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The AI will see you nowA doctor’s hopes for digital medicine

FOR ALL the technological wonders of modern medicine, from gene-editing to fetal surgery, health care—with its fax machines and clipboards—is often stubbornly antiquated.

He thinks AI will be particularly useful for repetitive, error-prone tasks, such as sifting images, scrutinising heart traces for abnormalities or transcribing doctors’ words into patient records.

It will be able to harness masses of data to work out optimal treatments (for both conditions and individuals), and improve workflows in hospitals.

If it confers a “gift of time” on doctors, he argues that this bonus should be used to prolong consultations, rather than simply speeding through them more efficiently.

That is a fine idea, but as health swallows an ever-bigger share of national wealth, greater efficiency is exactly what is needed, at least so far as governments and insurers are concerned.

Yet as demand for health care outstrips the supply of human carers, the future may involve consultations on smartphones and measurements monitored by chatbots.

On Tuesday, March 26th at 7:30 p.m., Warwick's will host Eric Topol as he discusses and signs his new book, 'Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again.'

On Tuesday, March 26th at 7:30 p.m., Warwick's will host Eric Topol as he discusses and signs his new book, 'Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again.'

A new study shows what it might take to make AI useful in health care

Early studies of ICU patients have shown that movement can accelerate healing, reduce delirium, and prevent muscle atrophy, but the scope of those studies has been limited by the challenges of monitoring patients at scale.

Depth sensors were installed in seven individual patient rooms and collected three-dimensional silhouette data 24 hours a day over the course of two months.

The results showed preliminary success: on average, the algorithm for detecting mobility activities correctly identified the activities a patient was performing 87% of the time.

For example, through discussions with the nurses and other hospital staff, the AI researchers concluded that using depth sensors rather than regular cameras would protect the privacy of patients and personnel.

“The clinicians I worked with—we discussed computer vision and AI for years,” says Serena Yeung, one of the lead authors on the paper, who will become an assistant professor of biomedical data science at Stanford this fall.

The approach meant the study went slowly: it took time to get buy-in from all levels of the hospital, and it was technically complex to analyze the hectic, messy environment of the ICU while using only silhouette data.

But taking this time was absolutely critical to design a safe, effective prototype of a system that will one day be genuinely beneficial to the patients and care staff, says Yeung.

The pressure to move fast and publish quickly leads researchers to avoid projects that don’t produce immediate results, and the privatization of a lot of AI funding hurts projects without clear commercialization opportunities.

“It is rare to see people working on an end-to-end system in the real world, and also spending the many years that it takes and doing the grunt work that is required to do this type of impactful work,” says Timnit Gebru, co-lead of the Ethical AI Team at Google, who was not involved in the research.

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