This discussion relates to the very long run assuming that some functions like Radiology may be better performed by intelligent machines.
My comment for discussion in this Blog relates to the immediate term, when in my opinion AI based algorithms using one patient's data can focus attention on those who need medical intervention, even if they do not line up. The problem is in special important when elder adults do not complain as being afraid of relocation until their frailty symptoms or elderly diseases are clearly observable. Than it is too late to prevent and treatment is so costly that it is a burden on State Economy.
From the article:
Much discussion and debate surround the topic of physicians and the use of artificial intelligence. The notion that AI could ever fully replace a doctor is not a completely absurd one — there are many jobs, including white-collar professions, that eventually will be replaced by automation and various levels of machine-learning technology.
Certainly, from a pragmatic perspective, it is interesting to consider the possibility of a physician who never needs to eat, never tires, can read thousands of pages of new research every day, can record and remember every experience and can even communicate in multiple languages.
But can a machine provide better patient care?
Fallacy: Empathy Is Not Necessary
Some radiologists may indeed lose their jobs.
Fallacy: T ech Will Take Over Tumor Treatment
Yes, cancer detection and treatment represent another area in which machine learning is progressing rapidly. IBM Watson, for instance, uses cognitive computing AI technology to recommend cancer treatments in rural areas of the United States, India and China, which suffer from shortages of trained professionals; however, those tools and services should be viewed as supplementary to a physician’s repertoire, not a replacement for it.
Though the technology is increasing in popularity, what can clinicians actually gain from the data the devices collect?
Not all that much , posited Richard Milani, a physician and the chief clinical transformation officer at Ochsner Health System. He noted that most of the information from wearables isn’t extremely valuable. Activity, steps and sleep data are nice, but they’re not worthwhile to providers.
“Currently, the data is quite limited in terms of what we collect from wearables,” Milani said. “Wearables are an important component of our future, but what we seek is information.”
Yet James Mault , another physician and CMO of Qualcomm Life, said wearable data is useful because gives providers a glimpse into what happens to the patient after he or she leaves the hospital, particularly following a surgery. Previously, a patient’s post-hospital activity was essentially a black hole in that the physician had no clue what the patient was doing. "Now we have a wearable device that has the ability to collect very simple pieces of information,” he said. “Well, guess what? That simple information is way more than what I’ve got right now, ‘cuz I’ve got jack nothing.”
But there’s not buy-in from every physician. One major challenge standing in the way is convincing providers that the data is accurate and reliable.
Consumer engagement poses another problem. Some patients lose interest in wearables , which prevents the provider from gaining access to long-term patterns in their behavior.
But perhaps the biggest issue surrounds the separation between the wearable information and the physician’s workflow. Data from wearables sits in one world, while the episodic care model of the clinical environment is another world. “Those two worlds don’t map very well,” said Drew Schiller, CEO and cofounder of Validic. “It’s a big challenge. You have to … be able to deliver the right data at the right time and show the information in front of the clinician inside the clinical workflow and then interface with the data.”