Assessing Trustworthy AI.

Best Practice: AI for Predicting Cardiovascular Risks

We have used and tested Z-Inspection® by evaluating a non invasive AI medical device which was designed to assist medical doctors in the diagnosis of cardiovascular diseases.

Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year. Over the past decade, several machine-learning techniques have been used for cardiovascular disease diagnosis and prediction. The potential of AI in cardiovascular medicine is high; however, ignorance of the challenges may overshadow its potential clinical impact

The assessment was conducted with the AI system already deployed and in use in several countries in Europe and in other parts of the world.

The work we performed was conducted purely for research interest and did not involve any compensation or personal benefits. The product in question was a non-invasive AI medical device that used machine learning to analyze sensor data (i.e. electrical signals of the heart) of patients to predict the risk of cardiovascular heart disease.

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 We started to assess this new use case:

Assessing Trustworthy AI.

Best Practice: Machine learning as a supportive tool to recognize cardiac arrest in emergency calls.

In cooperation with

Emergency Medical Services Copenhagen, Denmark
Department of Clinical Medicine, University of Copenhagen, Denmark

Emergency medical dispatchers fail to identify approximately 25% of cases of out of hospital cardiac arrest, thus loose the opportunity to provide the caller instructions in cardiopulmonary resuscitation.

 A team lead by Stig Nikolaj Blomberg (Emergency Medical Services Copenhagen, and Department of Clinical Medicine, University of Copenhagen, Denmark) examined whether a machine learning framework could recognize out-of-hospital cardiac arrest from audio files of calls to the emergency medical dispatch center.

The result of this analysis is published here.

We will work with Stig Nikolaj Blomberg and his team and apply Z-inspection®to assess the ethicaltechnical and legal implications of using machine learning in this context.

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Assessing Trustworthy AI. Best Practices

Z-Inspection® is tested with real use cases.

We will start in October 2020 to assess this new use case:

Assessing Trustworthy AI.

Best Practice: Deep Learning based Skin Lesion Classifiers.

In cooperation with

German Research Center for Artificial Intelligence GmbH (DFKI)

The team of Dr. Andreas Dengel at the German Research Center for Artificial Intelligence(DFKI) used a well-trained and high performing neural network for classification of three skin tumours, i.e. Melanocytic Naevi, Melanoma and Seborrheic Keratosis and performed a detailed analysis on its latent space.

The result of their work is available here: IJCNN_Interpretability (1)

We will work with Andreas Dengel and his team and apply our Z-inspection® process to assess the ethicaltechnical and legal implications of using Deep Learning in this context.

Learn more.