Tag Archives: Medtronic

Being smart about using artificial intelligence in the field of medicine

Since my August 20, 2018 post featured an opinion piece about the possibly imminent replacement of radiologists with artificial intelligence systems and the latest research about employing them for diagnosing eye diseases, it seems like a good time to examine some of the mythology embedded in the discussion about AI and medicine.

Imperfections in medical AI systems

An August 15, 2018 article for Slate.com by W. Nicholson Price II (who teaches at the University of Michigan School of Law; in addition to his law degree he has a PhD in Biological Sciences from Columbia University) begins with the peppy, optimistic view before veering into more critical territory (Note: Links have been removed),

For millions of people suffering from diabetes, new technology enabled by artificial intelligence promises to make management much easier. Medtronic’s Guardian Connect system promises to alert users 10 to 60 minutes before they hit high or low blood sugar level thresholds, thanks to IBM Watson, “the same supercomputer technology that can predict global weather patterns.” Startup Beta Bionics goes even further: In May, it received Food and Drug Administration approval to start clinical trials on what it calls a “bionic pancreas system” powered by artificial intelligence, capable of “automatically and autonomously managing blood sugar levels 24/7.”

An artificial pancreas powered by artificial intelligence represents a huge step forward for the treatment of diabetes—but getting it right will be hard. Artificial intelligence (also known in various iterations as deep learning and machine learning) promises to automatically learn from patterns in medical data to help us do everything from managing diabetes to finding tumors in an MRI to predicting how long patients will live. But the artificial intelligence techniques involved are typically opaque. We often don’t know how the algorithm makes the eventual decision. And they may change and learn from new data—indeed, that’s a big part of the promise. But when the technology is complicated, opaque, changing, and absolutely vital to the health of a patient, how do we make sure it works as promised?

Price describes how a ‘closed loop’ artificial pancreas with AI would automate insulin levels for diabetic patients, flaws in the automated system, and how companies like to maintain a competitive advantage (Note: Links have been removed),

[…] a “closed loop” artificial pancreas, where software handles the whole issue, receiving and interpreting signals from the monitor, deciding when and how much insulin is needed, and directing the insulin pump to provide the right amount. The first closed-loop system was approved in late 2016. The system should take as much of the issue off the mind of the patient as possible (though, of course, that has limits). Running a close-loop artificial pancreas is challenging. The way people respond to changing levels of carbohydrates is complicated, as is their response to insulin; it’s hard to model accurately. Making it even more complicated, each individual’s body reacts a little differently.

Here’s where artificial intelligence comes into play. Rather than trying explicitly to figure out the exact model for how bodies react to insulin and to carbohydrates, machine learning methods, given a lot of data, can find patterns and make predictions. And existing continuous glucose monitors (and insulin pumps) are excellent at generating a lot of data. The idea is to train artificial intelligence algorithms on vast amounts of data from diabetic patients, and to use the resulting trained algorithms to run a closed-loop artificial pancreas. Even more exciting, because the system will keep measuring blood glucose, it can learn from the new data and each patient’s artificial pancreas can customize itself over time as it acquires new data from that patient’s particular reactions.

Here’s the tough question: How will we know how well the system works? Diabetes software doesn’t exactly have the best track record when it comes to accuracy. A 2015 study found that among smartphone apps for calculating insulin doses, two-thirds of the apps risked giving incorrect results, often substantially so. … And companies like to keep their algorithms proprietary for a competitive advantage, which makes it hard to know how they work and what flaws might have gone unnoticed in the development process.

There’s more,

These issues aren’t unique to diabetes care—other A.I. algorithms will also be complicated, opaque, and maybe kept secret by their developers. The potential for problems multiplies when an algorithm is learning from data from an entire hospital, or hospital system, or the collected data from an entire state or nation, not just a single patient. …

The [US Food and Drug Administraiont] FDA is working on this problem. The head of the agency has expressed his enthusiasm for bringing A.I. safely into medical practice, and the agency has a new Digital Health Innovation Action Plan to try to tackle some of these issues. But they’re not easy, and one thing making it harder is a general desire to keep the algorithmic sauce secret. The example of IBM Watson for Oncology has given the field a bit of a recent black eye—it turns out that the company knew the algorithm gave poor recommendations for cancer treatment but kept that secret for more than a year. …

While Price focuses on problems with algorithms and with developers and their business interests, he also hints at some of the body’s complexities.

Can AI systems be like people?

Susan Baxter, a medical writer with over 20 years experience, a PhD in health economics, and author of countless magazine articles and several books, offers a more person-centered approach to the discussion in her July 6, 2018 posting on susanbaxter.com,

The fascination with AI continues to irk, given that every second thing I read seems to be extolling the magic of AI and medicine and how It Will Change Everything. Which it will not, trust me. The essential issue of illness remains perennial and revolves around an individual for whom no amount of technology will solve anything without human contact. …

But in this world, or so we are told by AI proponents, radiologists will soon be obsolete. [my August 20, 2018 post] The adaptational learning capacities of AI mean that reading a scan or x-ray will soon be more ably done by machines than humans. The presupposition here is that we, the original programmers of this artificial intelligence, understand the vagaries of real life (and real disease) so wonderfully that we can deconstruct these much as we do the game of chess (where, let’s face it, Big Blue ate our lunch) and that analyzing a two-dimensional image of a three-dimensional body, already problematic, can be reduced to a series of algorithms.

Attempting to extrapolate what some “shadow” on a scan might mean in a flesh and blood human isn’t really quite the same as bishop to knight seven. Never mind the false positive/negatives that are considered an acceptable risk or the very real human misery they create.

Moravec called it

It’s called Moravec’s paradox, the inability of humans to realize just how complex basic physical tasks are – and the corresponding inability of AI to mimic it. As you walk across the room, carrying a glass of water, talking to your spouse/friend/cat/child; place the glass on the counter and open the dishwasher door with your foot as you open a jar of pickles at the same time, take a moment to consider just how many concurrent tasks you are doing and just how enormous the computational power these ostensibly simple moves would require.

Researchers in Singapore taught industrial robots to assemble an Ikea chair. Essentially, screw in the legs. A person could probably do this in a minute. Maybe two. The preprogrammed robots took nearly half an hour. And I suspect programming those robots took considerably longer than that.

Ironically, even Elon Musk, who has had major production problems with the Tesla cars rolling out of his high tech factory, has conceded (in a tweet) that “Humans are underrated.”

I wouldn’t necessarily go that far given the political shenanigans of Trump & Co. but in the grand scheme of things I tend to agree. …

Is AI going the way of gene therapy?

Susan draws a parallel between the AI and medicine discussion with the discussion about genetics and medicine (Note: Links have been removed),

On a somewhat similar note – given the extent to which genetics discourse has that same linear, mechanistic  tone [as AI and medicine] – it turns out all this fine talk of using genetics to determine health risk and whatnot is based on nothing more than clever marketing, since a lot of companies are making a lot of money off our belief in DNA. Truth is half the time we don’t even know what a gene is never mind what it actually does;  geneticists still can’t agree on how many genes there are in a human genome, as this article in Nature points out.

Along the same lines, I was most amused to read about something called the Super Seniors Study, research following a group of individuals in their 80’s, 90’s and 100’s who seem to be doing really well. Launched in 2002 and headed by Angela Brooks Wilson, a geneticist at the BC [British Columbia] Cancer Agency and SFU [Simon Fraser University] Chair of biomedical physiology and kinesiology, this longitudinal work is examining possible factors involved in healthy ageing.

Turns out genes had nothing to do with it, the title of the Globe and Mail article notwithstanding. (“Could the DNA of these super seniors hold the secret to healthy aging?” The answer, a resounding “no”, well hidden at the very [end], the part most people wouldn’t even get to.) All of these individuals who were racing about exercising and working part time and living the kind of life that makes one tired just reading about it all had the same “multiple (genetic) factors linked to a high probability of disease”. You know, the gene markers they tell us are “linked” to cancer, heart disease, etc., etc. But these super seniors had all those markers but none of the diseases, demonstrating (pretty strongly) that the so-called genetic links to disease are a load of bunkum. Which (she said modestly) I have been saying for more years than I care to remember. You’re welcome.

The fundamental error in this type of linear thinking is in allowing our metaphors (genes are the “blueprint” of life) and propensity towards social ideas of determinism to overtake common sense. Biological and physiological systems are not static; they respond to and change to life in its entirety, whether it’s diet and nutrition to toxic or traumatic insults. Immunity alters, endocrinology changes, – even how we think and feel affects the efficiency and effectiveness of physiology. Which explains why as we age we become increasingly dissimilar.

If you have the time, I encourage to read Susan’s comments in their entirety.

Scientific certainties

Following on with genetics, gene therapy dreams, and the complexity of biology, the June 19, 2018 Nature article by Cassandra Willyard (mentioned in Susan’s posting) highlights an aspect of scientific research not often mentioned in public,

One of the earliest attempts to estimate the number of genes in the human genome involved tipsy geneticists, a bar in Cold Spring Harbor, New York, and pure guesswork.

That was in 2000, when a draft human genome sequence was still in the works; geneticists were running a sweepstake on how many genes humans have, and wagers ranged from tens of thousands to hundreds of thousands. Almost two decades later, scientists armed with real data still can’t agree on the number — a knowledge gap that they say hampers efforts to spot disease-related mutations.

In 2000, with the genomics community abuzz over the question of how many human genes would be found, Ewan Birney launched the GeneSweep contest. Birney, now co-director of the European Bioinformatics Institute (EBI) in Hinxton, UK, took the first bets at a bar during an annual genetics meeting, and the contest eventually attracted more than 1,000 entries and a US$3,000 jackpot. Bets on the number of genes ranged from more than 312,000 to just under 26,000, with an average of around 40,000. These days, the span of estimates has shrunk — with most now between 19,000 and 22,000 — but there is still disagreement (See ‘Gene Tally’).

… the inconsistencies in the number of genes from database to database are problematic for researchers, Pruitt says. “People want one answer,” she [Kim Pruitt, a genome researcher at the US National Center for Biotechnology Information {NCB}] in Bethesda, Maryland] adds, “but biology is complex.”

I wanted to note that scientists do make guesses and not just with genetics. For example, Gina Mallet’s 2005 book ‘Last Chance to Eat: The Fate of Taste in a Fast Food World’ recounts the story of how good and bad levels of cholesterol were established—the experts made some guesses based on their experience. That said, Willyard’s article details the continuing effort to nail down the number of genes almost 20 years after the human genome project was completed and delves into the problems the scientists have uncovered.

Final comments

In addition to opaque processes with developers/entrepreneurs wanting to maintain their secrets for competitive advantages and in addition to our own poor understanding of the human body (how many genes are there anyway?), there are same major gaps (reflected in AI) in our understanding of various diseases. Angela Lashbrook’s August 16, 2018 article for The Atlantic highlights some issues with skin cancer and shade of your skin (Note: Links have been removed),

… While fair-skinned people are at the highest risk for contracting skin cancer, the mortality rate for African Americans is considerably higher: Their five-year survival rate is 73 percent, compared with 90 percent for white Americans, according to the American Academy of Dermatology.

As the rates of melanoma for all Americans continue a 30-year climb, dermatologists have begun exploring new technologies to try to reverse this deadly trend—including artificial intelligence. There’s been a growing hope in the field that using machine-learning algorithms to diagnose skin cancers and other skin issues could make for more efficient doctor visits and increased, reliable diagnoses. The earliest results are promising—but also potentially dangerous for darker-skinned patients.

… Avery Smith, … a software engineer in Baltimore, Maryland, co-authored a paper in JAMA [Journal of the American Medical Association] Dermatology that warns of the potential racial disparities that could come from relying on machine learning for skin-cancer screenings. Smith’s co-author, Adewole Adamson of the University of Texas at Austin, has conducted multiple studies on demographic imbalances in dermatology. “African Americans have the highest mortality rate [for skin cancer], and doctors aren’t trained on that particular skin type,” Smith told me over the phone. “When I came across the machine-learning software, one of the first things I thought was how it will perform on black people.”

Recently, a study that tested machine-learning software in dermatology, conducted by a group of researchers primarily out of Germany, found that “deep-learning convolutional neural networks,” or CNN, detected potentially cancerous skin lesions better than the 58 dermatologists included in the study group. The data used for the study come from the International Skin Imaging Collaboration, or ISIC, an open-source repository of skin images to be used by machine-learning algorithms. Given the rise in melanoma cases in the United States, a machine-learning algorithm that assists dermatologists in diagnosing skin cancer earlier could conceivably save thousands of lives each year.

… Chief among the prohibitive issues, according to Smith and Adamson, is that the data the CNN relies on come from primarily fair-skinned populations in the United States, Australia, and Europe. If the algorithm is basing most of its knowledge on how skin lesions appear on fair skin, then theoretically, lesions on patients of color are less likely to be diagnosed. “If you don’t teach the algorithm with a diverse set of images, then that algorithm won’t work out in the public that is diverse,” says Adamson. “So there’s risk, then, for people with skin of color to fall through the cracks.”

As Adamson and Smith’s paper points out, racial disparities in artificial intelligence and machine learning are not a new issue. Algorithms have mistaken images of black people for gorillas, misunderstood Asians to be blinking when they weren’t, and “judged” only white people to be attractive. An even more dangerous issue, according to the paper, is that decades of clinical research have focused primarily on people with light skin, leaving out marginalized communities whose symptoms may present differently.

The reasons for this exclusion are complex. According to Andrew Alexis, a dermatologist at Mount Sinai, in New York City, and the director of the Skin of Color Center, compounding factors include a lack of medical professionals from marginalized communities, inadequate information about those communities, and socioeconomic barriers to participating in research. “In the absence of a diverse study population that reflects that of the U.S. population, potential safety or efficacy considerations could be missed,” he says.

Adamson agrees, elaborating that with inadequate data, machine learning could misdiagnose people of color with nonexistent skin cancers—or miss them entirely. But he understands why the field of dermatology would surge ahead without demographically complete data. “Part of the problem is that people are in such a rush. This happens with any new tech, whether it’s a new drug or test. Folks see how it can be useful and they go full steam ahead without thinking of potential clinical consequences. …

Improving machine-learning algorithms is far from the only method to ensure that people with darker skin tones are protected against the sun and receive diagnoses earlier, when many cancers are more survivable. According to the Skin Cancer Foundation, 63 percent of African Americans don’t wear sunscreen; both they and many dermatologists are more likely to delay diagnosis and treatment because of the belief that dark skin is adequate protection from the sun’s harmful rays. And due to racial disparities in access to health care in America, African Americans are less likely to get treatment in time.

Happy endings

I’ll add one thing to Price’s article, Susan’s posting, and Lashbrook’s article about the issues with AI , certainty, gene therapy, and medicine—the desire for a happy ending prefaced with an easy solution. If the easy solution isn’t possible accommodations will be made but that happy ending is a must. All disease will disappear and there will be peace on earth. (Nod to Susan Baxter and her many discussions with me about disease processes and happy endings.)

The solutions, for the most part, are seen as technological despite the mountain of evidence suggesting that technology reflects our own imperfect understanding of health and disease therefore providing what is at best an imperfect solution.

Also, we tend to underestimate just how complex humans are not only in terms of disease and health but also with regard to our skills, understanding, and, perhaps not often enough, our ability to respond appropriately in the moment.

There is much to celebrate in what has been accomplished: no more black death, no more smallpox, hip replacements, pacemakers, organ transplants, and much more. Yes, we should try to improve our medicine. But, maybe alongside the celebration we can welcome AI and other technologies with a lot less hype and a lot more skepticism.

US military wants you to remember

While this July 10, 2014 news item on ScienceDaily concerns DARPA, an implantable neural device, and the Lawrence Livermore National Laboratory (LLNL), it is a new project and not the one featured here in a June 18, 2014 posting titled: ‘DARPA (US Defense Advanced Research Projects Agency) awards funds for implantable neural interface’.

The new project as per the July 10, 2014 news item on ScienceDaily concerns memory,

The Department of Defense’s Defense Advanced Research Projects Agency (DARPA) awarded Lawrence Livermore National Laboratory (LLNL) up to $2.5 million to develop an implantable neural device with the ability to record and stimulate neurons within the brain to help restore memory, DARPA officials announced this week.

The research builds on the understanding that memory is a process in which neurons in certain regions of the brain encode information, store it and retrieve it. Certain types of illnesses and injuries, including Traumatic Brain Injury (TBI), Alzheimer’s disease and epilepsy, disrupt this process and cause memory loss. TBI, in particular, has affected 270,000 military service members since 2000.

A July 2, 2014 LLNL news release, which originated the news item, provides more detail,

The goal of LLNL’s work — driven by LLNL’s Neural Technology group and undertaken in collaboration with the University of California, Los Angeles (UCLA) and Medtronic — is to develop a device that uses real-time recording and closed-loop stimulation of neural tissues to bridge gaps in the injured brain and restore individuals’ ability to form new memories and access previously formed ones.

Specifically, the Neural Technology group will seek to develop a neuromodulation system — a sophisticated electronics system to modulate neurons — that will investigate areas of the brain associated with memory to understand how new memories are formed. The device will be developed at LLNL’s Center for Bioengineering.

“Currently, there is no effective treatment for memory loss resulting from conditions like TBI,” said LLNL’s project leader Satinderpall Pannu, director of the LLNL’s Center for Bioengineering, a unique facility dedicated to fabricating biocompatible neural interfaces. …

LLNL will develop a miniature, wireless and chronically implantable neural device that will incorporate both single neuron and local field potential recordings into a closed-loop system to implant into TBI patients’ brains. The device — implanted into the entorhinal cortex and hippocampus — will allow for stimulation and recording from 64 channels located on a pair of high-density electrode arrays. The entorhinal cortex and hippocampus are regions of the brain associated with memory.

The arrays will connect to an implantable electronics package capable of wireless data and power telemetry. An external electronic system worn around the ear will store digital information associated with memory storage and retrieval and provide power telemetry to the implantable package using a custom RF-coil system.

Designed to last throughout the duration of treatment, the device’s electrodes will be integrated with electronics using advanced LLNL integration and 3D packaging technologies. The microelectrodes that are the heart of this device are embedded in a biocompatible, flexible polymer.

Using the Center for Bioengineering’s capabilities, Pannu and his team of engineers have achieved 25 patents and many publications during the last decade. The team’s goal is to build the new prototype device for clinical testing by 2017.

Lawrence Livermore’s collaborators, UCLA and Medtronic, will focus on conducting clinical trials and fabricating parts and components, respectively.

“The RAM [Restoring Active Memory] program poses a formidable challenge reaching across multiple disciplines from basic brain research to medicine, computing and engineering,” said Itzhak Fried, lead investigator for the UCLA on this project and  professor of neurosurgery and psychiatry and biobehavioral sciences at the David Geffen School of Medicine at UCLA and the Semel Institute for Neuroscience and Human Behavior. “But at the end of the day, it is the suffering individual, whether an injured member of the armed forces or a patient with Alzheimer’s disease, who is at the center of our thoughts and efforts.”

LLNL’s work on the Restoring Active Memory program supports [US] President [Barack] Obama’s Brain Research through Advancing Innovative Neurotechnologies (BRAIN) initiative.

Obama’s BRAIN is picking up speed.

Some talk about nanotechnology-enabled medical devices and regulatory requirements

Jim Butschli suggests a ‘new-to-me’ question in regard to nanotechnology-enabled devices and regulatory frameworks in his Feb. 12, 2013 article for Packaging World,

The projected worldwide market for nanotech-enabled products could range between $500 billion and $3 trillion by 2015, according to “Nanotechnology and nanomaterials medical devices—The regulatory frontier,” a presentation by Mary Gray, manager of regulatory affairs with DePuy Synthes—Johnson & Johnson, during MD&M West.

Gray said that medical devices and multiple industrial sectors could be improved through nanotech applications. She pointed to nano-based coatings that could be applied to medical device surfaces to optimize and improve health outcomes. Nanotechnology applications involve medical devices and consumer products, as well as their packaging.

Why would a company want to introduce combination products given their potentially onerous regulatory challenges? A key reason: enhanced therapeutic results and better patient outcomes.

That was an important takeaway from another presentation at MDM West given by Winifred Wu, president of Strategic Regulatory Partners LLC, entitled, “Strategies for the combination product pathway: Submission and approval.”

Wu cited a Research & Markets report that said the drug-device combination product market was growing 11.8% annually between 2010 and 2014.

Wu spent time discussing the U.S. Food and Drug Administration’s Office of Combination Products, noting that OCP tends to work closely with other FDA centers. She noted that there is still considerable debate around what products are combination products. [emphasis mine] The definition and scope continues to evolve as more therapies are developed.

Further investigation about combination products yielded this definition from the US Food and Drug Administration (FDA),

Combination products are defined in 21 CFR 3.2(e).  The term combination product includes:

(1) A product comprised of two or more regulated components, i.e., drug/device, biologic/device, drug/biologic, or drug/device/biologic, that are physically, chemically, or otherwise combined or mixed and produced as a single entity;

(2) Two or more separate products packaged together in a single package or as a unit and comprised of drug and device products, device and biological products, or biological and drug products;

(3) A drug, device, or biological product packaged separately that according to its investigational plan or proposed labeling is intended for use only with an approved individually specified drug, device, or biological product where both are required to achieve the intended use, indication, or effect and where upon approval of the proposed product the labeling of the approved product would need to be changed, e.g., to reflect a change in intended use, dosage form, strength, route of administration, or significant change in dose; or

(4) Any investigational drug, device, or biological product packaged separately that according to its proposed labeling is for use only with another individually specified investigational drug, device, or biological product where both are required to achieve the intended use, indication, or effect.

I wish there was a bit more detail about the area of debate regarding what constitutes a combination product but Butschli seemed a little more interested in regulatory strategies,

Morton’s [Michael Morton, senior director of global regulatory affairs with Medtronic] speech, “Regulatory strategies for 2013: The art of framing a successful submission,” touched on some familiar observations, including the following:

• Determining the intended use of the product seems obvious, but uses can become difficult to define clearly among multiple stakeholders.

• Understand the indicated patient population and be clear about a product’s benefit claims. …

There’s more about nanotechnology and regulatory strategies in Butschli’s article.

The MD&M (medical devices and manufacturing) West 2013 conference where these talks were presented ended on Feb. 14, 2013.

Science outreach and Nova’s Making Stuff series on PBS

The February 2011 NISE (Nanoscale Informal Science Education) Net newsletter pointed me towards a video interview with Amy Moll, a materials scientist (Boise State University) being interviewed by Joe McEntee, group editor IOP Publishing, for the physicsworld.com video series,

Interesting discussion, yes? The Making Stuff series on PBS is just part of their (materials scientists’ working through their professional association, the Materials Research Society) science outreach effort. The series itself has been several years in the planning but is just one piece of a much larger effort.

All of which puts another news item into perspective. From the Feb. 7, 2011 news item on Nanowerk,

The Arizona Science Center is enlisting the expertise of professors in Arizona State University’s Ira A. Fulton Schools of Engineering in showcasing the latest advances in materials science and engineering.

The engineering schools are among organizations collaborating with the science center to present the Making Stuff Festival Feb. 18-20. [emphasis mine]

The event will explore how new kinds of materials are shaping the future of technology – in medicine, computers, energy, space travel, transportation and an array of personal electronic devices.

No one is making a secret of the connection,

The festival is being presented in conjunction with the broadcast of “Making Stuff”, a multi-part television series of the Public Broadcasting Service program NOVA that focuses on advances in materials technologies. It’s airing locally on KAET-Channel 8.

Channel 8 is another collaborator on the Making Stuff Festival, along with ASU’s Consortium for Science, Policy and Outcomes, the Arizona Technology Council, Medtronic, Intel and Science Foundation Arizona.

I highlight these items to point out how much thought, planning, and effort can go into science outreach.

Nano haikus (from the Feb. 2011 issue of the NISE Net Newsletter,

We received two Haikus from Michael Flynn expressing his hopes and fears for nanotechnology:

Miracle fibers
Weave a new reality
Built from the ground up

Too Small to be seen
This toxin is nanoscale
Can’t tell if it spilled