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Zhong Jia1*,
Jie Zhang1, Chao-Jun Kong2
1Hangzhou First
People's Hospital, Nanjing Medical University Affiliated Hangzhou Hospital,
Hangzhou, Zhejiang, China
2Chinese Medicine
University Fourth Affiliated Clinical Hospital, Hangzhou, Zhejiang,China
*Corresponding
author: Zhong
Jia, Department of Hepatopancreatobililiary Surgery, Huansha Road No.261,
Hangzhou First People's Hospital, Nanjing Medical University Affiliated
Hangzhou Hospital, Hangzhou, Zhejiang 310006, China. Tel: +8613958114181; Fax:
+86057187914773; Email: jiazhong20058@hotmail.com
Received Date: 01 October,
2017; Accepted Date: 02 October,
2017; Published Date: 09 October, 2017
Citation: Jia Z, Zhang J, Kong CJ (2017) Comments for Unintended Consequences of Machine Learning in Medicine. J Surg 2017: 176. DOI: 10.29011/2575-9760.000176
Comments
In the era of big data-based “Artificial intelligence”, machine learning is
becoming a key core of application technologies now and future. But in the
course of its growth and improvement, many negative factors,including the incomplete
clinical data, lacking of optimal algorithm,etc. may take inaccurate or even
counterproductive effects on ML-DSS,so it's not necessary to make a fuss.It
indeed is just a fike if someone has over worrisome attitude about the new
emerging advance [1] in this article would like to express
their real concerns, aiming to transmit alert messages to first-line clinicians
as overreliance of automated results may accidently produce unintended
consequences, such as weakening clinical skills, decision-making accuracy of
the machine, etc. It also has raised upset in the readers.The authors' starting
point is good, with intention to alert clinicians not ignore unintended
consequence of machine. But this is just begging for sick days. In fact,
machine brain is to extend human brain rather than replace human brain.
Sometimes, our experience and inertia will also mislead to accidental errors or
results. In practice, we usually recheck results from automated machine by
manual review, so the final judgment is determined by clinicians particularly
regarding to the critical value so as to remind and register on record book,
and then to urge clinicians to take steps. Labor tools let hands free, while
deepmachine learning liberates human brain. But when machine thinks like a
human, we are really worry about due to its cool metal without any
emotions.Just like exploration of nuclear energy, machine learning in medical
fields must be under control and have essential constraints in medicine, if
any.
short commentary