Monthly Archives: September 2017

How 3-D Printing Is Changing Health Care

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Recent advances have made the technology more useful for planning surgery and creating drugs

The Mayo Clinic printed a model of a patient’s pelvis to plan surgery to remove a rare tumor that had spread to the base of the spine.
The Mayo Clinic printed a model of a patient’s pelvis to plan surgery to remove a rare tumor that had spread to the base of the spine. PHOTO: MAYO CLINIC

A year ago, an 11-year-old girl named London Secor had surgery at the Mayo Clinic to remove a rare tumor located in her pelvis. In the past, surgeons would have considered amputating one of Ms. Secor’s legs, given that the tumor had spread to the bone and nerves of her sacrum and was encroaching on her hip socket.

That didn’t happen this time, however, due largely to advances in 3-D printing.

Before the surgery, Mayo printed a 3-D model of the girl’s pelvis, scaled to size and showing her bladder, veins, blood vessels, ureters and the tumor. Members of the medical team were able to hold the model in their hands, examine it and plot a surgical approach that would allow them to remove the entire tumor without taking her leg.

“There is nothing like holding a 3-D model to understand a complicated anatomical procedure,” says Peter Rose, the surgeon who performed the operation on Ms. Secor, an avid swimmer and basketball player from Charlotte, N.C. “The model helped us understand the anatomy that was altered by the tumor and helped us orient ourselves for our cuts around it.”

The pelvis model was one of about 500 3-D-printed objects created at the Mayo Clinic last year. It’s part of a web of organizations racing to find ways to use 3-D printing to improve health care.

Some research institutions, including the Mayo Clinic, have set up on-site printing labs in partnership with such makers of 3-D printers as Stratasys , 3D Systems and Formlabs. General Electric Co. and Johnson & Johnson are diving in, too, with GE focused on 3-D printers and translating images from various sources into 3-D objects, and J&J focused on developing a range of materials that can be used as “ink” to print customized objects.

Using data from MRIs, CT scans and ultrasounds, as well as three-dimensional pictures, 3-D printers create objects, layer by layer, using materials ranging from plastics to metal to human tissue. Beyond organ models, the printers are being used in health care to create dental and medical implants, hearing aids, prosthetics, drugs and even human skin.

Research firm Gartner predicts that by 2019, 10% of people in the developed world will be living with 3-D-printed items on or in their bodies, and 3-D printing will be a central tool in more than one-third of surgical procedures involving prosthetics and implanted devices. According to research firm IndustryARC, the overall market for medical 3-D printing is expected to grow to $1.21 billion by 2020 from about $660 million in 2016.

Though the industry is young, Anurag Gupta, a Gartner vice president of research, says 3-D printing in health care “could have the transformative impact of the internet or cloud computing a few years ago.”

The technology of 3-D printing has been around since the 1980s, but recent advances in software and hardware have made it faster, more cost-efficient and of higher quality. Five years ago, the 3-D printers made by Stratasys could print in one or two materials and one or two colors. Now they can print six materials simultaneously and create more than 360,000 combinations of textures and colors to better mimic materials ranging from soft tissue to bone, paving the way for wider adoption.

The rise of customized medicine, in which care and medicine is tailored to individual patients, also has helped fuel growth of 3-D printing in health care, as more patients and doctors seek out customized medical devices, surgical tools and drugs.

One of the areas in which the technology may hold particular promise, experts say, is in the manufacturing of drugs in the dose and shape best suited to certain groups of patients. Aprecia Pharmaceuticals recently launched a 3-D printed epilepsy drug called Spritam, a high-dosage pill that dissolves quickly with a small amount of water and in a shape that is easy to swallow.

Printing whole organs, such as livers and kidneys, remains the Holy Grail, but that is more than a decade away, says Gartner’s Mr. Gupta. Printing smaller pieces of human material, however, has already begun.

Researchers at the University Carlos III of Madrid, along with the Spanish biotech company BioDan, have printed human skin to eventually help burn victims and others suffering from skin injuries and diseases. The process involves a 3-D printer that deposits bioinks containing cells from an individual as well as other biological molecules to create a patch of skin. Like the real thing, this printed skin consists of an external layer, the epidermis, and the thicker, deeper layer, the dermis.

Organovo Holdings Inc. of San Diego prints pieces of liver and kidney tissue to test new therapies and the toxicology of early-stage drugs. Johnson & Johnson is working with Aspect Biosystems Ltd. to develop bioprinted knee meniscus tissue. And 3D Systems is developing 3-D-printed lung tissue with United Therapeutics Corp.

While entry-level 3-D printers used by hobbyists can cost a few hundred dollars, industrial 3-D printers used by hospitals can range from $10,000 to $400,000 for those that print plastics and polymers.

Another hurdle for hospitals is the “hidden cost” of operating 3-D printers, says Jimmie Beacham who leads GE Healthcare’s 3-D printing strategy. Engineers are required to transform dense digital images from MRI, CT and ultrasound scans into information that can be printed into a 3-D model. What’s more, printing a 3-D object doesn’t yet happen with the click of a button. It took 60 hours for Mayo Clinic to print Ms. Secor’s pelvis and tumor, for example.

Still, 3-D printing can lead to cost savings in other areas, say experts such as Jonathan Morris, a Mayo radiologist. Allowing surgeons to practice on 3-D models of a specific patient’s organs before surgery can significantly reduce time in the operating room. Printing implants and prosthetics on demand and on location means fewer middlemen in the supply chain and less waste. And given the better fit of customized implants from 3-D printers, patients may not have to replace them as often.

The Mayo Clinic and a half dozen other cutting-edge research hospitals have blazed the path in terms of creating 3-D printing labs on site. Now some larger city network hospitals are beginning to purchase their own 3-D printers, while smaller hospitals and doctors can order 3-D models for complicated surgeries on a case-by-case basis from 3-D printing companies.

A Survey of 3,000 Executives Reveals How Businesses Succeed with AI

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AUGUST 28, 2017

The buzz over artificial intelligence (AI) has grown loud enough to penetrate the C-suites of organizations around the world, and for good reason. Investment in AI is growing and is increasingly coming from organizations outside the tech space. And AI success stories are becoming more numerous and diverse, from Amazon reaping operational efficiencies using its AI-powered Kiva warehouse robots, to GE keeping its industrial equipment running by leveraging AI for predictive maintenance.

While it’s clear that CEOs need to consider AI’s business implications, the technology’s nascence in business settings makes it less clear how to profitably employ it. Through a study of AI that included a survey of 3,073 executives and 160 case studies across 14 sectors and 10 countries, and through a separate digital research program, we have identified 10 key insights CEOs need to know to embark on a successful AI journey.

Don’t believe the hype: Not every business is using AI… yet. While investment in AI is heating up, corporate adoption of AI technologies is still lagging. Total investment (internal and external) in AI reached somewhere in the range of $26 billion to $39 billion in 2016, with external investment tripling since 2013. Despite this level of investment, however, AI adoption is in its infancy, with just 20% of our survey respondents using one or more AI technologies at scale or in a core part of their business, and only half of those using three or more. (Our results are weighted to reflect the relative economic importance of firms of different sizes. We include five categories of AI technology systems: robotics and autonomous vehicles, computer vision, language, virtual agents, and machine learning.)

For the moment, this is good news for those companies still experimenting or piloting AI (41%). Our results suggest there’s still time to climb the learning curve and compete using AI.

However, we are likely at a key inflection point of AI adoption. AI technologies like neural-based machine learning and natural language processing are beginning to mature and prove their value, quickly becoming centerpieces of AI technology suites among adopters. And we expect at least a portion of current AI piloters to fully integrate AI in the near term. Finally, adoption appears poised to spread, albeit at different rates, across sectors and domains. Telecom and financial services are poised to lead the way, with respondents in these sectors planning to increase their AI tech spend by more than 15% a year — seven percentage points higher than the cross-industry average — in the next three years.

Believe the hype that AI can potentially boost your top and bottom line. Thirty percent of early AI adopters in our survey — those using AI at scale or in core processes — say they’ve achieved revenue increases, leveraging AI in efforts to gain market share or expand their products and services. Furthermore, early AI adopters are 3.5 times more likely than others to say they expect to grow their profit margin by up to five points more than industry peers. While the question of correlation versus causation can be legitimately raised, a separate analysis uncovered some evidence that AI is already directly improving profits, with ROI on AI investment in the same range as associated digital technologies such as big data and advanced analytics.

Without support from leadership, your AI transformation might not succeed. Successful AI adopters have strong executive leadership support for the new technology. Survey respondents from firms that have successfully deployed an AI technology at scale tend to rate C-suite support as being nearly twice as high as those companies that have not adopted any AI technology. They add that strong support comes not only from the CEO and IT executives but also from all other C-level officers and the board of directors.

You don’t have to go it alone on AI — partner for capability and capacity. With the AI field recently picking up its pace of innovation after the decades-long “AI winter,” technical expertise and capabilities are in short supply. Even large digital natives such as Amazon and Google have turned to companies and talent outside their confines to beef up their AI skills. Consider, for example, Google’s acquisition of DeepMind, which is using its machine learning chops to help the tech giant improve even core businesses like search optimization. Our survey, in fact, showed that early AI adopters have primarily bought the right fit-for-purpose technology solutions, with only a minority of respondents both developing and implementing all AI solutions in-house.

Resist the temptation to put technology teams solely in charge of AI initiatives. Compartmentalizing accountability for AI with functional leaders in IT, digital, or innovation can result in a hammer-in-search-of-a-nail outcome: technologies being launched without compelling use cases. To ensure a focus on the most valuable use cases, AI initiatives should be assessed and co-led by both business and technical leaders, an approach that has proved successful in the adoption of other digital technologies.

Take a portfolio approach to accelerate your AI journey. AI tools today vary along a spectrum ranging from tools that have been proven to solve business problems (for example, pattern detection for predictive maintenance) to those with low awareness and currently-limited-but-high-potential utility (for example, application of AI to developing competitive strategy). This distribution suggests that organizations could consider a portfolio-based approach to AI adoption across three time horizons:

Short-term: Focus on use cases where there are proven technology solutions today, and scale them across the organization to drive meaningful bottom-line value.

Medium-term: Experiment with technology that’s emerging but still relatively immature (deep learning video recognition) to prove their value in key business use cases before scaling.

Long-term: Work with academia or a third party to solve a high-impact use case (augmented human decision making in a key knowledge worker role, for example) with bleeding-edge AI technology to potentially capture a sizable first-mover advantage.

Machine learning is a powerful tool, but it’s not right for everything. Machine learning and its most prominent subfield, deep learning, have attracted a lot of media attention and received a significant share of the financing that has been pouring into the AI universe, garnering nearly 60% of all investments from outside the industry in 2016.

But while machine learning has many applications, it is just one of many AI-related technologies capable of solving business problems. There’s no one-size-fits-all AI solution. For example, the AI techniques implemented to improve customer call center performance could be very different from the technology used to identify credit card payments fraud. It’s critical to look for the right tool to solve each value-creating business problem at a particular stage in an organization’s digital and AI journey.

Digital capabilities come before AI. We found that industries leading in AI adoption — such as high-tech, telecom, and automotive — are also the ones that are the most digitized. Likewise, within any industry the companies that are early adopters of AI have already invested in digital capabilities, including cloud infrastructure and big data. In fact, it appears that companies can’t easily leapfrog to AI without digital transformation experience. Using a battery of statistics, we found that the odds of generating profit from using AI are 50% higher for companies that have strong experience in digitization.

Be bold. In a separate study on digital disruption, we found that adopting an offensive digital strategy was the most important factor in enabling incumbent companies to reverse the curse of digital disruption. An organization with an offensive strategy radically adapts its portfolio of businesses, developing new business models to build a growth path that is more robust than before digitization. So far, the same seems to hold true for AI: Early AI adopters with a very proactive, strictly offensive strategy report a much better profit outlook than those without one.

The biggest challenges are people and processes. In many cases, the change-management challenges of incorporating AI into employee processes and decision making far outweigh technical AI implementation challenges. As leaders determine the tasks machines should handle, versus those that humans perform, both new and traditional, it will be critical to implement programs that allow for constant reskilling of the workforce. And as AI continues to converge with advanced visualization, collaboration, and design thinking, businesses will need to shift from a primary focus on process efficiency to a focus on decision management effectiveness, which will further require leaders to create a culture of continuous improvement and learning.

Make no mistake: The next digital frontier is here, and it’s AI. While some firms are still reeling from previous digital disruptions, a new one is taking shape. But it’s early days. There’s still time to make AI a competitive advantage.


Jacques Bughin is a director of the McKinsey Global Institute based in Brussels.

Brian McCarthy is a partner in McKinsey’s Atlanta office.

Michael Chui is a McKinsey Global Institute partner based in San Francisco, and leads MGI’s work on the impact of technological change.