iToBoS project aims to provide new opportunities and added value to society in terms of novel health solutions, patient care, innovation, technical improvements and economic development.

Until a few years ago, regulations and standards existed around the handling and use of certain types of data, including the General Data Protection Regulation (GDPR)[1] in Europe, HIPAA[2] and PCI-DSS[3] in the United States, the Canadian Consumer Privacy Protection Act (CPPA)[4] and many more.

In iToBoS, machine learning/ artificial intelligence is key to combine all the design and make the system really a standout product.

We present an overview of the online datasets meant for machine learning algorithms. As the saying goes, “An algorithm can be only as good as the data set”. 

Melanoma has a poor prognosis with median survival of 6-9 months in the absence of timely diagnosis and treatment.

In any data processing project that deals with personal information there is an inherent tradeoff between safeguarding data subjects’ privacy and yielding useful and accurate insights from the data.

The transparency of Artificial Intelligence (AI) models is an essential criterion for the deployment of AI in high-risk settings, such as medical applications. Consequently, numerous approaches for explaining AI systems have been proposed over the years (Samek et al., 2021).

In this blogpost we want to talk about color from a technical perspective. As you might already know, dermatologists use the ABCDE-criterium for the diagnosis of melanoma.

L’intelligence artificielle (l’IA) soulève aussi bien des questionnements philosophiques qu’informatiques, chacun a sa petite idée au sujet des IA. Mais qu’en savons-nous vraiment? 

The work "Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvement", supported by iToBoS project, has been published.