[HTML][HTML] The false hope of current approaches to explainable artificial intelligence in health care
M Ghassemi, L Oakden-Rayner… - The Lancet Digital Health, 2021 - thelancet.com
The black-box nature of current artificial intelligence (AI) has caused some to question
whether AI must be explainable to be used in high-stakes scenarios such as medicine. It has …
whether AI must be explainable to be used in high-stakes scenarios such as medicine. It has …
A review of the role of artificial intelligence in healthcare
A Al Kuwaiti, K Nazer, A Al-Reedy, S Al-Shehri… - Journal of personalized …, 2023 - mdpi.com
Artificial intelligence (AI) applications have transformed healthcare. This study is based on a
general literature review uncovering the role of AI in healthcare and focuses on the following …
general literature review uncovering the role of AI in healthcare and focuses on the following …
Capabilities of gpt-4 on medical challenge problems
H Nori, N King, SM McKinney, D Carignan… - arXiv preprint arXiv …, 2023 - arxiv.org
Large language models (LLMs) have demonstrated remarkable capabilities in natural
language understanding and generation across various domains, including medicine. We …
language understanding and generation across various domains, including medicine. We …
Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations
Artificial intelligence (AI) systems have increasingly achieved expert-level performance in
medical imaging applications. However, there is growing concern that such AI systems may …
medical imaging applications. However, there is growing concern that such AI systems may …
A transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics
During the diagnostic process, clinicians leverage multimodal information, such as the chief
complaint, medical images and laboratory test results. Deep-learning models for aiding …
complaint, medical images and laboratory test results. Deep-learning models for aiding …
Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images
Computer-aided diagnosis for the reliable and fast detection of coronavirus disease (COVID-
19) has become a necessity to prevent the spread of the virus during the pandemic to ease …
19) has become a necessity to prevent the spread of the virus during the pandemic to ease …
Artificial intelligence in healthcare: review, ethics, trust challenges & future research directions
The use of artificial intelligence (AI) in medicine is beginning to alter current procedures in
prevention, diagnosis, treatment, amelioration, cure of disease and other physical and …
prevention, diagnosis, treatment, amelioration, cure of disease and other physical and …
A deep learning based model for the detection of pneumonia from chest X-ray images using VGG-16 and neural networks
Pneumonia is a viral infection which affects a significant proportion of individuals, especially
in developing and penurious countries where contamination, overcrowded, and unsanitary …
in developing and penurious countries where contamination, overcrowded, and unsanitary …
Convolutional neural networks in medical image understanding: a survey
DR Sarvamangala, RV Kulkarni - Evolutionary intelligence, 2022 - Springer
Imaging techniques are used to capture anomalies of the human body. The captured images
must be understood for diagnosis, prognosis and treatment planning of the anomalies …
must be understood for diagnosis, prognosis and treatment planning of the anomalies …
Unsolved problems in ml safety
Machine learning (ML) systems are rapidly increasing in size, are acquiring new
capabilities, and are increasingly deployed in high-stakes settings. As with other powerful …
capabilities, and are increasingly deployed in high-stakes settings. As with other powerful …