Might this be a sign that AI platforms like Watson still cannot diagnose the wide range of patients’ conditions as accurately as a board-certified clinical pathologist?
Computer technology evolves so quickly, products often become obsolete before fulfilling their expected potential. Such, apparently, is the case with Watson, the genius artificial intelligence (AI) brainchild of International Business Machines Corp. (IBM) which was going to revolutionize how healthcare providers diagnose disease. In some areas of healthcare, such as analyzing MRIs and X-rays, AI has been a boon. But from a business perspective, Watson has failed to turn a profit for IBM, so it has to go.
In February, The Wall Street Journal (WSJ) reported that IBM is looking to sell its Watson Health unit because it is not profitable, despite bringing in $1 billion annually in revenue. The sale of Watson Health, the article states, would be aligned with IBM’s goal of streamlining the company and focusing its energies on cloud computing and other AI functions. Because one goal of the Watson project was to give physicians a tool to help them diagnose patients more accurately and faster, the problems that prevented Watson from achieving that goal should be of interest to pathologists and clinical laboratory managers, who daily are on the front lines of helping doctors diagnose the most challenging cases.
In a follow-up article, titled, “Potential IBM Watson Health Sale Puts Focus on Data Challenges,” the WSJ wrote, “… some experts found that it can be difficult to apply AI to treating complex medical conditions. Having access to data that represents patient populations broadly has been a challenge, experts told the Journal, and gaps in knowledge about complex diseases may not be fully captured in clinical databases.”
“I believe that we’re many years away from AI products that really positively impact clinical care for many patients,” Bob Kocher, Partner at Venrock, a venture-capital firm that invests in healthcare IT and related services, told the WSJ.
IBM Watson was promoted as a major resource to help improve medical care and support doctors in making more accurate diagnoses. However, in “IBM’s Retreat from Watson Highlights Broader AI Struggles in Health,” the WSJ reported that “IBM spent several billion dollars on acquisitions to build up Watson [Health] … a unit whose marquee product was supposed to help doctors diagnose and cure cancer … A decade later, reality has fallen short of that promise.”
IBM initially created Watson (above center) to be an AI tool capable of a wide variety of applications, starting with answering questions. In January 2011, Watson made headlines when it defeated previous Jeopardy champions Ken Jennings (left) and Brad Rutter (right) on the popular television game show.After its triumph, IBM announced it would transition Watson for use in medical applications and promoted it as a major resource to help improve medical care and support healthcare professionals in making more precise diagnoses. The company called its new division IBM Watson Health and stated that massive data sets would be the key to accomplish its healthcare mission. That same year, The Dark Report had an IBM executive do a presentation about Watson Health at the Executive War College on Laboratory and Pathology Management. (Photo copyright: CBS News.)
Watson’s Successes and Failures in Healthcare
During the years following Watson’s Jeopardy win, Watson Health made some positive advances in the fields of healthcare data analytics, performance measurements, clinical trial recruitment, and healthcare information technology (HIT).
However, Watson Health also experienced some high-profile failures as well. One such failure involved a collaboration with MD Anderson Cancer Center, established in 2013, to help the health systems’ oncologists develop new tools to benefit cancer patients. MD Anderson ended the relationship in 2018 after spending more than $60 million on the project, citing “multiple examples of unsafe and incorrect treatment recommendations,” made by the Watson supercomputer, Healthcare IT News reported.
Watson Health later readjusted the development and sales of its AI drug discovery tools and altered its marketing strategy amid reports of disappointing sales and skepticism surrounding machine learning for medical applications.
Underestimating the Challenge of AI in Healthcare
Since its inception, Watson Health has achieved substantial growth, mainly through a series of acquisitions. Those targeted acquisitions include:
Merge Healthcare, a healthcare imaging software company that was purchased for $1 billion in 2015,
Phytel, a health management software company that was purchased for an undisclosed amount in 2015,
Explorys, a healthcare analytics company that was purchased for an undisclosed amount in 2015, and
Truven Health Analytics, a provider of cloud-based healthcare data, analytics, and insights that was purchased for $2.6 billion in 2016.
“IBM’s Watson Health business came together as a result of several acquisitions,” said Paddy Padmanabhan, founder and CEO of Damo Consulting, a firm that provides digital transformation strategy and advisory services for healthcare organizations. “The decision to sell the business may also have to do with the performance of those units on top of the core Watson platform’s struggles,” he told Healthcare IT News.
It should be noted that these acquisitions involved companies that already had a product in the market which was generating revenue. So, the proposed sale of Watson Health includes not just the original Watson AI product, but the other businesses that IBM put into its Watson Health business division.
Padmanabhan noted that there are many challenges for AI in healthcare and that “historical data is at best a limited guide to the future when diagnosing and treating complex conditions.” He pointed to the failure with MD Anderson (in the use of Watson Health as a resource or tool for diagnosing cancer) was a setback for both IBM and the use of AI in healthcare. However, Padmanabhan is optimistic regarding the future use of AI in healthcare.
“To use an oft-quoted analogy, AI’s performance in healthcare right now is more akin to that of the hedgehog than the fox. The hedgehog can solve for one problem at a time, especially when the problem follows familiar patterns discerned in narrow datasets,” he told Healthcare IT News. “The success stories in healthcare have been in specific areas such as sepsis and readmissions. Watson’s efforts to apply AI in areas such as cancer care may have underestimated the nuances of the challenge.”
Other experts agree that IBM was overly ambitious and overreached with Watson Health and ended up over-promising and under-delivering.
“IBM’s initial approach misfired due to how the solution AI was trained and developed,” Dan Olds, Principal Analyst with Gabriel Consulting Group, told EnterpriseAI. “It didn’t conform well to how doctors work in the real world and didn’t learn from its experiences with real doctors. It was primarily learning from synthetic cases, not real-life cases.”
Was Watson Already Obsolete?
Another issue with Watson was that IBM’s marketing campaign may have exceeded the product’s design capabilities. When Watson was developed, it was built with AI and information technologies (IT) that were already outdated and behind the newest generation of those technologies, noted Tech Republic.
“There were genuine AI innovation triggers at Watson Health in natural language processing and generation, knowledge extraction and management, and similarity analytics,” Jeff Cribbs, Research Vice President at Gartner Research, told Tech Republic. “The hype got ahead of the engineering, as the hype cycle says it almost always will, and some of those struggles became apparent.”
Can Artificial Intelligence Fulfill its Potential in Healthcare?
The fact that IBM is contemplating the sale of Watson Health is another illustration of how difficult it can be to navigate the healthcare industry in the US. It is probable that someday AI could make healthcare diagnostics more accurate and reduce overall costs, however, data challenges still exist and more research and exploration will be needed for AI to fulfill its potential.
“Today’s AI systems are great in beating you at chess or Jeopardy,” Kumar Srinivas, Chief Technology Officer, Health Plans, at NTT DATA Services told Forbes. “But there are major challenges when addressing practical clinical issues that need a bit of explanation as to ‘why.’ Doctors aren’t going to defer to AI-decisions or respond clinically to a list of potential cancer cases if it’s generated from a black box.”
And perhaps that is the biggest challenge of all. For doctors to entrust their patients’ lives to a supercomputer, it better be as good as the hype. But can AI in healthcare ever accomplish that feat?
“AI can work incredibly well when it’s applied to specific use cases,” gastroenterologist Nirav R. Shah, MD, Chief Medical Officer at Sharecare, told Forbes. “With regards to cancer, we’re talking about a constellation of thousands of diseases, even if the focus is on one type of cancer. What we call ‘breast cancer,’ for example, can be caused by many different underlying genetic mutations and shouldn’t really be lumped together under one heading. AI can work well when there is uniformity and large data sets around a simple correlation or association. By having many data points around a single question, neural networks can ‘learn.’ With cancer, we’re breaking several of these principles.”
So, in deciding to divest itself of Watson Health, IBM may simply be as prescient now as it was when it first embraced the concept of AI in healthcare. The tech giant may foresee that AI will likely never replace the human mind of a trained healthcare diagnostician.
If this proves true—at least for several more years—then board-certified clinical pathologists can continue to justifiably refer to themselves as “the doctor’s doctor” because of their skills in diagnosing difficult-to-diagnose patients, and because of their knowledge of which clinical laboratory tests to order and how to interpret those test results.
Ever shrinking “lab-on-a-…” technologies, a boon to medical laboratories and anatomic pathologists in remote resource-strapped regions, also have a place in modern labs
Researchers took another leap forward in reducing the size of clinical laboratory diagnostic tests and observational tools. This demonstration involved lab-on-a-fiber technology and showed promise in both monitoring anatomic pathology biomarkers in vivo and supplementing the abilities of existing lab-on-a-chip and microfluidic devices.
In 2013, Dark Daily reported on research into an implantable laboratory-on-a-chip (LOC) for monitoring blood chemistry during chemotherapy. It was a major breakthrough at the time, which promised new and powerful tools for cancer treatment regimens.
However, most LOC systems aren’t designed for wet environments. Also, while microfluidics and flexible membranes allow for smaller footprints and tighter placement, they are still invasive in ways that might make patients uncomfortable or make real-world use less than ideal. And, long-term use brings further complications, such as corrosion or foreign-body granulomas.
Thus, lab-on-a-fiber’s ability to function in vivo, is one of the device’s principal advantages, as ExtremeTech noted.
Lab-on-a-fiber technology addresses many concerns. It is small enough to insert directly into organs, muscle mass, or veins when used as biosensors. And the fibers can return a wealth of information by using light and reflection, while allowing for minimal discomfort and precision placement.
Schematic of the lab-on-a-fiber biosensing principle. A metallic nanostructure supporting a resonant plasmonic mode is integrated on the optical fiber tip. When a molecular binding event occurs at the sensor surface, the reflectance peak associated to the plasmonic mode shifts towards longer wavelengths. (Image and caption copyright: Analyst/The Royal Society of Chemistry.)
The Past and Future of Scaling Clinical Laboratory Testing
Developers believe lab-on-a-fiber approaches could offer further adaptability and functionality to other “lab-on-a-…” technologies. For example, as highlighted in Advanced Science News, researchers are employing lab-on-a-fiber technologies to further refine and improve LOC functions and designs.
“As the scientific world moves inexorably to smaller dimensions … The emerging concept of ‘lab‐on‐fiber’ will give the optical fiber platform additional (highly integrated) functionalities,” noted Deepak Uttamchandani, PhD, Vice Dean Research, Faculty of Engineering, and, Robert Blue, PhD, Research Fellow, both at the University of Strathclyde, Glasgow, UK, in their review paper, “Recent Advances In Optical Fiber Devices for Microfluidics Integration.” The paper, published in the Journal of Biophotonics, examined “the recent emergence of miniaturized optical fiber-based sensing and actuating devices that have been successfully integrated into fluidic microchannels that are part of microfluidic and lab‐on‐chip systems.”
In his review paper on the emerging concept of lab-on-a-fiber, Deepak Uttamchandani, PhD, notes, “The versatility of the optical fiber platform has already allowed researchers to conduct immunoassays in microchannels using both fluorescently‐labelled and label‐free formats whilst gaining advantages of reduced assay time and increased sensitivity.” (Photo copyright: University of Strathclyde.)
Lab-on-a-Fiber: Another Step Forward or a Major Change?
At each milestone in the scaling of clinical laboratory testing, experts and media outlets predicted the demise of big laboratories and the dawn of a POC-centric testing era. Yet, despite 20-plus years of progress, this has yet to happen.
While it is critical for anatomical pathology leaders and clinical laboratory managers to stay abreast of developments in testing technology, much of the innovation behind lab-on-a-fiber remains strictly in the research realm. Challenges to the commercialization of these new techniques include both physical factors, such as design and manufacture of ready-to-use tests, and regulatory concerns, including FDA clearances and payer approval of new assays and diagnostic procedures.
Until researchers and test manufacturers overcome these hurdles, threats to current standards and workflows are minimal. However, much like the gains in scale realized through incorporating lab-on-a-chip concepts into clinical laboratory testing, the research powering these innovations might prove useful in further improving and expanding medical laboratory testing options.
Big data offers new opportunities for healthcare providers, clinical laboratories, and pathology groups, and this new alliance hopes to accelerate big data capabilities
Big data has the potential to deliver unprecedented insight into optimizing the patient care experience and managing outcomes for healthcare providers. That is particularly true for clinical laboratories, and pathology groups. Yet, with the sheer amount of data generated by today’s ever-expanding menus of diagnostic procedures, communicating this data between systems and analyzing data at high-levels still presents challenges.
To help healthcare organizations jumpstart their Big Data programs, key stakeholders are joining forces. One such alliance involves Siemens Healthineers and IBM Watson Health. In an October 2016 press release, the two organizations announced a five-year global strategic alliance aimed at helping healthcare professionals optimize value-based care that leverages increasingly complex data collected for use in precision medicine.
What should intrigue pathologists and medical laboratory managers about this new alliance is the fact that Siemens Healthineers owns two of the world’s largest businesses in radiology/imaging and in vitro (IVD). Thus, it can be expected that the alliance will be looking to identify ways to combine radiology data with clinical laboratory data that produce knowledge that can be applied to clinical care. (more…)
Clinical laboratories and pathology groups may eventually use these devices to detect minute quantities of biomarkers
IBM has regularly declared its interest in being a player in the field of healthcare big data. Now comes news that the information technology giant wants to develop lab-on-a-chip (LOC) technology that can handle different types of clinical laboratory and anatomic pathology tests.
As reported in Nature Nanotechnology, researchers at IBM are working with a team from Mount Sinai Health System. Together, they created a lab-on-a-chip device capable of separating biomolecules as small as 20nm in length from urine, saliva, or blood samples without the need for specialized clinical laboratory equipment. The technology is called nanoDLD.
Current testing of this lab-on-a-chip focuses on exosomes and cancer research. However, researchers note that the asymmetric pillar array on their silicon chip can also separate DNA, viruses, and protein complexes. With further development, they hope to separate particles down to 10nm in length. This would allow isolation of specific proteins. (more…)
