguyen Thanh, Bui Vy, Thai Anh, Lam Van, Raub Christopher, Chang Lin-Ching, Nehmetallah Georges
artificial intelligence, fluorescence imaging, microscopy
Artificial intelligence evaluating primary thoracic lesions has an overall lower error rate compared to veterinarians or veterinarians in conjunction with the artificial intelligence.
In Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association
Boissady Emilie, de La Comble Alois, Zhu Xiaojuan, Hespel Adrien-Maxence
computer vision-based decision support system, convolutional neural networks, deep learning, small animal thoracic radiology