Why AI won’t replace doctors yet Article that makes the opposite case
Everyone;
Interesting opinion piece
that makes the opposite case that the author is arguing. He is saying that the doctors knowledge &
experience is superior to what AI would have…… and yet states that for “Yet
it may be safe to say that AI is
superior to radiotherapy physicians and technicians” and that AI correctly
diagnosed a rare cancer & its cure that doctors missed.
Ken
Why AI won’t replace doctors yet
BY TAKAMITSU SAWA AUG 16, 2018
HIKONE, SHIGA PREF. – A medical doctor diagnoses the
patient and writes prescriptions based on interview with the patient as well as
blood tests, analysis of image data obtained from magnetic resonance imaging
(MRI) and computed tomography (CT), information related to the patient’s genes
and so on. In giving the diagnosis, the doctor combines the information
obtained through such processes with his or her own knowledge and experience.
No matter how reputed a physician may be, the chances of them making a wrong
diagnosis can never be zero.
With the recent progress in artificial intelligence,
there has been much speculation that artificial intelligence could very well
surpass a human doctor’s ability to make diagnoses and write prescriptions.
In August 2016, the Institute of Medical Science at the
University of Tokyo released the outcome of a case study to show how powerful
AI can be. IBM’s Watson AI program was fed with information contained in nearly
20 million medical articles related to cancer research and more than 17 million
pieces of information related to pharmaceuticals.
The program, when it was then provided with data on the
examination of a leukemia patient and the gene information of the patient’s
cancer cells, offered within 10 minutes the diagnosis of a special type of
leukemia that the physician in charge of the patient had never even dreamed of
and, moreover, prescribed a combination of anticancer drugs best suited for
that disease. Soon after being treated in accordance with the prescription, the
patient recovered completely and was released from the hospital.
It is an easy task for any AI program to read and
memorize information featured in nearly 20 million cancer-related papers and
over 17 million pieces of pharmaceutical information, but it would be an
impossible task for a human. Even if a person could read and analyze 10 medical
research papers every day, it would still take nearly 5,500 years to complete
the task. Moreover, there are limits to what a human brain can remember. It is
impossible for anyone to accurately remember all the figures in these papers, and
information is bound to slip out of memory as time passes by.
If medical doctors are no match for AI in making
diagnoses and writing prescriptions, as this study shows, their jobs could be
reduced to asking the patient some questions, informing the patient of the
diagnosis given by the AI program and providing the patient with a
prescription. The doctor’s skills may be tested only in asking the right
questions to the patient. Does that mean that “excellent doctors” have become a
things of the past? I will try to prove that is definitely not the case.
The 20 million cancer-related papers include information
both relevant and irrelevant to cancer treatment. What determines the level of
a doctor’s competence is the ability to determine whether a particular paper is
relevant or irrelevant simply by skimming through it. It would be a total waste
of time to read through a paper that is irrelevant.
It is said that in the fields of medicine and life
science in particular, there are more than a few papers that contain fabricated
or falsified images and data. The ability to detect fabrications and
falsifications just by glancing at a paper will come from a doctor’s
professional intuition — which an AI program cannot emulate. Moreover, the
doctor’s ability to make diagnoses and write prescriptions may not necessarily
be surpassed just by reading and memorizing all the professional papers and
information in them. This is because AI does not possess the type of empirical
knowledge that a clinical doctor has accumulated through treating large numbers
of patients.
Yet it may be safe to say that AI is superior to
radiotherapy physicians and technicians when it comes to analysis of MRI and CT
images, which is said to hold the key to cancer diagnosis. AI can concentrate
on such analysis over an extended period without fatigue or distracting
thoughts.
The type of knowledge a medical doctor gains by reading
and comprehending professional books and papers is called “explicit knowledge,”
whereas the type of knowledge gained in the form of intuition or senses by
conversing with and treating a large number of patients is called “tacit
knowledge.”
A doctor hands down a judgment on the condition of a
patient by combining these two types of knowledge. A doctor reputed to be
excellent possesses a vast amount of tacit knowledge that has been accumulated
through numerous cases of clinical experience, in addition to his explicit
knowledge.
It is impossible to express or transmit tacit knowledge
in writing, drawings, numbers or mathematical formulas. That is to say, tacit
knowledge cannot be expressed in a scientific paper. What AI can learn from
papers and other forms of information is limited to explicit knowledge that can
be expressed in words and numbers. In other words, opportunities to accumulate
clinical experiences are closed to AI.
Being a novice in the field, I can’t possibly assess how
much weight a doctor’s tacit knowledge accumulated through numerous clinical
experiences has in making diagnoses and writing prescriptions. But one thing is
certain. AI does not possess an iota of tacit knowledge. That is to say, a
diagnosis made or a prescription written by AI is solely based on huge amounts
of explicit knowledge and the power of logical thinking.
The AlphaGo software developed by Google DeepMind for the
board game of go has studied not only jōseki (standard moves considered to be
optimum in the game) but also the records of tens of thousands of actual games
played in the past. In addition, it conducts deep learning not only by playing
games against professional go players but also playing tens of millions of
games against itself.
In May 2017, AlphaGo soundly defeated the most skilled
South Korean go player by four games to one. AlphaGo is different from Watson
in that the former has carried out deep learning of tacit knowledge based on
the experience of numerous games — whose volume is tens of thousands times more
than that accumulated by any professional player. In short, AlphaGo possesses
tacit knowledge gained through an immense number of games that no human go
players could possibly play.
At least in the world of go, there may be no human
players who can beat AI. But I am convinced that when it comes to the diagnosis
and treatment of cancer, there are outstanding doctors whose ability well
surpasses that of Watson, which lacks tacit knowledge.
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