Machine Learning in Paediatrics and the Childs's Right to An Open Future

13 Pages Posted: 21 Dec 2022

Date Written: December 7, 2022


Machine Learning (ML)-driven diagnostic systems for mental and behavioural paediatric conditions can have profound implications for child development, children’s image of themselves and their prospects for social integration.

The use of machine learning (ML) in biomedical research, clinical practice and public health is set to radically transform medicine. Ethical challenges associated to such transformation are particularly salient in the case of vulnerable or dependent patients. One relatively neglected ethical issue in this space is the extent to which the clinical implementation of ML-based predictive analytics is bound to erode what philosopher Joel Feinberg has defined as children’s right to an open future.

An ethical analysis of how the unprecedented predictive power of ML diagnostic systems can affect a child’s right to an open future has not yet been undertaken. In this paper, I illustrate the right to an open future and explain its relevance in relation to diagnostic uses of ML in paediatric medicine, with a particular focus on Attention-Deficit/Hyperactivity Disorder and autism.

ML-based diagnostic tools focused on brain imaging run the risk of objectifying mental and behavioural conditions as brain abnormalities, even though the neuropathological mechanisms causing such abnormalities at the level of the brain are far from clear.

Gains in automating psychiatric diagnosis have to be weighed against the risks that ML-driven diagnoses may affect a child’s capacity to uphold a sense of self-worth and social integration.

Funding Declaration: The author wishes to acknowledge support from a number of research grants he is involved with as a principal investigator: the NRP77 project D-GOVmap (award grant number: 407740_187356) funded by the Swiss National Science Foundation, the ERA-NET Neuron project ‘BEAD’ (award grant number: 10NE17_199434), the Botnar Fundation grant “Enabling Digital Health Promotion in LMICs” (award grant number: N/A).

Conflict of Interests: None.

Keywords: Machine learning, artificial intelligence, diagnosis, paediatrics, autism, ADHD, ethics, right to an open future, autonomy, self-determination

Suggested Citation

Blasimme, Alessandro, Machine Learning in Paediatrics and the Childs's Right to An Open Future (December 7, 2022). Available at SSRN: or

Alessandro Blasimme (Contact Author)

ETH Zurich ( email )

Universitätstrasse 2
Zurich, 8092

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