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Your search keyword '"Rezazadegan D"' showing total 2 results
Start Over Author "Rezazadegan D" Topic artificial intelligence
2 results on '"Rezazadegan D"'

1. Envisioning an artificial intelligence documentation assistant for future primary care consultations: A co-design study with general practitioners.

Author

Kocaballi AB, Ijaz K, Laranjo L, Quiroz JC, Rezazadegan D, Tong HL, Willcock S, Berkovsky S, and Coiera E

Subjects
Attitude to Computers, Decision Making, Computer-Assisted, Electronic Health Records, Forecasting, Humans, Referral and Consultation, User-Computer Interface, Artificial Intelligence, Attitude of Health Personnel, Documentation trends, General Practitioners, Primary Health Care, Professional Autonomy
Abstract

Objective: The study sought to understand the potential roles of a future artificial intelligence (AI) documentation assistant in primary care consultations and to identify implications for doctors, patients, healthcare system, and technology design from the perspective of general practitioners., Materials and Methods: Co-design workshops with general practitioners were conducted. The workshops focused on (1) understanding the current consultation context and identifying existing problems, (2) ideating future solutions to these problems, and (3) discussing future roles for AI in primary care. The workshop activities included affinity diagramming, brainwriting, and video prototyping methods. The workshops were audio-recorded and transcribed verbatim. Inductive thematic analysis of the transcripts of conversations was performed., Results: Two researchers facilitated 3 co-design workshops with 16 general practitioners. Three main themes emerged: professional autonomy, human-AI collaboration, and new models of care. Major implications identified within these themes included (1) concerns with medico-legal aspects arising from constant recording and accessibility of full consultation records, (2) future consultations taking place out of the exam rooms in a distributed system involving empowered patients, (3) human conversation and empathy remaining the core tasks of doctors in any future AI-enabled consultations, and (4) questioning the current focus of AI initiatives on improved efficiency as opposed to patient care., Conclusions: AI documentation assistants will likely to be integral to the future primary care consultations. However, these technologies will still need to be supervised by a human until strong evidence for reliable autonomous performance is available. Therefore, different human-AI collaboration models will need to be designed and evaluated to ensure patient safety, quality of care, doctor safety, and doctor autonomy., (© The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Published
2020
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2. A review of uncertainty quantification in deep learning:techniques, applications and challenges

Author

Abdar, M. (Moloud), Pourpanah, F. (Farhad), Hussain, S. (Sadiq), Rezazadegan, D. (Dana), Liu, L. (Li), Ghavamzadeh, M. (Mohammad), Fieguth, P. (Paul), Cao, X. (Xiaochun), Khosravi, A. (Abbas), Acharya, U. R. (U. Rajendra), Makarenkov, V. (Vladimir), and Nahavandi, S. (Saeid)

Subjects
Artificial intelligence, Ensemble learning, Machine learning, Deep learning, Bayesian statistics, Uncertainty quantification
Abstract

Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of uncertainties during both optimization and decision making processes. They have been applied to solve a variety of real-world problems in science and engineering. Bayesian approximation and ensemble learning techniques are two widely-used types of uncertainty quantification (UQ) methods. In this regard, researchers have proposed different UQ methods and examined their performance in a variety of applications such as computer vision (e.g., self-driving cars and object detection), image processing (e.g., image restoration), medical image analysis (e.g., medical image classification and segmentation), natural language processing (e.g., text classification, social media texts and recidivism risk-scoring), bioinformatics, etc. This study reviews recent advances in UQ methods used in deep learning, investigates the application of these methods in reinforcement learning, and highlights fundamental research challenges and directions associated with UQ.

Published
2021

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