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What does the future hold for AI in healthcare?

Can you imagine a future in which babies wear smart clothing to track their every move? It may sound like something from science fiction, but a romper suit being piloted in Helsinki, Copenhagen, and Pisa does exactly that.

The ‘motor assessment of infants jumpsuit’ (MAIJU) looks like typical baby clothing, but there is a crucial difference – it is full of sensors which assess child development.

“MAIJU offers the first of its kind quantitative assessment of infant’s motor abilities through the age from supine lying to fluent walking,” explains Professor Sampsa Vanhatalo, project lead at the University of Helsinki. “Such quantitation has not been possible anywhere, not even in hospitals. Here, we are bringing the solution to homes, which provides the only ecologically relevant context for motor assessment.”

Vanhatalo describes the path from wishful thinking about a solution to a possible clinical implementation as a “windy road”.

“There is no lack of dreams or technology, but we are lacking relevant and sufficient clinical problem statements, ecologically and context relevant datasets, reliable clinical phenotyping of the material, as well as suitable legislation for products that don’t follow the traditional forms,” he says.   

Machine learning allowed the researchers at Helsinki to find latent characteristics in infant’s movement signals that could not be identified through conventional heuristic planning.

“At the same time, we need to remember that AI in medical applications can only be as intelligent as we allow it to be,” adds Vanhatalo. “Real world situations are much muddier than we hope, and the ambiguity of many clinical situations or diagnoses is significantly limiting our chance to build as accurate AI solutions as we would hope. For instance, it is not possible to train and validate a classifier for the myriad of medical diagnoses which do not have clear-cut boundaries.”

Vanhatalo also believes that the medical community needs to recognise sensible targets for AI.

“It is much more fruitful to train clinical decision support systems (CDSS) than to train clinical decision systems,” he argues. “The latter is what some people hope and others fear; but the liabilities, including legal ones, from the decisions are so big that I struggle to see any company dare to commercialise such solutions. Indeed, I can already see how the legal risks from such liabilities, even if indirect or illusionary, are creating a bottleneck for commercialisation of many good AI products.”

The cutting edge of oncology

One area of medicine in which AI holds great potential to revolutionise care is oncology. Professor Karol Sikora, chief medical officer (CMO) at cancer care vanguard, Rutherford House, believes that machine learning can benefit physicians by assisting in complex treatment decisions.

“A range of commercial solutions are available to identify and map nearby organs at risk in apposition to the cancer,” explains Sikora. “Precision oncology demands the analysis of large volumes of data in an unprecedented way and we hope AI will provide patient benefit long term.”

Rutherford Health’s network of oncology centres use the latest innovations in cancer technology, such as AI for radiotherapy treatment planning.

According to Sikora, machine learning could also have a huge benefit in enhancing patient choice in the future. “AI could drive patient understanding of the risk benefit equation associated with any intervention,” he says.

Demystifying AI

But for healthcare organisations to fully untap the potential of AI there is a need to demystify “the noise” around it, according to Atif Chaughtai, senior director of global healthcare and life sciences business at software firm Red Hat.

“AI applied correctly has huge potential in savings lives and managing the ever-increasing cost of healthcare,” says Chaughtai. “In the future AI will continue to evolve and will be widely used as an assistive technology to perform tasks with more accuracy and efficiency with humans in the loop to make final decisions.”

He adds that for AI capability to be adopted successfully, organisations must introduce change at a manageable pace and work collaboratively to innovate on intelligent business processes.

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