Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare
Abstract Machine learning (ML) and artificial intelligence (AI) algorithms have the potential to
derive insights from clinical data and improve patient outcomes. However, these highly …
derive insights from clinical data and improve patient outcomes. However, these highly …
Clinlabomics: leveraging clinical laboratory data by data mining strategies
X Wen, P Leng, J Wang, G Yang, R Zu, X Jia… - BMC …, 2022 - Springer
The recent global focus on big data in medicine has been associated with the rise of artificial
intelligence (AI) in diagnosis and decision-making following recent advances in computer …
intelligence (AI) in diagnosis and decision-making following recent advances in computer …
[HTML][HTML] Quod erat demonstrandum?-Towards a typology of the concept of explanation for the design of explainable AI
In this paper, we present a fundamental framework for defining different types of
explanations of AI systems and the criteria for evaluating their quality. Starting from a …
explanations of AI systems and the criteria for evaluating their quality. Starting from a …
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
Despite major advances in artificial intelligence (AI) research for healthcare, the deployment
and adoption of AI technologies remain limited in clinical practice. This paper describes the …
and adoption of AI technologies remain limited in clinical practice. This paper describes the …
Targeted validation: validating clinical prediction models in their intended population and setting
Clinical prediction models must be appropriately validated before they can be used. While
validation studies are sometimes carefully designed to match an intended population/setting …
validation studies are sometimes carefully designed to match an intended population/setting …
Evaluation of clinical prediction models (part 1): from development to external validation
Evaluating the performance of a clinical prediction model is crucial to establish its predictive
accuracy in the populations and settings intended for use. In this article, the first in a three …
accuracy in the populations and settings intended for use. In this article, the first in a three …
[HTML][HTML] Evaluation of uncertainty quantification methods in multi-label classification: A case study with automatic diagnosis of electrocardiogram
Artificial Intelligence (AI) use in automated Electrocardiogram (ECG) classification has
continuously attracted the research community's interest, motivated by their promising …
continuously attracted the research community's interest, motivated by their promising …
Assessing the effects of data drift on the performance of machine learning models used in clinical sepsis prediction
Background Data drift can negatively impact the performance of machine learning
algorithms (MLAs) that were trained on historical data. As such, MLAs should be …
algorithms (MLAs) that were trained on historical data. As such, MLAs should be …
Functional MRI in neuro-oncology: state of the art and future directions
L Pasquini, KK Peck, M Jenabi, A Holodny - Radiology, 2023 - pubs.rsna.org
Since its discovery in the early 1990s, functional MRI (fMRI) has been used to study human
brain function. One well-established application of fMRI in the clinical setting is the …
brain function. One well-established application of fMRI in the clinical setting is the …
[HTML][HTML] Machine learning applications in precision medicine: overcoming challenges and unlocking potential
Precision medicine, utilizing genomic and phenotypic data, aims to tailor treatments for
individual patients. However, successful implementation into clinical practice is challenging …
individual patients. However, successful implementation into clinical practice is challenging …