Tag Archives: data

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.

Ishiguro’s robots and Swiss scientist question artificial intelligence at SXSW (South by Southwest) 2017

It seems unexpected to stumble across presentations on robots and on artificial intelligence at an entertainment conference such as South by South West (SXSW). Here’s why I thought so, from the SXSW Wikipedia entry (Note: Links have been removed),

South by Southwest (abbreviated as SXSW) is an annual conglomerate of film, interactive media, and music festivals and conferences that take place in mid-March in Austin, Texas, United States. It began in 1987, and has continued to grow in both scope and size every year. In 2011, the conference lasted for 10 days with SXSW Interactive lasting for 5 days, Music for 6 days, and Film running concurrently for 9 days.

Lifelike robots

The 2017 SXSW Interactive featured separate presentations by Japanese roboticist, Hiroshi Ishiguro (mentioned here a few times), and EPFL (École Polytechnique Fédérale de Lausanne; Switzerland) artificial intelligence expert, Marcel Salathé.

Ishiguro’s work is the subject of Harry McCracken’s March 14, 2017 article for Fast Company (Note: Links have been removed),

I’m sitting in the Japan Factory pavilion at SXSW in Austin, Texas, talking to two other attendees about whether human beings are more valuable than robots. I say that I believe human life to be uniquely precious, whereupon one of the others rebuts me by stating that humans allow cars to exist even though they kill humans.

It’s a reasonable point. But my fellow conventioneer has a bias: It’s a robot itself, with an ivory-colored, mask-like face and visible innards. So is the third participant in the conversation, a much more human automaton modeled on a Japanese woman and wearing a black-and-white blouse and a blue scarf.

We’re chatting as part of a demo of technologies developed by the robotics lab of Hiroshi Ishiguro, based at Osaka University, and Japanese telecommunications company NTT. Ishiguro has gained fame in the field by creating increasingly humanlike robots—that is, androids—with the ultimate goal of eliminating the uncanny valley that exists between people and robotic people.

I also caught up with Ishiguro himself at the conference—his second SXSW—to talk about his work. He’s a champion of the notion that people will respond best to robots who simulate humanity, thereby creating “a feeling of presence,” as he describes it. That gives him and his researchers a challenge that encompasses everything from technology to psychology. “Our approach is quite interdisciplinary,” he says, which is what prompted him to bring his work to SXSW.

A SXSW attendee talks about robots with two robots.

If you have the time, do read McCracken’t piece in its entirety.

You can find out more about the ‘uncanny valley’ in my March 10, 2011 posting about Ishiguro’s work if you scroll down about 70% of the way to find the ‘uncanny valley’ diagram and Masahiro Mori’s description of the concept he developed.

You can read more about Ishiguro and his colleague, Ryuichiro Higashinaka, on their SXSW biography page.

Artificial intelligence (AI)

In a March 15, 2017 EPFL press release by Hilary Sanctuary, scientist Marcel Salathé poses the question: Is Reliable Artificial Intelligence Possible?,

In the quest for reliable artificial intelligence, EPFL scientist Marcel Salathé argues that AI technology should be openly available. He will be discussing the topic at this year’s edition of South by South West on March 14th in Austin, Texas.

Will artificial intelligence (AI) change the nature of work? For EPFL theoretical biologist Marcel Salathé, the answer is invariably yes. To him, a more fundamental question that needs to be addressed is who owns that artificial intelligence?

“We have to hold AI accountable, and the only way to do this is to verify it for biases and make sure there is no deliberate misinformation,” says Salathé. “This is not possible if the AI is privatized.”

AI is both the algorithm and the data

So what exactly is AI? It is generally regarded as “intelligence exhibited by machines”. Today, it is highly task specific, specially designed to beat humans at strategic games like Chess and Go, or diagnose skin disease on par with doctors’ skills.

On a practical level, AI is implemented through what scientists call “machine learning”, which means using a computer to run specifically designed software that can be “trained”, i.e. process data with the help of algorithms and to correctly identify certain features from that data set. Like human cognition, AI learns by trial and error. Unlike humans, however, AI can process and recall large quantities of data, giving it a tremendous advantage over us.

Crucial to AI learning, therefore, is the underlying data. For Salathé, AI is defined by both the algorithm and the data, and as such, both should be publicly available.

Deep learning algorithms can be perturbed

Last year, Salathé created an algorithm to recognize plant diseases. With more than 50,000 photos of healthy and diseased plants in the database, the algorithm uses artificial intelligence to diagnose plant diseases with the help of your smartphone. As for human disease, a recent study by a Stanford Group on cancer showed that AI can be trained to recognize skin cancer slightly better than a group of doctors. The consequences are far-reaching: AI may one day diagnose our diseases instead of doctors. If so, will we really be able to trust its diagnosis?

These diagnostic tools use data sets of images to train and learn. But visual data sets can be perturbed that prevent deep learning algorithms from correctly classifying images. Deep neural networks are highly vulnerable to visual perturbations that are practically impossible to detect with the naked eye, yet causing the AI to misclassify images.

In future implementations of AI-assisted medical diagnostic tools, these perturbations pose a serious threat. More generally, the perturbations are real and may already be affecting the filtered information that reaches us every day. These vulnerabilities underscore the importance of certifying AI technology and monitoring its reliability.

h/t phys.org March 15, 2017 news item

As I noted earlier, these are not the kind of presentations you’d expect at an ‘entertainment’ festival.

Policy makers, beware experts! And, evidence too

There is much to admire in this new research but there’s also a troubling conflation.

An Oct. 14, 2015 University of Cambridge press release (also on EurekAlert) cautions policy makers about making use of experts,

The accuracy and reliability of expert advice is often compromised by “cognitive frailties”, and needs to be interrogated with the same tenacity as research data to avoid weak and ill-informed policy, warn two leading risk analysis and conservation researchers in the journal Nature today.

While many governments aspire to evidence-based policy [emphasis mine], the researchers say the evidence on experts themselves actually shows that they are highly susceptible to “subjective influences” – from individual values and mood, to whether they stand to gain or lose from a decision – and, while highly credible, experts often vastly overestimate their objectivity and the reliability of peers.

They appear to be conflating evidence and expertise. Evidence usually means data while expertise is a more ephemeral concept. (Presumably, an expert is someone whose opinion is respected for one reason or another and who has studied the evidence and drawn some conclusions from it.)

The study described in the press release notes that one of the weaknesses of relying on experts is that they are subject to bias. They don’t mention that evidence or data can also be subject to bias but perhaps that’s why they suggest the experts should provide and assess the evidence on which they are basing their advice,

The researchers caution that conventional approaches of informing policy by seeking advice from either well-regarded individuals or assembling expert panels needs to be balanced with methods that alleviate the effects of psychological and motivational bias.

They offer a straightforward framework for improving expert advice, and say that experts should provide and assess [emphasis mine] evidence on which decisions are made – but not advise decision makers directly, which can skew impartiality.

“We are not advocating replacing evidence with expert judgements, rather we suggest integrating and improving them,” write professors William Sutherland and Mark Burgman from the universities of Cambridge and Melbourne respectively.

“Policy makers use expert evidence as though it were data. So they should treat expert estimates with the same critical rigour that must be applied to data,” they write.

“Experts must be tested, their biases minimised, their accuracy improved, and their estimates validated with independent evidence. Put simply, experts should be held accountable for their opinions.”

Sutherland and Burgman point out that highly regarded experts are routinely shown to be no better than novices at making judgements.

However, several processes have been shown to improve performances across the spectrum, they say, such as ‘horizon scanning’ – identifying all possible changes and threats – and ‘solution scanning’ – listing all possible options, using both experts and evidence, to reduce the risk of overlooking valuable alternatives.

To get better answers from experts, they need better, more structured questions, say the authors. “A seemingly straightforward question, ‘How many diseased animals are there in the area?’ for example, could be interpreted very differently by different people. Does it include those that are infectious and those that have recovered? What about those yet to be identified?” said Sutherland, from Cambridge’s Department of Zoology.

“Structured question formats that extract upper and lower boundaries, degrees of confidence and force consideration of alternative theories are important for shoring against slides into group-think, or individuals getting ascribed greater credibility based on appearance or background,” he said.

When seeking expert advice, all parties must be clear about what they expect of each other, says Burgman, Director of the Centre of Excellence for Biosecurity Risk Analysis. “Are policy makers expecting estimates of facts, predictions of the outcome of events, or advice on the best course of action?”

“Properly managed, experts can help with estimates and predictions, but providing advice assumes the expert shares the same values and objectives as the decision makers. Experts need to stick to helping provide and assess evidence on which such decisions are made,” he said.

Sutherland and Burgman have created a framework of eight key ways to improve the advice of experts. These include using groups – not individuals – with diverse, carefully selected members well within their expertise areas.

They also caution against being bullied or “starstruck” by the over-assertive or heavyweight. “People who are less self-assured will seek information from a more diverse range of sources, and age, number of qualifications and years of experience do not explain an expert’s ability to predict future events – a finding that applies in studies from geopolitics to ecology,” said Sutherland.

Added Burgman: “Some experts are much better than others at estimation and prediction. However, the only way to tell a good expert from a poor one is to test them. Qualifications and experience don’t help to tell them apart.”

“The cost of ignoring these techniques – of using experts inexpertly – is less accurate information and so more frequent, and more serious, policy failures,” write the researchers.

Here’s a link to and a citation for the paper,

Policy advice: Use experts wisely by William J. Sutherland & Mark Burgman. Nature 526, 317–318 (15 October 2015) doi:10.1038/526317a

It’s good to see a nuanced attempt to counteract mindless adherence to expert opinion. I hope they will include evidence and data as  needing to be approached cautiously in future work.

More on US National Nanotechnology Initiative (NNI) and EHS research strategy

In my Oct, 18, 2011 posting I noted that the US National Nanotechnology Initiative (NNI) would be holding a webinar on Oct. 20, 2011 to announce an environmental, health, and safety (EHS) research strategy for federal agencies participating in the NNI. I also noted that I was unable to register for the event. Thankfully all is not lost. There are a couple of news items on Nanowerk which give some information about the research strategy. The first news item, U.S. government releases environmental, health, and safety research strategy for nanotechnology, from the NNI offers this,

The strategy identifies six core categories of research that together can contribute to the responsible development of nanotechnology: (1) Nanomaterial Measurement Infrastructure, (2) Human Exposure Assessment, (3) Human Health, (4) Environment, (5) Risk Assessment and Risk Management, and (6) Informatics and Modeling. The strategy also aims to address the various ethical, legal, and societal implications of this emerging technology. Notable elements of the 2011 NNI EHS Research Strategy include:

  • The critical role of informatics and predictive modeling in organizing the expanding nanotechnology EHS knowledge base;
  • Targeting and accelerating research through the prioritization of nanomaterials for research; the establishment of standardized measurements, terminology, and nomenclature; and the stratification of knowledge for different applications of risk assessment; and
  • Identification of best practices for the coordination and implementation of NNI interagency collaborations and industrial and international partnerships. “The EHS Research Strategy provides guidance to all the Federal agencies that have been producing gold-standard scientific data for risk assessment and management, regulatory decision making, product use, research planning, and public outreach,” said Dr. Sally Tinkle, NNI EHS Coordinator and Deputy Director of the National Nanotechnology Coordination Office (NNCO), which coordinates activities of the 25 agencies that participate in the NNI. “This continues a trend in this Administration of increasing support for nanotechnology-related EHS research, as exemplified by new funding in 2011 from the Food and Drug Administration and the Consumer Product Safety Commission and increased funding from both the Environmental Protection Agency and the National Institute of Occupational Safety and Health within the Centers for Disease Control and Prevention.”

The other news item, Responsible development of nanotechnology: Maximizing results while minimizing risk, from Sally Tinkle, Deputy Director of the National Nanotechnology Coordination Office and Tof Carim, Assistant Director for Nanotechnology at OSTP (White House Office of Science and Technology Policy) adds this,

Core research areas addressed in the 2011 strategy include: nanomaterial measurement, human exposure assessment, human health, environment, risk assessment and management, and the new core area of predictive modeling and informatics. Also emphasized in this strategy is a more robust risk assessment component that incorporates product life cycle analysis and ethical, legal, and societal implications of nanotechnology. Most importantly, the strategy introduces principles for targeting and accelerating nanotechnology EHS research so that risk assessment and risk management decisions are based on sound science.

Progress in EHS research is occurring on many fronts as the NNI EHS research agencies have joined together to plan and fund research programs in core areas. For example, the Food and Drug Administration and National Institutes of Health have researched the safety of nanomaterials used in skin products like sunscreen; the Environmental Protection Agency and Consumer Product Safety Commission are monitoring the health and environmental impacts of products containing silver nanoparticles, and National Institute of Occupational Safety and Health has recommended safe handling guidelines for workers in industries and laboratories.

Erwin Gianchandani of the Computing Community Consortium blog focuses, not unnaturally, on the data aspect of the research strategy in his Oct. 20, 2011 posting titled, New Nanotechnology Strategy Touts Big Data, Modeling,

From the EHS Research Strategy:

Expanding informatics capabilities will aid development, analysis, organization, archiving, sharing, and use of data that is acquired in nanoEHS research projects… Effective management of reliable, high-quality data will also help support advanced modeling and simulation capabilities in support of future nanoEHS R&D and nanotechnology-related risk management.

Research needs highlighted span “Big Data”…

Data acquisition: Improvements in data reliability and reproducibility can be effected quickly by leveraging the widespread use of wireless and video-enabled devices by the public and by standards development organizations to capture protocol detail through videos…

Data analysis: The need for sensitivity analysis in conjunction with error and uncertainty analysis is urgent for hazard and exposure estimation and the rational design of nanomaterials… Collaborative efforts in nanomaterial design [will include] curation of datasets with known uncertainties and errors, the use of sensitivity analysis to predict changes in nanomaterial properties, and the development of computational models to augment and elucidate experimental data.

Data sharing: Improved data sharing is a crucial need to accelerate progress in nanoscience by removing the barriers presented by the current “siloed” data environment. Because data must be curated by those who have the most intimate knowledge of how it was obtained and analyzed and how it will be used, a central repository to facilitate sharing is not an optimal solution. However, federating database systems through common data elements would permit rapid semantic search and transparent sharing over all associated databases, while leaving control and curation of the data in the hands of the experts. The use of nanomaterial ontologies to define those data elements together with their computer-readable logical relationships can provide a semantic search capability.

…and predictive modeling:

Predictive models and simulations: The turnaround times for the development and validation of predictive models is measured in years. Pilot websites, applications, and tools should be added to the NCN [Network for Computational Nanotechnology] to speed collaborative code development among relevant modeling and simulation disciplines, including the risk modeling community. The infrastructure should provide for collaborative code development by public and private scientists, code validation exercises, feedback through interested user communities, and the transfer of validated versions to centers such as NanoHUB… Collaborative efforts could supplement nanomaterial characterization measurements to provide more complete sensitivity information and structure-property relationships.

Gianchandani’s post provides an unusual insight into the importance of data where research is considered. I do recommend more of his posting.

Dr. Andrew Maynard on his 2020 Science blog has posted as of Oct. 20, 2011 with a comparison of the original draft to the final report,

Given the comments received, I was interested to see how much they had influenced the final strategy.  If you take the time to comment on a federal document, it’s always nice to know that someone has paid attention.  Unfortunately, it isn’t usual practice for the federal government to respond directly to public comments, so I had the arduous task of carrying out a side by side comparison of the draft, and today’s document.

As it turns out, there are extremely few differences between the draft and the final strategy, and even fewer of these alter the substance of the document.  Which means that, by on large, my assessment of the document at the beginning of the year still stands.

Perhaps the most significant changes were on chapter 6 – Risk Assessment and Risk Management Methods. The final strategy presents a substantially revised set of current research needs, that more accurately and appropriately (in my opinion) reflect the current state of knowledge and uncertainty (page 66).  This is accompanied by an updated analysis of current projects (page 73), and additional text on page 77 stating

“Risk communication should also be appropriately tailored to the targeted audience. As a result, different approaches may be used to communicate risk(s) by Federal and state agencies, academia, and industry stakeholders with the goal of fostering the development of an effective risk management framework.”

Andrew examines the document further,

Comparing the final strategy to public comments from Günter Oberdörster [professor of Environmental Medicine at the University of Rochester in NY state] on the draft document. I decided to do this as Günter provided some of the most specific public comments, and because he is one of the most respected experts in the field.  The specificity of his comments also provided an indication of the extent to which they had been directly addressed in the final strategy.

Andrew’s post is well worth reading especially if you’ve ever made a submission to a public consultation held by your government.

The research strategy and other associated documents are now available for access and the webinar will be available for viewing at a later date. Go here.

Aside, I was a little surprised that I was unable to register to view the webinar live (I wonder if I’ll encounter the same difficulties later). It’s the first time I’ve had a problem viewing any such event hosted by a US government agency.