Associate Professor Nan Liu
Real world examples have shown that the misuse of algorithms in healthcare could perpetuate human prejudices or inadvertently cause harm when the machines are trained on biased datasets. “Many believe ML will revolutionise healthcare because machines make choices more objectively than humans. But without proper oversight, ML models may do more harm than good,” said Associate Professor Nan Liu, senior author of the comment, from the Centre for Quantitative Medicine and Health Services & Systems Research Programme at Duke-NUS Medical School, Singapore. “If, through ML, we uncover patterns that we otherwise would not see—like in radiology and pathology images—we should be able to explain how the algorithms got there, to allow for checks and balances.”
Together with a group of scientists from the UK and Singapore, the researchers highlight that although guidelines have been formulated to regulate the use of ML in clinical research, these guidelines are only applicable once a decision to use ML has been made and do not ask whether or when its use is appropriate in the first place.
For example, companies have successfully trained ML algorithms to recognise faces and road objects using billions of images and videos. But when it comes to their use in healthcare settings, they are often trained on data in the tens, hundreds or thousands. “This underscores the relative poverty of big data in healthcare and the importance of working towards achieving sample sizes that have been attained in other industries, as well as the importance of a concerted, international big data sharing effort for health data,” the researchers write.
Source: Healthcare in Europe
