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February 24 2012

Four short links: 24 February 2012

  1. Excel Cloud Data Analytics (Microsoft Research) -- clever--a cloud analytics backend with Excel as the frontend. Almost every business and finance person I've known has been way more comfortable with Excel than any other tool. (via Dr Data)
  2. HTTP Client -- Mac OS X app for inspecting and automating a lot of HTTP. cf the lovely Charles proxy for debugging. (via Nelson Minar)
  3. The Creative Destruction of Medicine -- using big data, gadgets, and sweet tech in general to personalize and improve healthcare. (via New York Times)
  4. EFF Wins Protection of Time Zone Database (EFF) -- I posted about the silliness before (maintainers of the only comprehensive database of time zones was being threatened by astrologers). The EFF stepped in, beat back the buffoons, and now we're back to being responsible when we screw up timezones for phone calls.

January 25 2012

AI will eventually drive healthcare, but not anytime soon

TechCrunch recently published a guest post from Vinod Khosla with the headline "Do We Need Doctors or Algorithms?". Khosla is an investor and engineer, but he is a little outside his depth on some of his conclusions about health IT.

Let me concede and endorse his main point that doctors will become bionic clinicians by teaming with smart algorithms. He is also right that eventually the best doctors will be artificial intelligence (AI) systems — software minds rather than human minds.

That said, I disagree with Khosla on almost all of the details. Khosla has accidentally embraced a perspective that too many engineers and software guys bring to health IT.

Bear with me — I am the guy trying to write the "House M.D." AI algorithms that Khosla wants. It's harder than he thinks because of two main problems that he's not considering: The search space problem and the good data problem.

The search space problem

Any person even reasonably informed about AI knows about Go, an ancient game with simple rules. Those simple rules hide the fact that Go is a very complex game indeed. For a computer, it is much harder to play than chess.

Almost since the dawn of computing, chess was regarded as something that required intelligence and was therefore a good test of AI. In 1997, the world chess champion was beaten by a computer. In the year after, a professional Go player beat the best Go software in the world with a 25 stone handicap. Artificial intelligence experts study Go carefully precisely because it is so hard for computers. The approach that computers take toward being smart — thinking of lots of options really fast — stops working when the number of options skyrockets, and the number of potentially right answers also becomes enormous. Most significantly, Go can always be made more computationally difficult by simply expanding the board.

Make no mistake, the diagnosis and treatment of human illness is like Go. It's not like chess. Khosla is making a classic AI mistake, presuming that because he can discern the rules easily, it means the game is simple. Chess has far more complex rules than Go, but it ends up being a simpler game for computers to play.

To be great at Go, software must learn to ignore possibilities, rather than searching through them. In short, it must develop "Go instincts." The same is true for any software that could claim to be a diagnostician.

How can you tell when software diagnosticians are having search problems? When they cannot tell the difference between all of the "right" answers to a particular problem. The average doctor does not need to be told "could it be Zebra Fever?" by a computer that cannot tell that it should have ignored any zebra-related possibilities because it is not physically located in Africa. (No zebras were harmed in the writing of this article, and I do not believe there is a real disease called Zebra Fever.)

The good data problem

The second problem is the good data problem, which is what I spend most of my time working on.

Almost every time I get over-excited about the Direct Project or other health data exchange progress, my co-author David Uhlman brings me back to earth:

What good is it to have your lab results transferred from hospital A to hospital B using secure SMTP and XML? They are going to re-do the labs anyway because they don't trust the other lab.

While I still have hope for health information exchange in the long term, David is right in the short term. Healthcare data is not remotely solid or trustworthy. A good majority of the time, it is total crap. The reason that doctors insist on having labs done locally is not because they don't trust the competitor's lab; it's more of a "devil that you know" effect. They do not trust their own labs either, but they have a better understanding of how and when their own labs screw up. That is not a good environment for medical AI to blossom.

The simple reality is that doctors have good reason to be dubious about the contents of an EHR record. For lots of reasons, not the least of which is that the codes they are potentially entering there are not diagnostically helpful or valid.

Non-healthcare geeks presume that the dictionaries and ontologies used to encode healthcare data are automatically valid. But in fact, the best assumption is that ontologies consistently lead to dangerous diagnostic practices, as they shepherd clinicians into choosing a label for a condition rather than a true diagnosis. Once a patient's chart has a given label, either for diagnosis or for treatment, it can be very difficult to reassess that patient effectively. There is even a name for this problem: clinical inertia. Clinical inertia is an issue with or without computer software involved, but it is very easy for an ontology of diseases and treatments to make clinical inertia worse. The fact is, medical ontologies must be constantly policed to ensure that they do not make things worse, rather then better.

It simply does not matter how good the AI algorithm is if your healthcare data is both incorrect and described with a faulty healthcare ontology. My personal experiences with health data on a wide scale? It's like having a conversation with a habitual liar who has a speech impediment.

So Khosla is not "wrong" per-se; he's just focused on solving the wrong parts of the problem. As a result, his estimations of when certain things will happen are pretty far off.

I believe that we will not have really good diagnostic software until after the singularity and until after we can ensure that healthcare data is reliable. I actually spend most of my time on the second problem, which is really a sociological problem rather then a technology problem.

Imagine if we had a "House AI" before we were able to feed it reliable data? Ironically it would be very much like the character on TV: constantly annoyed that everyone around him keeps screwing up and getting in his way.

Anyone who has seen the show knows that the House character is constantly trying to convince the other characters that the patients are lying. The reality is that the best diagnosticians typically assume that the chart is lying before they assume that the patient is lying. With notable exceptions, the typical patient is highly motivated to get a good diagnosis and is, therefore, honest. The chart, on the other hand, be it paper or digital, has no motivation whatsoever, and it will happily mix in false lab reports and record inane diagnoses from previous visits.

The average doctor doubts the patient chart but trusts the patient story. For the foreseeable future, that is going to work much better than an algorithmically focused approach.

Eventually, Khosla's version of the future (which is typical of forward-thinking geeks in health IT) will certainly happen, but I think it is still 30 years away. The technology will be ready far earlier. Our screwed up incentive systems and backward corporate politics will be holding us back. I hardly have to make this argument, however, since Hugo Campos recently made it so well.

Eventually, people will get better care from AI. For now, we should keep the algorithms focused on the data that we know is good and keep the doctors focused on the patients. We should be worried about making patient data accurate and reliable.

I promise you we will have the AI problem finished long before we have healthcare data that is reliable enough to train it.

Until that happens, imagine how Watson would have performed on "Jeopardy" if it had been trained on "Lord of the Rings" and "The Cat in the Hat" instead of encyclopedias. Until we have healthcare data that is more reliable than "The Cat in the Hat," I will keep my doctor, and you can keep your algorithms, thank you very much.

Meaningful Use and Beyond: A Guide for IT Staff in Health Care — Meaningful Use underlies a major federal incentives program for medical offices and hospitals that pays doctors and clinicians to move to electronic health records (EHR). This book is a rosetta stone for the IT implementer who wants to help organizations harness EHR systems.

Related:

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Reposted bySchrammelhammelMrCoffeinmybetterworldkonikonikonikonikoniambassadorofdumbgroeschtlNaitliszpikkumyygittimmoejeschge

December 12 2011

December 07 2011

Four short links: 7 December 2011

  1. Don't Be a Free User (Maciej Ceglowski) -- pay for your free services, else they'll go away.
  2. Katta -- Lucene for massive data sets in the cloud. (via Pete Warden)
  3. Old Weather -- crowdsourced transcription of old nautical journals to yield historical information for climate researchers. (via National Digital Forum)
  4. Siddhartha Mukherjee Talks About Cancer (Guardian) -- fascinating profile of the author of a "biography of cancer". Touches on the cognitive biases we're all prone to, and their damaging effects on patients. Mukherjee cites a study which found that women with breast cancer recalled eating a high-fat diet, whereas women without cancer did not. But the very same study had asked both sets of women about their diets long before any of them developed cancer, and the diet of those who now had breast cancer had been no more fatty than the rest (via Courtney Johnston)

November 15 2011

Four short links: 15 November 2011

  1. Cost-Effectiveness of Internet-Based Self-Management Compared with Usual Care in Asthma (PLoSone) -- Internet-based self-management of asthma can be as effective as current asthma care and costs are similar.
  2. Apache Lucy -- full-text search engine library written in C and targeted at dynamic languages. It is a "loose C" port of Apache Lucene™, a search engine library for Java.
  3. The Near Future of Citizen Science (Fiona Romeo) -- near future of science is all about honing the division of labour between professionals, amateurs and bots. See Bryce's bionic software riff. (via Matt Jones)
  4. Microsoft's Patent Claims Against Android (Groklaw) -- behold, citizen, the formidable might of Microsoft's patents and how they justify a royalty from every Android device equal to that which you would owe if you built a Windows Mobile device: These Microsoft patents can be divided into several basic categories: (1) the '372 and '780 patents relate to web browsers; (2) the '551 and '233 patents relate to electronic document annotation and highlighting; (3) the '522 patent relates to resources provided by operating systems; (4) the '517 and '352 patents deal with compatibility with file names once employed by old, unused, and outmoded operating systems; (5) the '536 and '853 patents relate to simulating mouse inputs using non-mouse devices; and (6) the '913 patent relates to storing input/output access factors in a shared data structure. A shabby display of patent menacing.

November 09 2011

Four short links: 9 November 2011

  1. The Social Graph is Neither -- Maciej Ceglowski nails it. Imagine the U.S. Census as conducted by direct marketers - that's the social graph. Social networks exist to sell you crap. The icky feeling you get when your friend starts to talk to you about Amway, or when you spot someone passing out business cards at a birthday party, is the entire driving force behind a site like Facebook.
  2. Anonymous 101 (Wired) -- Quinn Norton explains where Anonymous came from, what it is, and why it is.
  3. Antibiotic Resistance (The Atlantic) -- Laxminarayan likens antibiotics resistance to global warming: every country needs to solve its own problems and cooperate—but if it doesn't, we all suffer. This is why we can't have nice things. (via Courtney Johnston)
  4. Deep Idle for Android -- developer saw his handset wasn't going into a deep-enough battery-saving idle mode, saw it wasn't implemented in the kernel, implemented it, and reduced battery consumption by 55%. Very cool to see open source working as it's supposed to. (via Leonard Lin)

October 14 2011

TERRA 613: Biofilm

"Biofilm" is an introduction to a relatively new field of medical research that investigates bacteria colonies known as biofilm. Recent research has shown that biofilm colonies often inhabit wounds of immuno-compromised patients, which consequently result in chronic wounds (wounds that remain open for months and years). Biofilm infections are difficult to treat and can lead to the amputation of limbs.

August 17 2011

Four short links: 17 August 2011

  1. Tablib -- MIT-licensed open source library for manipulating tabular data. Reputed to have a great API. (via Tim McNamara)
  2. Stanford Education Everywhere -- courses in CS, machine learning, math, and engineering that are open for all to take. Over 58,000 have already signed up for the introduction to machine learning taught by Peter Norvig, Google's Director of Research.
  3. Wearable LED Television -- 160x120 RGBs powered by a 12v battery, built for Burning Man (natch). (via Bridget McKendry)
  4. Temporary Tattoo Biosensors (Science News) -- early work putting flexible sensors into temporary tattoos. (via BoingBoing)

June 25 2010

Four short links: 25 June 2010

  1. Membase -- an open-source (Apache 2.0 license) distributed, key-value database management system optimized for storing data behind interactive web applications. These applications must service many concurrent users; creating, storing, retrieving, aggregating, manipulating and presenting data in real-time. Supporting these requirements, membase processes data operations with quasi-deterministic low latency and high sustained throughput. (via Hacker News)
  2. Sergey's Search (Wired) -- Sergey Brin, one of the Google founders, learned he had a gene allele that gave him much higher odds of getting Parkinson's. His response has been to help medical research, both with money and through 23andme. Langston decided to see whether the 23andMe Research Initiative might be able to shed some insight on the correlation, so he rang up 23andMe’s Eriksson, and asked him to run a search. In a few minutes, Eriksson was able to identify 350 people who had the mutation responsible for Gaucher’s. A few clicks more and he was able to calculate that they were five times more likely to have Parkinson’s disease, a result practically identical to the NEJM study. All told, it took about 20 minutes. “It would’ve taken years to learn that in traditional epidemiology,” Langston says. “Even though we’re in the Wright brothers early days with this stuff, to get a result so strongly and so quickly is remarkable.”
  3. Startup.gov (YouTube) -- Anil Dash talk at Personal Democracy Forum on applying insights from startups to government. I hope the more people say this, the greater the odds it'll be acted on.
  4. Open Core Software -- Marten Mickos (ex-MySQL) talks up "open core" (open source base, proprietary extensions) as a way to resolve the conflict of "change the world with open source" and "make money". Brian Aker disagrees: There has been no successful launch of an open core company that has reached any significant size, especially of the size that Marten hints at in the article. My take: there are three reasons for open source (freedoms, price, and development scale) and if you close the source to part of your product then the whole product loses those benefits. If you open source enough that the open source bit has massive momentum, then you probably don't have enough left proprietary to gain huge financial benefit.

April 27 2010

Four short links: 27 April 2010

  1. Five Short Links -- Pete Warden's one-upped me. I will not join this arms race, for I know that it will end with "5,187 short links". Pete has interesting crawling, geodata, and startup links.
  2. Ticketmaster Consent Decree -- the source code to their ticketing platform is affected by the consent to merger. (via Redmonk)
  3. Doctors Make Game Out of Learning Infection Control -- A table-top card battle game, "The Healing Blade" is built around a fantasy world, complete with sorcerers, villains and heroines that the two doctors created. Characters are divided into The Apothecary Healers, named after real-world antibiotics, and The Lords of Pestilence, named after actual bacterial agents. Teaches med students how to match antibiotics to infecting agents. (via Hacker News)
  4. The Visualization Cargo Cult -- eyecandy is not informative. The candy examples are wincesome.

February 14 2010

Die Dame und der Tod

La Dama y La Muerte” ist eine Computeranimation von Kandor Moon. Sie erzählt auf ungewöhnliche Weise die Geschichte einer alten Dame, die im Sterben liegt und von dem Kampf zwischen dem Sensenmann und ihrem Arzt.

Das spanische Filmstudio Kandor Moon wurde von Antonio Banderas Firma Green Moon und Kandor Graphics aus der Taufe gehoben. Der Film ist für einen Oscar nominiert.

(Gefunden bei acriacao)

Reposted fromglaserei glaserei

February 11 2010

Data not drugs

We have access to more health information now than any time in history, yet this deluge of medical data may sometimes make health decisions more difficult. The Internet has opened a Pandora’s Box of data that can easily overwhelm us. We need a way to process all this information to assist us in making better healthcare decisions. Sifting through the barrage of health information writhing across the Internet can be a challenge and new sources are continually cropping up.

There are some great online resources that can help. Search sites are now guiding consumers to safe, trusted health websites, says Susannah Fox of Pew Internet. Both Google and Bing are entering the health search arena by providing a highlight at the top of health related searches that allow you to access a wealth of information. Compare the results of a search on “type 2 diabetes” from Google and Bing. While they both present relevant articles from their libraries of health resources and present a summary related to the query, so far I have seen better results from Bing but Google is gaining fast. I expect both will continue to refine these algorithms to improve results. Google has also updated their popular Flu Trends providing flu info for 121 U.S. cities. Previously, flu trends were available on a state and country level.

WolframAlpha can calculate clinical markers on cholesterol levels, BMI and a wide range of indicators. WolframAlpha computes a breakdown of total calories, fat, cholesterol, sodium, carbohydrates, protein, and other particular nutrients of most foods. And then there is the incredible Genetics Home Reference by the National Library of Medicine which provides consumer-friendly information about the effects of genetic variations on human health. The “type 2 diabetes” search here gives some very interesting results with detailed genetic information and many links to additional valuable resources.

One strategy to cope with all this data and help to create a framework for our medical decisions is to use a decision tree. In his new book, The Decision Tree: Taking Control of Your Health in the New Era of Personalized Medicine, Thomas Goetz offers a structure to reduce uncertainty and allow us to make better choices. I was fortunate to read an advance copy of the book a few months ago and have had a series of interesting conversations with Thomas since then to discuss some aspects of the book. The decision tree is basically a flow chart to move us towards better healthcare choices. I am most impressed that he could take the rather complicated subject matter of personalized medicine and distill it into layman’s terms that make for an interesting and compelling read. The book will be released on February 16, 2010 and I highly recommend it. You can read Chapter 1 today on The Decision Tree blog.

It is basically divided into three sections: prevention, diagnosis and treatment; it is Thomas’s contention that we are all moving along this spectrum and our baseline is our DNA. One of the themes of the book is that by knowing and better understanding our genetic makeup, we can improve the medical decision making process. Spring boarding from a future of genetic medicine envisioned by Dr. George Church’s Personal Genome Project he leads us to the current state of personalized medicine with services like 23andMe and Navigenics which offer genetic testing. But with the cost of these tests, is this really for the average patient? “The price of genetic sequencing is falling rapidly," Thomas said, "but I’m not actually calling people to start with genomics." There are some basic starting points for using the decision tree strategy that don’t wholly rely on having your genome sequenced. The widget below gives you an idea of approach he takes:

The book describes his participation in a Quantified Self meeting, a sort of show and tell for people taking advantage of various kinds of personal tracking methods like geotracking, life-logging, DNA sequencing, etc. They track the various metrics in an effort to find quantifiable meaning to the data. These folks are "geeking out... just like the guys who stand in line for iPhones and then rush home and take them apart to see how they're made. They're just the same. Except in this case, the iPhones are their own bodies." While it is important to have as complete information as possible to make better choices that will improve our health, he said, "Tracking your health with gadgets and gizmos is not for everyone. But not long ago no one even knew what their blood pressure of cholesterol level was and now tracking these metrics is quite common." Tracking our health metrics and combining this with genetic data to use as a starting point for a decision tree can help us to make choices that will improve our health. Whether it is simply taking our blood pressure, or using the Twitter-based GetUpAndMove service started by Jen McCabe, keeping track of what our bodies are doing and what we do with them is good data to have.

One of the problems we face in dealing with health issues is finding drugs that actually help, and a chapter in Goetz's book deals with the drug problem in healthcare today. While modern pharmacology has developed many drugs that have saved and improved lives, finding the right drug for the right condition is a challenge. And the blockbuster model used for research and development in the pharmaceutical industry is fading out. Some of the the promising drugs now in the pipeline are designed for smaller groups of patients. But as chapter 8 points out:
The pipeline of new drugs has slowed to a crawl, as one promising candidate after another has petered out in the last phases of development. "The low-hanging fruit has been picked," says Derek Lowe, PhD, a drug discovery chemist and industry pundit. William Haseltine, PhD, a former researcher at Harvard Medical School and the founder of Human Genome Sciences and eight other biotechnology companies, notes that fewer than 1 in 100 new ideas reaches clinical trials and fewer than 10 percent of these are approved for sale.
But developing drugs for less common medical problems will require major changes for the industry. When promising molecules are found, and the chemistry works, the drug companies are anxious to get these to market. And all of those "failed" clinical trials (which may have produced valuable data) are locked away, leaving possible medicine for "lesser" conditions undiscovered. Goetz lays out a hopeful possible future where we enter into a new era of research that will rescue drugs and free the data that will help people to live healthier lives.

Drug ads are also sometimes misleading. By ambiguously defining who might need or benefit from the products advertised, they focus "on convincing people that they may be at risk for a wide array of health conditions" rather than genuinely educating consumers, concluded a 2007 study in the Annals of Family Medicine. Drug manufacturers maintain that their ads are not misleading. The Pharmaceutical Research and Manufacturers of America, an industry group, says: "Consistent with recently updated guidelines, PhRMA is committed to a fair balance of risk and benefit information in all direct-to-consumer advertising." The "brief" summaries in direct-to-consumer drug ads can take up a whole magazine page, and make it very difficult for a consumer to understand and weigh the risks and benefits of the medication.

Drug fact boxes are a possible solution that could eliminate a lot of the ambiguity. These user friendly boxes, similar to the nutrition labels found on food, include facts not found in the so-called brief summaries.They are the brainchild of a husband-and-wife team: physician-researchers Lisa Schwartz and Steven Woloshin from the Dartmouth Institute for Health Policy and Clinical Practice. In a study published by the Annals of Internal Medicine, they tested how much consumers could benefit from understandable information on drug products. The data were collected via mailed surveys measuring respondents' reactions to two drug fact boxes versus traditional direct-to-consumer advertisements. One trial compared two potential treatments for heartburn and the other compared two potential preventive medications for cardiovascular events. Of respondents who received the drug fact box on heartburn, 70 percent were able to correctly identify the most effective treatment, as compared to 8 percent of the control group. It's obvious that drug fact boxes have the potential to improve consumers' knowledge of the potential benefits and side effects of medications. The FDA is considering requiring factboxes so there is hope but the wheels of government grind slowly.

The book lays out three fundamental principles for making intelligent health choices. Number one is early is better than late. By learning genetic predispositions we can treat disease, sometimes before it even happens. Number two is let the data do the work. Using evidence-based medicine and monitoring the continuous stream of data we create, whether it be our diet, exercise, moods or DNA, gives us a baseline from which we can evaluate our future health. And number three is openness is a powerful thing. The more accurate information available to researchers, care providers, and consumers, the better decisions we can all make and the more options we will have for successful outcomes.

By making the most of the new science and technologies available and using the best practices from genetics, behavioral science, and information technology we have the opportunity to sculpt a process to better manage our health. Putting the patient at the center of healthcare and creating a strategy to process all of health data available today is a great start towards meaningful healthcare reform. While Congress debates payment methodologies, health insurance, and all of the political considerations which crowd into the discussion, we the people can take more control of our health today. After all, they are our bodies...

November 16 2009

Four short links: 16 November 2009

  1. Choose Your Own Adventure -- numerical and visual analysis of the Choose Your Own Adventure novels. The distinguishing characteristic of My Kind Of People is that they appreciate the quantitative study of the commonplace. (via Bryan O'Sullivan)
  2. Tracking Droid Numbers -- uLocate, the makers of the Where app for Android, have been tracking the growth of the Droid phone using the data they get from the Android app store. (via BoyGenius Report)
  3. Fly Eyes Makes Better Robot Vision -- to make smaller flying robots, researchers would like to find a simpler way of processing motion. Inspiration has come from the lowly fly, which uses just a relative handful of neurons to maneuver with extraordinary dexterity. And for more than a decade, O’Carroll and other researchers researchers have painstakingly studied the optical flight circuits of flies, measuring their cell-by-cell activity and turning evolution’s solutions into a set of computational principles. [...] Intriguingly, the algorithm doesn’t work nearly as well if any one operation is omitted. The sum is greater than the whole, and O’Carroll and Brinkworth don’t know why. Because the parameters are in constant feedback-driven flux, it produces a cascade of non-linear equations that are difficult to untangle in retrospect, and almost impossible to predict. (via Slashdot)
  4. Meat Band Aids and Mass Production of Living Tissue -- Apligraf is a matrix of cow collagen, human fibroblasts and keratinocyte stem cells (from discarded circumcisions), that, when applied to chronic wounds (particularly nasty problems like diabetic sores), can seed healing and regeneration. This Gizmodo Q&A is informative.

November 10 2009

Four short links: 10 November 2009

  1. A children’s toy inspires a cheap, easy production method for high-tech diagnostic chips -- microfluidic chips (with tiny liquid-filled channels) can cost $100k and more. Michelle Khine used the Shrinky Dinks childrens' toy to make her own. "I thought if I could print out the [designs] at a certain resolution and then make them shrink, I could make channels the right size for micro­fluidics," she says. (via BoingBoing)
  2. Complete Genomics publishes in Science on low-cost sequencing of 3 human genomes (press release) -- The consumables cost for these three genomes sequenced on the proof-of-principle genomic DNA nanoarrays ranged from $8,005 for 87x coverage to $1,726 for 45x coverage for the samples described in this report. Drive that cost down! There's a gold rush in biological discovery at the moment as we pick the low-hanging fruit of gross correlations between genome and physiome, but the science to reveal the workings of cause and effect is still in its infancy. We're in the position of the 18th century natural philosophers who were playing with static electricity, oxygen, anaesthetics, and so on but who lacked today's deeper insights into physical and chemical structure that explain the effects they were able to obtain. More data at this stage means more low-hanging fruit can be plucked, but the real power comes when we understand "how" and not just "what". (via BoingBoing)
  3. Far From a Lab? Turn a Cellphone into a Microscope (NY Times) -- for some tests, you can use a camphone instead of a microscope. In one prototype, a slide holding a finger prick of blood can be inserted over the phone’s camera sensor. The sensor detects the slide’s contents and sends the information wirelessly to a hospital or regional health center. For instance, the phones can detect the asymmetric shape of diseased blood cells or other abnormal cells, or note an increase of white blood cells, a sign of infection, he said.
  4. Augmented reality helps Marine mechanics carry out repair work (MIT TR) -- A user wears a head-worn display, and the AR system provides assistance by showing 3-D arrows that point to a relevant component, text instructions, floating labels and warnings, and animated, 3-D models of the appropriate tools. An Android-powered G1 smart phone attached to the mechanic's wrist provides touchscreen controls for cueing up the next sequence of instructions. [...] The mechanics using the AR system located and started repair tasks 56 percent faster, on average, than when wearing the untracked headset, and 47 percent faster than when using just a stationary computer screen.

October 09 2009

America's economy reformed?

A year after economic calamity brought promises of reform, is Wall Street back to business as usual?
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