Unlike legacy technologies that are only algorithms and tools that complement a human, health AI today can truly augment human activity taking over tasks that range from medical imaging to risk analysis to diagnosing health conditions.
Having originated as a concept as early as the 1950s, artificial intelligence (AI) research and application has come a long way during the 1980s–2000s and up to the current day. The technology is now beginning to mature and proliferate across a much wider cross-section of the economy. The ultimate frontier for AI systems continues to be achieving a level of sophistication that matches that of the human mind.
Researchers and commercial entities are harnessing ways in which AI solutions can make use of the massive data footprint being generated in the process of daily activities through smart technologies such as EHRs (electronic health records), wearable devices, mobiles, health and fitness apps, etc. Hardware and software are becoming ever-increasingly more powerful, less expensive, and easier to access. This enables the processing of large data sets quickly and cost effectively. The amount of data produced doubles every year. As much data was produced in 2016 as was produced from the beginning of time through 2015. The more the information available for processing, the more the AI system can learn, and the more accurate it becomes. AI is beginning to mature to the point where it can learn without human interaction.
It is estimated that over the years AI will penetrate every sphere of the medical space, enabling a complete patient-centric model. Through the application of machine learning, data mining, natural language processing (NLP), and advanced analytics, AI will assist doctors in diagnosing diseases faster. One of the most efficient use-cases of AI is optimization of clinical process through intelligent matching. This means that when a person feels sick, AI technology can check vital signs, analyze medical history, and provide prescriptions. This will effectively free up doctors' time and help them focus on more critical cases. AI can aid in the search to find a cure for chronic diseases like cancer where research organizations traditionally spend billions of dollars each year. AI technology can bring down the drug discovery cost by analyzing huge data points in a fraction of the time as compared to humans. Machine learning can also help find critical indicators and patterns in medical imagery, which the human eye cannot effectively identify. The insights gathered can also determine if a patient is at risk of developing the disease in the future. This can help doctors prescribe proactive treatment to their patients.
In the United States, experts at the University of North Carolina School of Medicine tested IBM Watson with a sample size of 1000 cancer cases. Surprisingly, in 99 percent of the cases, the platform gave the same recommendations as professional oncologists.
Stanford University researchers have developed an AI algorithm that can identify skin cancer. According to the results, the efficiency of this algorithm in diagnosing skin cancer rivals that of professional oncologists.
Microsoft has collaborated with Oregon Health & Science University to provide cancer drug treatment options for the patients. Microsoft is also working on several other healthcare projects including the image analysis of tumor progression and the development of programmable cells.
AiCure is a US-based company that uses AI on mobile devices to control patient adherence to prescriptions and confirm medical ingestion in clinical trials and high-risk populations. It determines if a user is taking the prescribed drug at the right time and is performing other tasks prescribed by the doctor. This can be useful for people with serious medical conditions and patients who might go against or unable to follow their doctor's prescriptions.
Molly is an AI nurse developed by the medical start-up Sense.ly with the exclusive goal to help people with monitoring their condition and treatment. It uses machine learning to support patients with chronic conditions in between doctor's visits. This AI nurse also provides proven, customized monitoring, and follow-up care, with a strong focus on chronic diseases.
Intel has recently invested in a start-up called Lumiata which helps in identifying at-risk patients and developing solutions for them using AI.
GE Healthcare provides a wide range of services including medical imaging, medical diagnostics, and patient monitoring system using AI. GE Healthcare collaborated with Partners HealthCare to integrate AI throughout every part of the patient experience. GE Healthcare also collaborated with Harvard hospitals to detect abnormalities in scans using AI that would benefit the radiologists.
Pharma companies too are also applying AI and machine learning to drug development and discovery. The Boston-based company Berg combines AI and big data to chalk out new drug compounds that can be even more beneficial.
Impact on Indian Healthcare Industry
India, a leading pharmaceutical producer in the world, still lags behind in the public health sector. Estimates show that India has a shortfall of Rs. 5 lakh doctors based on the World Health Organization (WHO) norm of 1:1000 population. Doctors are stretched thin – especially in rural areas – to respond to the growing needs of the population. Lack of motivation to work in rural areas due to the absence of basic infrastructure and substandard maintenance of primary health centers (PHCs) are looming concerns that threaten public health in India besides many other factors. That could soon be a thing of the past, if AI has its way in the Indian healthcare sector.
With AI making strides and becoming accessible to people using smartphones apps, it provides the opportunity to provide inexpensive screening to anyone with a smartphone. The applications of AI can prove crucial to hospitals in rural areas and medical centers that are lacking in human expertise and technical resources.
AI implementations in Indian healthcare, albeit in a nascent stage, have started picking up pace. In India, IBM has partnered with Manipal Hospitals to provide diagnosis and treatment to cancer patients. Watson for Oncology is used across 16 facilities and academic centers of the hospital where more than 20,000 patients are treated each year. With India facing an acute shortage of oncology specialists, this partnership will enable faster and better care for patients.
Google is poised to begin a grand experiment in using machine learning to widen access to healthcare. If it is successful, it could see the company help protect millions of people with diabetes from an eye disease that leads to blindness. Last year researchers at the search and ads company announced that they had trained image recognition algorithms to detect signs of diabetes-related eye disease roughly as well as human experts. The software examines photos of a patient's retina to spot tiny aneurisms indicating the early stages of a condition called diabetic retinopathy, which causes blindness if untreated. In India, Google is working with the Aravind Eye Care System, a network of eye hospitals established in the late 1970s and credited with helping reduce the incidence of blindness caused by cataracts in the country. Aravind helped Google develop its retinal screening system by contributing some of the images needed to train its image parsing algorithms. The system uses the same deep learning technique that allows Google's image search and photo storage service to do things like differentiate between dogs, cats, and people. One of the promises of this technology is being able to make healthcare more accessible.
There are more than 400 million people worldwide with diabetes, including
70 million in India.
Bangalore-based startup, SigTuple has created a unique AI-based engine that can analyze blood slides and generate an entire pathology report without requiring a pathologist. This solution could be provided to people in remote areas and at a fraction of the actual cost. Further, computer vision can produce more and more accurate data in a nonlinear manner, thanks to machine learning.
Major vendors may face some stiff competition from up-and-coming companies receiving millions in venture capital investments. Deals to healthcare-related AI companies have been increasing year-or-year. New startups are clearly venturing into this space. According to a 2016 report from CB Insights, healthcare AI startups are beating out companies in every other industry in terms of the volume of completed deals. Though the current funding landscape is nascent, few companies such as SigTuple, Niramai, QorQI, Qure.ai, Practo, and Predible Health are making significant inroads in the country. These companies promise everything from cancer detection to care and the process of finding a new doctor. For instance, the latest announcement by Practo to spend Rs. 369 crore in AI defines the level of participation and its huge betting on AI in healthcare market. SigTuple, which uses computer vision and AI for diagnosis, announced series A funding of Rs. 39 crore. It will help SigTuple scale up and go global. SigTuple has tied up with partner hospitals and diagnostic labs for a supply of visual data to feed its AI engine. It has also been running pilot programs in 17 medical institutions to validate its AI product.
With automation threatening the manufacturing sector, India has to put on its thinking cap to check the rise of robots in manufacturing. If the country wants to participate in the AI revolution, then it needs a policy that brings together Indian academicians, researchers, labs, private players, and investors on the same platform. And if it wants to counter the rise of China and its neighbors in AI, it needs to significantly ramp up its infrastructure as well. There is a lot of optimism around some of the landmark initiatives in motion, such as Make in India, Skill India, and Digital India.
The global market for AI in healthcare is expected to grow from USD 667.1 million in 2016 to USD 7988.8 million by 2022, reflecting a CAGR of 52.68 percent, predicts MarketsandMarkets. The growing usage of big data in the healthcare industry, ability of AI to improve patient outcomes, imbalance between health workforce and patients, reducing the healthcare costs, growing importance on precision medicine, cross-industry partnerships, and significant increase in venture capital investments are expected to drive the AI in the healthcare market.
The deep learning technology which includes image recognition, signal recognition, and data mining – is expected to witness the highest CAGR. The government mandates for using EHR, the presence of major companies such as IBM Corporation, Google Inc., and Microsoft Corporation, and the engagement in deep learning technology are expected to propel the AI in healthcare market.
North America dominates the overall market. The United States is a major contributor to the growth of the AI in the healthcare market in North America. Factors such as EMR, increasing focus on precision medicine, strong presence of leading companies engaged in developing AI solutions for healthcare, large number of cross-industry collaborations, growing investments in the field of healthcare AI, and high consumerization of personal care products are driving the growth of AI in healthcare market in this region. Asia-Pacific possesses high growth potential, owing to increase in accessibility and availability of AI systems, rise in healthcare and research expenditure, high economic development, and growing developments and innovations due to the presence of the leading companies.
AI Thinks and Pays for Itself
AI represents a significant opportunity for industry players to manage their bottom line in a new payment landscape, while capitalizing on new growth potential. To better understand the savings potential of AI, Accenture analyzed a comprehensive taxonomy of 10 AI applications with the greatest near-term impact in healthcare. The assessment defined the impact of each application, likelihood of adoption, and value to the health economy. The top three applications that represent the greatest near-term value are robot-assisted surgery
(USD 40 billion), virtual nursing assistants (USD 20 billion), and administrative workflow assistance (USD 18 billion). As these, and other AI applications gain more experience in the field, their ability to learn and act will continually lead to improvements in precision, efficiency, and outcomes.
Robot-assisted surgery leads the AI pack in terms of value potential. Cognitive robotics can integrate information from pre-op medical records with real-time operating metrics to physically guide and enhance the physician's instrument precision. The technology incorporates data from actual surgical experiences to inform new, improved techniques and insights. Such improvements enhance overall outcomes and consumer trust for AI applicability across surgical areas of practice. Robotics outcomes include a 21 percent reduction in length of stay. The value will only increase with the development of robotic solutions for a greater diversity of surgeries.
Virtual nursing assistants are another frontrunner of AI value. When AI solutions remotely assess a patient's symptoms and deliver alerts to clinicians only when patient care is needed, it reduces unnecessary hospital visits. It can also lessen the burden on medical professionals. In the case of nurses, AI can save 20 percent of RN time through avoided unnecessary visits.
As virtual nursing assistants become accustomed to patient diagnoses and conditions, their abilities will grow beyond effective triage into expertise and recommendations around patient treatment. Timesaving administrative workflow assistant capabilities – such as voice-to-text transcription – eliminate nonpatient care activities including writing chart notes, prescriptions, and ordering tests. This equates to a work time savings of 17 percent for doctors, and 51 percent for registered nurses.
Of all the sectors in India, AI is poised to disrupt healthcare the most, in the coming years. The Indian healthcare industry stands to gain immensely from AI. Organizations that will leverage AI will most likely find themselves with a competitive advantage relative to those who fail to understand and leverage the technology. Those that fall behind may find it difficult to close the competitive gap. AI technology is not about replacing doctors but enhancing their efforts to improve the overall quality and availability of health services. The potential gains and opportunities from AI are exciting, and new progress is seen every day. AI and machine learning in healthcare will fundamentally disrupt the healthcare industry which means that all stakeholders should be prepared to embrace and adapt to the change. The bottom-line – the harmonious collaboration of man and machine will bring about a meaningful and long-lasting change in Indian healthcare leading to enhanced and precise treatment of medical cases, be it in urban or rural areas.