Machine learning for healthcare wearable devices: the big picture
Using artificial intelligence and machine learning techniques in healthcare applications has
been actively researched over the last few years. It holds promising opportunities as it is …
been actively researched over the last few years. It holds promising opportunities as it is …
[HTML][HTML] Public covid-19 x-ray datasets and their impact on model bias–a systematic review of a significant problem
Computer-aided-diagnosis and stratification of COVID-19 based on chest X-ray suffers from
weak bias assessment and limited quality-control. Undetected bias induced by inappropriate …
weak bias assessment and limited quality-control. Undetected bias induced by inappropriate …
A flexible microfluidic chip-based universal fully integrated nanoelectronic system with point-of-care raw sweat, tears, or saliva glucose monitoring for potential …
M Sun, X Pei, T Xin, J Liu, C Ma, M Cao… - Analytical …, 2022 - ACS Publications
By combining the distinctive noninvasive feature with the peculiar complete functional
implementation trait, fully integrated raw noninvasive biofluid glucose biosensors offer active …
implementation trait, fully integrated raw noninvasive biofluid glucose biosensors offer active …
Robotics applications in facial plastic surgeries
E Tokgöz, MA Carro - Cosmetic and reconstructive facial plastic surgery: A …, 2023 - Springer
Robotics applications in facial plastic surgery has been practical and observed to have
benefits. For instance, minimal invasiveness, small number of incisions, less blood loss …
benefits. For instance, minimal invasiveness, small number of incisions, less blood loss …
The AIMe registry for artificial intelligence in biomedical research
The AIMe registry for artificial intelligence in biomedical research | Nature Methods Skip to
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Machine-learning versus traditional approaches for atherosclerotic cardiovascular risk prognostication in primary prevention cohorts: a systematic review and meta …
Background Cardiovascular disease (CVD) risk prediction is important for guiding the
intensity of therapy in CVD prevention. Whilst current risk prediction algorithms use …
intensity of therapy in CVD prevention. Whilst current risk prediction algorithms use …
[HTML][HTML] Prevalence and predicting factors of perceived stress among Bangladeshi university students using machine learning algorithms
Background Stress-related mental health problems are one of the most common causes of
the burden in university students worldwide. Many studies have been conducted to predict …
the burden in university students worldwide. Many studies have been conducted to predict …
[HTML][HTML] Patient and public involvement to build trust in artificial intelligence: a framework, tools, and case studies
Artificial intelligence (AI) is increasingly taking on a greater role in healthcare. However,
hype and negative news reports about AI abound. Integrating patient and public involvement …
hype and negative news reports about AI abound. Integrating patient and public involvement …
[HTML][HTML] Machine learning use for prognostic purposes in multiple sclerosis
The course of multiple sclerosis begins with a relapsing-remitting phase, which evolves into
a secondarily progressive form over an extremely variable period, depending on many …
a secondarily progressive form over an extremely variable period, depending on many …
[HTML][HTML] Multi-institutional prognostic modeling in head and neck cancer: evaluating impact and generalizability of deep learning and radiomics
Artificial intelligence (AI) and machine learning (ML) are becoming critical in developing and
deploying personalized medicine and targeted clinical trials. Recent advances in ML have …
deploying personalized medicine and targeted clinical trials. Recent advances in ML have …