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Internal IBM documents show that its Watson supercomputer often spit out erroneous cancer treatment advice and that company medical specialists and customers identified “multiple examples of unsafe and incorrect treatment recommendations” as IBM was promoting the product to hospitals and physicians around the world.
Both institutions forecast huge success and gains using this new technology, to reduce cost and errors in cancer treatments. Given the huge advances in "personalized medicine" it was hoped the new technology would be able to keep pace with research and specific treatments for a wide variety of cancers and their specific treatments.
The New England Journal of Medicine, Health Catalyst reported the disappointing and potentially dangerous errors in relying on AI decisions for treatment.
Background Information:
As the complexity and cost of health care increase, many health care institutions have turned to information technology as a means of improving patient care. Electronic medical records, patient portals, digitized medical devices, and even wearables are becoming more broadly used. These systems have been largely transactional, but artificial intelligence (AI) systems that are capable of machine learning go beyond traditional medical transactions and record-keeping to analyze data, make decisions, and exercise judgment.
Because AI tools perform tasks previously performed by humans, they raise concerns about large-scale job loss in health care and other industries. Our view, however, is that “augmentation” of human labor is more likely than large-scale automation.
The Potential of Cognitive Technologies in Medicine
Cognitive technologies are being been introduced in health care in part to reduce human decision-making and the potential for human error in providing care. Medical errors are the third leading cause of death in the United States, but they are not generally due to inherently bad clinicians. Instead, they are often attributed to cognitive errors (such as failures in perception, failed heuristics, and biases), an absence or underuse of safety nets and other protocols, and unwarranted variation in physician practice patterns.
The use of AI technologies promises to reduce the cognitive workload for physicians, thus improving care, diagnostic accuracy, clinical and operational efficiency, and the overall patient experience. While there are understandable concerns and discussion about AI taking over human jobs, there is limited evidence to date that AI will replace humans in health care. For example, numerous studies have suggested that computer-aided readings of radiological images are just as accurate (or more so) than readings performed by human radiologists.
But such systems are not yet in broad use, and, when they are used, they serve as a “second set of eyes.” We know of no radiologists who have lost their jobs from this form of automation. AI technologies such as IBM Watson have excited observers with their potential to treat cancer, but they don’t seem to have replaced any oncologists and, for that matter, there have been no rigorous examinations of their impact on patients. Sedasys, a semi-automated system for administering the anesthesia drug Propofol, met with poor sales and resistance from anesthesiologists and was withdrawn from the market. AI technologies may automate some medical tasks in the future, but few if any jobs have been fully computerized thus far.
Instead of large-scale job loss resulting from automation of human work, we propose that AI provides an opportunity for the more human-centric approach of augmentation. In contrast to automation, augmentation presumes that smart humans and smart machines can coexist and create better outcomes than either could alone. AI systems may perform some health care tasks with limited human intervention, thereby freeing clinicians to perform higher-level tasks.
In IBMs Watson's case, clinicians found many serious errors in recommendations for dosing and medications. IBM attributed these errors to the integration of clinical recommendation and software programming.
Whatever cost savings or increased accuracy in prescribing is easily offset by the unknown cost of programming development for AI. ie, AI is not yet ready for primetime in this area. Both of these institutions had considerable investment capital committed. Sloan--Kettering abandoned the use of Watson when it's clinicians found the errors. IBM was premature in promoting their AI/Watson product. It was a product looking for new business, and not yet mature.
The saving grace is that physicians will not, in the long run, allow algorithms to treat patients without supervision, error checking and suitable references on the AI platform. It's use will not be transparent, with multiple choice type answers.
Read this primer on IBM Watson to understand what it is and how it is being used.
Mini-glossary: AI terms you should know
All is not lost. AI has significant value for natural language processing and may contribute much to the use of the electronic health record human-machine interface.
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