AIMC Topic: Humans

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Tackling inter-subject variability in smartwatch data using factorization models.

Scientific reports
Smartwatches enable longitudinal and continuous data acquisition. This has the potential to remotely monitor (changes) of the health of users. However, differences among subjects (inter-subject variability) limit a model to generalize to unseen subje...

Diagnostic technologies for neuroblastoma.

Lab on a chip
Neuroblastoma is an aggressive childhood cancer characterised by high relapse rates and heterogenicity. Current medical diagnostic methods involve an array of techniques, from blood tests to tumour biopsies. This process is associated with long-term ...

Development and validation of an improved volumetric breast density estimation model using the ResNet technique.

Biomedical physics & engineering express
. Temporal changes in volumetric breast density (VBD) may serve as prognostic biomarkers for predicting the risk of future breast cancer development. However, accurately measuring VBD from archived x-ray mammograms remains challenging. In a previous ...

Recent advances in applying machine learning to proton radiotherapy.

Biomedical physics & engineering express
.: In radiation oncology, precision and timeliness of both planning and treatment are paramount values of patient care. Machine learning has increasingly been applied to various aspects of photon radiotherapy to reduce manual error and improve the ef...

Assessing the impact of information on patient attitudes toward artificial intelligence-based clinical decision support (AI/CDS): a pilot web-based SMART vignette study.

Journal of medical ethics
BACKGROUND: It is increasingly recognised that the success of artificial intelligence-based clinical decision support (AI/CDS) tools will depend on physician and patient trust, but factors impacting patients' views on clinical care reliant on AI have...

When time is of the essence: ethical reconsideration of XAI in time-sensitive environments.

Journal of medical ethics
The objective of explainable artificial intelligence systems designed for clinical decision support (XAI-CDSS) is to enhance physicians' diagnostic performance, confidence and trust through the implementation of interpretable methods, thus providing ...

Materiality and practicality: a response to - are clinicians ethically obligated to disclose their use of medical machine learning systems to patients?

Journal of medical ethics
In his recent paper Hatherley discusses four reasons given to support mandatory disclosure of the use of machine learning technologies in healthcare, and provides counters to each of these reasons. While I agree with Hatherley's conclusion that such ...

Are clinicians ethically obligated to disclose their use of medical machine learning systems to patients?

Journal of medical ethics
It is commonly accepted that clinicians are ethically obligated to disclose their use of medical machine learning systems to patients, and that failure to do so would amount to a moral fault for which clinicians ought to be held accountable. Call thi...

An autoencoder learning method for predicting breast cancer subtypes.

PloS one
Heterogeneity of breast cancer poses several challenges for detection and treatment. With next-generation sequencing, we can now map the transcriptional profile of each patient's breast tissue, which has the potential for identifying and characterizi...

Advanced feature engineering in Acute:Chronic Workload Ratio (ACWR) calculation for injury forecasting in elite soccer.

PloS one
Controlling training monotony and monitoring external workload using the Acute:Chronic Workload Ratio (ACWR) is a common practice among elite soccer teams to prevent non-contact injuries. However, recent research has questioned whether ACWR offers su...