“AI” has rapidly gone from science fiction fantasy to trendy buzz word to business application. Let’s take a quick look at the evolution of artificial intelligence, and what the latest developments mean for health…
Last week we glanced at “machine learning,” a fascinating and eminently practical application of artificial intelligence that enables computers to detect patterns and learn new functions without being explicitly programmed. Prior to that we discussed similarly innovative opportunities for healthcare realized through “healthbots” and “bannerbots,” essentially customer service widgets capable of mimicking human interaction.
Strictly defined, “artificial intelligence” is exhibited by any device that perceives its environment and takes actions to maximize its success of achieving a goal—in the case of healthcare, analyzing the relationship between prevention or treatment techniques, and accomplishing optimal patient outcomes. In other words, an “intelligent machine” that approximates human cognition to help stakeholders throughout the patient journey.
Recently morphing from hope to hype to hero, AI for healthcare has rapidly exploded across the full spectrum of health system services, with dozens of startups in patient data and risk analytics, medical research, imaging, and diagnostics, lifestyle management and marketing, mental health, emergency room and surgery, in-patient care and hospital management, drug discovery, virtual assistants, wearables, and numerous other specialties ripe for “intelligent machines”:
Big tech players including IBM, Microsoft, Google, and Intel are also heavily vested in AI, taking advantage of exponential leaps in computing power, natural language processing and computer vision, and the availability of health-related data from devices and electronic health records. Partnering-up with institutions and industries from hospitals and universities to pharma and device manufacturers, the perfect AI storm is brewing.
Already providing healthcare stakeholders powerful analysis and expert insight on a mass scale and for relatively low cost, like all emerging technologies AI is loaded with incredible opportunities and daunting challenges. The tech’s proven ability to heighten efficiencies, reduce risks, and improve outcomes is often off-set by obstacles including monetization, policy, and sheer feasibility—but given recent successes, likely not for long.
“I’m sorry Dave, I’m afraid I can’t do that”
Google CEO Sundar Pichai has recently adopted an “AI-First” strategy, seeing utilization of the technology as ubiquitous and urgent—in contrast to Alphabet Executive Chairman Eric Schmidt’s initial impression that artificial intelligence wasn’t ready for prime time, and wouldn’t be able to scale to meet the organization’s needs. Given the perilous and frequently disappointing trajectory of AI, however, the pessimism certainly seemed warranted.
Watch John Kelly from IBM Research discuss the “three eras of computing,” outlining the leaps from tabulation to programming to cognition. The evolution moves from relatively simple machines that make mechanical computations to the age of electronic computers capable of following sophisticated instructions, and into the aspirational realm where zettabytes of data can only be processed by a machine that learns and understands.
Whereas the jump from tabulation to programming essentially demanded the electronics technology used to facilitate it, creating an “intelligent computer” has proven a far more daunting and often thoroughly confusing task. Along the way, the philosophical underpinnings of thinking itself has been called into question, requiring a rigorous definition and emulation of cognition, sentience, and what it ultimately means to be human.
Arguably the most memorable vision of such a “sentient machine” is the HAL 9000 computer from Stanley Kubrick’s adaptation of 2001: A Space Odyssey: Calm and logical until threatened when survival instincts trigger human emotions that confirm our worst fears, HAL seemed the culmination of both tech splendor and irresponsible hubris. Fast forward two decades beyond 2001, and AI has become far less threatening and much simpler.
From the enchanted optimism of the 50 and 60s to the reality checks of the 70s and 80s, AI research actually only started making significant progress in the 90s with the advent of “intelligent agents”—a practical generalization of “intelligence” beyond human cognition, and into essential programs that solve specific, verifiable, and useful problems. The singular focus led to successful applications in data, robotics, speech recognition—and healthcare.
One recent example is Healthcare NExT from Microsoft, a cross-disciplinary industry partnership that uses natural language processing to help reduce data entry, a “triagebot” to interactively process patients, and HealthVault integration with connected devices to optimize outpatient care. The strategy focuses on making AI central to and continuous throughout the patient experience, rather than a bright and shiny add-on tactic.
Your Creative Technologists
Is your brand adapting to the constantly changing market place, and taking full advantage of the latest in emerging tech to remain competitive? Here at Klick Health we’re actively experimenting with and applying machine learning and artificial intelligence to healthcare. From revealing hidden audience segments to working on healthbot prototypes, we’re a unique kind of commercialization partner, eager to provide you and your brands state of the art intelligence.