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#434648 The Pediatric AI That Outperformed ...
Training a doctor takes years of grueling work in universities and hospitals. Building a doctor may be as easy as teaching an AI how to read.
Artificial intelligence has taken another step towards becoming an integral part of 21st-century medicine. New research out of Guangzhou, China, published February 11th in Nature Medicine Letters, has demonstrated a natural-language processing AI that is capable of out-performing rookie pediatricians in diagnosing common childhood ailments.
The massive study examined the electronic health records (EHR) from nearly 600,000 patients over an 18-month period at the Guangzhou Women and Children’s Medical Center and then compared AI-generated diagnoses against new assessments from physicians with a range of experience.
The verdict? On average, the AI was noticeably more accurate than junior physicians and nearly as reliable as the more senior ones. These results are the latest demonstration that artificial intelligence is on the cusp of becoming a healthcare staple on a global scale.
Less Like a Computer, More Like a Person
To outshine human doctors, the AI first had to become more human. Like IBM’s Watson, the pediatric AI leverages natural language processing, in essence “reading” written notes from EHRs not unlike how a human doctor would review those same records. But the similarities to human doctors don’t end there. The AI is a machine learning classifier (MLC), capable of placing the information learned from the EHRs into categories to improve performance.
Like traditionally-trained pediatricians, the AI broke cases down into major organ groups and infection areas (upper/lower respiratory, gastrointestinal, etc.) before breaking them down even further into subcategories. It could then develop associations between various symptoms and organ groups and use those associations to improve its diagnoses. This hierarchical approach mimics the deductive reasoning human doctors employ.
Another key strength of the AI developed for this study was the enormous size of the dataset collected to teach it: 1,362,559 outpatient visits from 567,498 patients yielded some 101.6 million data points for the MLC to devour on its quest for pediatric dominance. This allowed the AI the depth of learning needed to distinguish and accurately select from the 55 different diagnosis codes across the various organ groups and subcategories.
When comparing against the human doctors, the study used 11,926 records from an unrelated group of children, giving both the MLC and the 20 humans it was compared against an even playing field. The results were clear: while cohorts of senior pediatricians performed better than the AI, junior pediatricians (those with 3-15 years of experience) were outclassed.
Helping, Not Replacing
While the research used a competitive analysis to measure the success of the AI, the results should be seen as anything but hostile to human doctors. The near future of artificial intelligence in medicine will see these machine learning programs augment, not replace, human physicians. The authors of the study specifically call out augmentation as the key short-term application of their work. Triaging incoming patients via intake forms, performing massive metastudies using EHRs, providing rapid ‘second opinions’—the applications for an AI doctor that is better-but-not-the-best are as varied as the healthcare industry itself.
That’s only considering how artificial intelligence could make a positive impact immediately upon implementation. It’s easy to see how long-term use of a diagnostic assistant could reshape the way modern medical institutions approach their work.
Look at how the MLC results fit snugly between the junior and senior physician groups. Essentially, it took nearly 15 years before a physician could consistently out-diagnose the machine. That’s a decade and a half wherein an AI diagnostic assistant would be an invaluable partner—both as a training tool and a safety measure. Likewise, on the other side of the experience curve you have physicians whose performance could be continuously leveraged to improve the AI’s effectiveness. This is a clear opportunity for a symbiotic relationship, with humans and machines each assisting the other as they mature.
Closer to Us, But Still Dependent on Us
No matter the ultimate application, the AI doctors of the future are drawing nearer to us step by step. This latest research is a demonstration that artificial intelligence can mimic the results of human deductive reasoning even in some of the most complex and important decision-making processes. True, the MLC required input from humans to function; both the initial data points and the cases used to evaluate the AI depended on EHRs written by physicians. While every effort was made to design a test schema that removed any indication of the eventual diagnosis, some “data leakage” is bound to occur.
In other words, when AIs use human-created data, they inherit human insight to some degree. Yet the progress made in machine imaging, chatbots, sensors, and other fields all suggest that this dependence on human input is more about where we are right now than where we could be in the near future.
Data, and More Data
That near future may also have some clear winners and losers. For now, those winners seem to be the institutions that can capture and apply the largest sets of data. With a rapidly digitized society gathering incredible amounts of data, China has a clear advantage. Combined with their relatively relaxed approach to privacy, they are likely to continue as one of the driving forces behind machine learning and its applications. So too will Google/Alphabet with their massive medical studies. Data is the uranium in this AI arms race, and everyone seems to be scrambling to collect more.
In a global community that seems increasingly aware of the potential problems arising from this need for and reliance on data, it’s nice to know there’ll be an upside as well. The technology behind AI medical assistants is looking more and more mature—even if we are still struggling to find exactly where, when, and how that technology should first become universal.
Yet wherever we see the next push to make AI a standard tool in a real-world medical setting, I have little doubt it will greatly improve the lives of human patients. Today Doctor AI is performing as well as a human colleague with more than 10 years of experience. By next year or so, it may take twice as long for humans to be competitive. And in a decade, the combined medical knowledge of all human history may be a tool as common as a stethoscope in your doctor’s hands.
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#434311 Understanding the Hidden Bias in ...
Facial recognition technology has progressed to point where it now interprets emotions in facial expressions. This type of analysis is increasingly used in daily life. For example, companies can use facial recognition software to help with hiring decisions. Other programs scan the faces in crowds to identify threats to public safety.
Unfortunately, this technology struggles to interpret the emotions of black faces. My new study, published last month, shows that emotional analysis technology assigns more negative emotions to black men’s faces than white men’s faces.
This isn’t the first time that facial recognition programs have been shown to be biased. Google labeled black faces as gorillas. Cameras identified Asian faces as blinking. Facial recognition programs struggled to correctly identify gender for people with darker skin.
My work contributes to a growing call to better understand the hidden bias in artificial intelligence software.
Measuring Bias
To examine the bias in the facial recognition systems that analyze people’s emotions, I used a data set of 400 NBA player photos from the 2016 to 2017 season, because players are similar in their clothing, athleticism, age and gender. Also, since these are professional portraits, the players look at the camera in the picture.
I ran the images through two well-known types of emotional recognition software. Both assigned black players more negative emotional scores on average, no matter how much they smiled.
For example, consider the official NBA pictures of Darren Collison and Gordon Hayward. Both players are smiling, and, according to the facial recognition and analysis program Face++, Darren Collison and Gordon Hayward have similar smile scores—48.7 and 48.1 out of 100, respectively.
Basketball players Darren Collision (left) and Gordon Hayward (right). basketball-reference.com
However, Face++ rates Hayward’s expression as 59.7 percent happy and 0.13 percent angry and Collison’s expression as 39.2 percent happy and 27 percent angry. Collison is viewed as nearly as angry as he is happy and far angrier than Hayward—despite the facial recognition program itself recognizing that both players are smiling.
In contrast, Microsoft’s Face API viewed both men as happy. Still, Collison is viewed as less happy than Hayward, with 98 and 93 percent happiness scores, respectively. Despite his smile, Collison is even scored with a small amount of contempt, whereas Hayward has none.
Across all the NBA pictures, the same pattern emerges. On average, Face++ rates black faces as twice as angry as white faces. Face API scores black faces as three times more contemptuous than white faces. After matching players based on their smiles, both facial analysis programs are still more likely to assign the negative emotions of anger or contempt to black faces.
Stereotyped by AI
My study shows that facial recognition programs exhibit two distinct types of bias.
First, black faces were consistently scored as angrier than white faces for every smile. Face++ showed this type of bias. Second, black faces were always scored as angrier if there was any ambiguity about their facial expression. Face API displayed this type of disparity. Even if black faces are partially smiling, my analysis showed that the systems assumed more negative emotions as compared to their white counterparts with similar expressions. The average emotional scores were much closer across races, but there were still noticeable differences for black and white faces.
This observation aligns with other research, which suggests that black professionals must amplify positive emotions to receive parity in their workplace performance evaluations. Studies show that people perceive black men as more physically threatening than white men, even when they are the same size.
Some researchers argue that facial recognition technology is more objective than humans. But my study suggests that facial recognition reflects the same biases that people have. Black men’s facial expressions are scored with emotions associated with threatening behaviors more often than white men, even when they are smiling. There is good reason to believe that the use of facial recognition could formalize preexisting stereotypes into algorithms, automatically embedding them into everyday life.
Until facial recognition assesses black and white faces similarly, black people may need to exaggerate their positive facial expressions—essentially smile more—to reduce ambiguity and potentially negative interpretations by the technology.
Although innovative, artificial intelligence can perpetrate and exacerbate existing power dynamics, leading to disparate impact across racial/ethnic groups. Some societal accountability is necessary to ensure fairness to all groups because facial recognition, like most artificial intelligence, is often invisible to the people most affected by its decisions.
Lauren Rhue, Assistant Professor of Information Systems and Analytics, Wake Forest University
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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#433301 ‘Happiness Tech’ Is On the Rise. Is ...
We often get so fixated on technological progress that we forget it’s merely one component of the entirety of human progress. Technological advancement does not necessarily correlate with increases in human mental well-being.
While cleaner energy, access to education, and higher employment rates can improve quality of life, they do not guarantee happiness and inner peace. Amid what appears to be an increasing abundance of resources and ongoing human progress, we are experiencing a mental health epidemic, with high anxiety and depression rates. This is especially true in the developed world, where we have access to luxuries our ancestors couldn’t even dream of—all the world’s information contained in a device we hold in the palm of our hands, for example.
But as you may have realized through your own experience, technology can make us feel worse instead of better. Social media can become a tool for comparison and a source of debilitating status anxiety. Increased access to goods and services, along with the rise of consumerism, can lead people to choose “stuff” over true sources of meaning and get trapped in a hedonistic treadmill of materialism. Tools like artificial intelligence and big data could lead to violation of our privacy and autonomy. The digital world can take us away from the beauty of the present moment.
Understanding Happiness
How we use technology can significantly impact our happiness. In this context, “happiness” refers to a general sense of well-being, gratitude, and inner peace. Even with such a simple definition, it is a state of mind many people will admit they lack.
Eastern philosophies have told us for thousands of years that the problem of human suffering begins with our thoughts and perceptions of the circumstances we are in, as opposed to beginning with the circumstances themselves. As Derren Brown brilliantly points out in Happy: Why More or Less Everything Is Absolutely Fine, “The problem with the modern conception of happiness is that it is seen as some kind of commodity. There is this fantasy that simply by believing in yourself and setting goals you can have anything. But that simply isn’t how life works. The ancients had a much better view of it. They offered an approach of not trying to control things you can’t control, and of lessening your desires and your expectations so you achieve a harmony between what you desire and what you have.”
A core part of feeling more happy is about re-wiring our minds to adjust our expectations, exercise gratitude, escape negative narratives, and live in the present moment.
But can technology help us do that?
Applications for Mental Well-Being
Many doers are asking themselves how they can leverage digital tools to contribute to human happiness.
Meditation and mindfulness are examples of practices we can use to escape the often overwhelming burden of our thoughts and ground our minds into the present. They have become increasingly democratized with the rise of meditation mobile apps, such as Headspace, Gaia, and Calm, that allow millions of people globally to use their phones to learn from experts at a very low cost.
These companies have also partnered with hospitals, airlines, athletic teams, and others that could benefit from increased access to mindfulness and meditation. The popularity of these apps continues to rise as more people recognize their necessity. The combination of mass technology and ancient wisdom is one that can lead to a transformation of the collective consciousness.
Sometimes merely reflecting on the sources of joy in our lives and practicing gratitude can contribute to better well-being. Apps such as Happier encourage users to reflect upon and share pleasant everyday moments in their daily lives. Such exercises are based on the understanding that being happy is a “skill” one can build though practice and through scientifically-proven activities, such as writing down a nice thought and sharing your positivity with the world. Many other tools such as Track Your Happiness and Happstr allow users to track their happiness, which often serves as a valuable source of data to researchers.
There is also a growing body of knowledge that tells us we can achieve happiness by helping others. This “helper’s high” is a result of our brains producing endorphins after having a positive impact on the lives of others. In many shapes and forms, technology has made it easier now more than ever to help other people no matter where they are located. From charitable donations to the rise of social impact organizations, there is an abundance of projects that leverage technology to positively impact individual lives. Platforms like GoVolunteer connect nonprofits with individuals from a variety of skill sets who are looking to gift their abilities to those in need. Kiva allows for fundraising loans that can change lives. These are just a handful of examples of a much wider positive paradigm shift.
The Future of Technology for Well-Being
There is no denying that increasingly powerful and immersive technology can be used to better or worsen the human condition. Today’s leaders will not only have to focus on their ability to use technology to solve a problem or generate greater revenue; they will have to ask themselves if their tech solutions are beneficial or detrimental to human well-being. They will also have to remember that more powerful technology does not always translate to happier users. It is also crucial that future generations be equipped with the values required to use increasingly powerful tools responsibly and ethically.
In the Education 2030 report, the Millennium Project envisions a world wherein portable intelligent devices combined with integrated systems for lifelong learning contribute to better well-being. In this vision, “continuous evaluation of individual learning processes designed to prevent people from growing unstable and/or becoming mentally ill, along with programs aimed at eliminating prejudice and hate, could bring about a more beautiful, loving world.”
There is exciting potential for technology to be leveraged to contribute to human happiness at a massive scale. Yet, technology shouldn’t consume every aspect of our lives, since a life worth living is often about balance. Sometimes, even if just for a few moments, what would make us feel happier is we disconnected from technology to begin with.
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#433284 Tech Can Sustainably Feed Developing ...
In the next 30 years, virtually all net population growth will occur in urban regions of developing countries. At the same time, worldwide food production will become increasingly limited by the availability of land, water, and energy. These constraints will be further worsened by climate change and the expected addition of two billion people to today’s four billion now living in urban regions. Meanwhile, current urban food ecosystems in the developing world are inefficient and critically inadequate to meet the challenges of the future.
Combined, these trends could have catastrophic economic and political consequences. A new path forward for urban food ecosystems needs to be found. But what is that path?
New technologies, coupled with new business models and supportive government policies, can create more resilient urban food ecosystems in the coming decades. These tech-enabled systems can sustainably link rural, peri-urban (areas just outside cities), and urban producers and consumers, increase overall food production, and generate opportunities for new businesses and jobs (Figure 1).
Figure 1: The urban food value chain nodes from rural, peri-urban and urban producers
to servicing end customers in urban and peri-urban markets.
Here’s a glimpse of the changes technology may bring to the systems feeding cities in the future.
A technology-linked urban food ecosystem would create unprecedented opportunities for small farms to reach wider markets and progress from subsistence farming to commercially producing niche cash crops and animal protein, such as poultry, fish, pork, and insects.
Meanwhile, new opportunities within cities will appear with the creation of vertical farms and other controlled-environment agricultural systems as well as production of plant-based and 3D printed foods and cultured meat. Uberized facilitation of production and distribution of food will reduce bottlenecks and provide new business opportunities and jobs. Off-the-shelf precision agriculture technology will increasingly be the new norm, from smallholders to larger producers.
As part of Agricultural Revolution 4.0, all this will be integrated into the larger collaborative economy—connected by digital platforms, the cloud, and the Internet of Things and powered by artificial intelligence. It will more efficiently and effectively use resources and people to connect the nexus of food, water, energy, nutrition, and human health. It will also aid in the development of a circular economy that is designed to be restorative and regenerative, minimizing waste and maximizing recycling and reuse to build economic, natural, and social capital.
In short, technology will enable transformation of urban food ecosystems, from expanded production in cities to more efficient and inclusive distribution and closer connections with rural farmers. Here’s a closer look at seven tech-driven trends that will help feed tomorrow’s cities.
1. Worldwide Connectivity: Information, Learning, and Markets
Connectivity from simple cell phone SMS communication to internet-enabled smartphones and cloud services are providing platforms for the increasingly powerful technologies enabling development of a new agricultural revolution. Internet connections currently reach more than 4 billion people, about 55% of the global population. That number will grow fast in coming years.
These information and communications technologies connect food producers to consumers with just-in-time data, enhanced good agricultural practices, mobile money and credit, telecommunications, market information and merchandising, and greater transparency and traceability of goods and services throughout the value chain. Text messages on mobile devices have become the one-stop-shop for small farmers to place orders, gain technology information for best management practices, and access market information to increase profitability.
Hershey’s CocoaLink in Ghana, for example, uses text and voice messages with cocoa industry experts and small farm producers. Digital Green is a technology-enabled communication system in Asia and Africa to bring needed agricultural and management practices to small farmers in their own language by filming and recording successful farmers in their own communities. MFarm is a mobile app that connects Kenyan farmers with urban markets via text messaging.
2. Blockchain Technology: Greater Access to Basic Financial Services and Enhanced Food Safety
Gaining access to credit and executing financial transactions have been persistent constraints for small farm producers. Blockchain promises to help the unbanked access basic financial services.
The Gates Foundation has released an open source platform, Mojaloop, to allow software developers and banks and financial service providers to build secure digital payment platforms at scale. Mojaloop software uses more secure blockchain technology to enable urban food system players in the developing world to conduct business and trade. The free software reduces complexity and cost in building payment platforms to connect small farmers with customers, merchants, banks, and mobile money providers. Such digital financial services will allow small farm producers in the developing world to conduct business without a brick-and-mortar bank.
Blockchain is also important for traceability and transparency requirements to meet food regulatory and consumer requirement during the production, post-harvest, shipping, processing and distribution to consumers. Combining blockchain with RFID technologies also will enhance food safety.
3. Uberized Services: On-Demand Equipment, Storage, and More
Uberized services can advance development of the urban food ecosystem across the spectrum, from rural to peri-urban to urban food production and distribution. Whereas Uber and Airbnb enable sharing of rides and homes, the model can be extended in the developing world to include on-demand use of expensive equipment, such as farm machinery, or storage space.
This includes uberization of planting and harvesting equipment (Hello Tractor), transportation vehicles, refrigeration facilities for temporary storage of perishable product, and “cloud kitchens” (EasyAppetite in Nigeria, FoodCourt in Rwanda, and Swiggy and Zomto in India) that produce fresh meals to be delivered to urban customers, enabling young people with motorbikes and cell phones to become entrepreneurs or contractors delivering meals to urban customers.
Another uberized service is marketing and distributing “ugly food” or imperfect produce to reduce food waste. About a third of the world’s food goes to waste, often because of appearance; this is enough to feed two billion people. Such services supply consumers with cheaper, nutritious, tasty, healthy fruits and vegetables that would normally be discarded as culls due to imperfections in shape or size.
4. Technology for Producing Plant-Based Foods in Cities
We need to change diet choices through education and marketing and by developing tasty plant-based substitutes. This is not only critical for environmental sustainability, but also offers opportunities for new businesses and services. It turns out that current agricultural production systems for “red meat” have a far greater detrimental impact on the environment than automobiles.
There have been great advances in plant-based foods, like the Impossible Burger and Beyond Meat, that can satisfy the consumer’s experience and perception of meat. Rather than giving up the experience of eating red meat, technology is enabling marketable, attractive plant-based products that can potentially drastically reduce world per capita consumption of red meat.
5. Cellular Agriculture, Lab-Grown Meat, and 3D Printed Food
Lab-grown meat, literally meat grown from cultured cells, may radically change where and how protein and food is produced, including the cities where it is consumed. There is a wide range of innovative alternatives to traditional meats that can supplement the need for livestock, farms, and butchers. The history of innovation is about getting rid of the bottleneck in the system, and with meat, the bottleneck is the animal. Finless Foods is a new company trying to replicate fish fillets, for example, while Memphis meats is working on beef and poultry.
3D printing or additive manufacturing is a “general purpose technology” used for making, plastic toys, human tissues, aircraft parts, and buildings. 3D printing can also be used to convert alternative ingredients such as proteins from algae, beet leaves, or insects into tasty and healthy products that can be produced by small, inexpensive printers in home kitchens. The food can be customized for individual health needs as well as preferences. 3D printing can also contribute to the food ecosystem by making possible on-demand replacement parts—which are badly needed in the developing world for tractors, pumps, and other equipment. Catapult Design 3D prints tractor replacement parts as well as corn shellers, cart designs, prosthetic limbs, and rolling water barrels for the Indian market.
6. Alt Farming: Vertical Farms to Produce Food in Urban Centers
Urban food ecosystem production systems will rely not only on field-grown crops, but also on production of food within cities. There are a host of new, alternative production systems using “controlled environmental agriculture.” These include low-cost, protected poly hoop houses, greenhouses, roof-top and sack/container gardens, and vertical farming in buildings using artificial lighting. Vertical farms enable year-round production of selected crops, regardless of weather—which will be increasingly important in response to climate change—and without concern for deteriorating soil conditions that affect crop quality and productivity. AeroFarms claims 390 times more productivity per square foot than normal field production.
7. Biotechnology and Nanotechnology for Sustainable Intensification of Agriculture
CRISPR is a promising gene editing technology that can be used to enhance crop productivity while avoiding societal concerns about GMOs. CRISPR can accelerate traditional breeding and selection programs for developing new climate and disease-resistant, higher-yielding, nutritious crops and animals.
Plant-derived coating materials, developed with nanotechnology, can decrease waste, extend shelf-life and transportability of fruits and vegetables, and significantly reduce post-harvest crop loss in developing countries that lack adequate refrigeration. Nanotechnology is also used in polymers to coat seeds to increase their shelf-life and increase their germination success and production for niche, high-value crops.
Putting It All Together
The next generation “urban food industry” will be part of the larger collaborative economy that is connected by digital platforms, the cloud, and the Internet of Things. A tech-enabled urban food ecosystem integrated with new business models and smart agricultural policies offers the opportunity for sustainable intensification (doing more with less) of agriculture to feed a rapidly growing global urban population—while also creating viable economic opportunities for rural and peri-urban as well as urban producers and value-chain players.
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