Technology has advanced enormously in the 30 years since the introduction of the first Children Act, which shaped the UK's system of child safeguarding. Today a computer-generated analysis – "machine learning" that produces predictive analytics – can help social workers assess the probability of a child coming on to the at-risk register. It can also help show how they might prevent that happening. But with technological advances come dilemmas unimaginable back in 1989. Is it right for social workers to use computers to help promote the welfare of children in need?

A machine learning algorithm can detect signs of anxiety and depression in the speech patterns of young children, potentially providing a fast and easy way of diagnosing conditions that are difficult to spot and often overlooked in young people, according to new research published in the Journal of Biomedical and Health Informatics. Around one in five children suffer from anxiety and depression, collectively known as "internalizing disorders." But because children under the age of eight can't reliably articulate their emotional suffering, adults need to be able to infer their mental state, and recognise potential mental health problems. Waiting lists for appointments with psychologists, insurance issues, and failure to recognise the symptoms by parents all contribute to children missing out on vital treatment. "We need quick, objective tests to catch kids when they are suffering," says Ellen McGinnis, a clinical psychologist at the University of Vermont Medical Center's Vermont Center for Children, Youth and Families and lead author of the study.

The study conducted by researchers at the University of Vermont in the USA suggests a machine learning algorithm might provide a fast and easy way of diagnosing anxiety and depression – conditions that are difficult to spot and often overlooked in young people. "We need quick, objective tests to catch kids when they are suffering," said study lead author Ellen McGinnis, who is a clinical psychologist at the university's Medical Centre's Vermont Centre for Children, Youth and Families. "The majority of kids under eight are undiagnosed," she added. Early diagnosis of these conditions is critical as children respond well to treatment while their brains are still developing, according to the researchers, but if they are left untreated they are at greater risk of substance abuse and suicide later in life. Standard diagnosis involves a 60-90-minute semi-structured interview with a trained clinician and their primary caregiver. McGinnis, along with University of Vermont biomedical engineer and study senior author Ryan McGinnis, have been looking at ways to overcome this, by using artificial intelligence and machine learning to make diagnosis faster and more reliable.