The sheer volume of available medical knowledge has long since outstripped even the most capable clinician's ability to review and properly analyze the all the data now being generated and collected about a patient's health. Today it requires the assistance of sophisticated computer systems to not only help them with the analysis of the data, but to also stay on top of breakthroughs in genomics, predictive analytics, clinical decision support, population health management, and the continuous changes in best practices. Read How Healthcare Can Prep for Artificial Intelligence.
Even the best, most attentive health care providers simply can’t stay on top of all the data and the growing body of ever-changing medical knowledge. Doctors just can't be there for a patient around the clock, to catch every bad habit or reinforce every positive behavior. However, Artificial intelligence (AI) has evolved to the point that it can now begin to significantly augment the healthcare provided by physicians, pharmacists, nurses, and health coaches – not to mention self care.
Today, over 90 Artificial Intelligence (AI) startup comanies are already active in developing new solutions in the areas of Imaging & Diagnostics, Drug Discovery, Remote Patient Monitoring, Hospital Management, Customer Service, Healthcare Analytics & Research, Patient Alerts & Reminders, Telemedicine, Public Health, and more. See Artificial Intelligence Startups in Healthcare by CB Insights.
Current Examples of AI in Healthcare
Aside from the big players like IBM and Google, some of the other major startup companies focused on AI in healthcare include: Ayasdi, Babylon Health, Digital Reasoning, Gauss Surgical, H2O AI, iCarbonX, Lumiata, Pathway Genomics, Stratified Medical, Welltok and Zephyr Health – to name just a few.
The following are specific examples of major AI systems and project activities in healthcare that you might want to explore further:
- Google Deepmind Health project is being used to mine the data of electronic medical records (EMR) in order to provide better and faster health services.
- Medical Sieve, an ambitious long-term exploratory project by IBM is being used to build a next generation “cognitive assistant” with analytical, reasoning capabilities and a wide range of clinical knowledge to assist in clinical decision making in radiology and cardiology.
- IBM has also launched Watson for Oncology that is able to provide clinicians with better evidence-based treatment options.
- Babylon Health has launched an app this year which offers medical AI consultation based on personal medical history and common medical knowledge.
- Medical start-up SenseLy uses AI and machine learning to support patients with chronic conditions in-between doctor’s visits using Molly, the world's first virtual nurse.
- Deep Genomics aims at identifying patterns in huge data sets of genetic information and medical records, looking for mutations and linkages to disease.
- Human Longevity offers patients complete genome sequencing coupled with full body scan and very detailed medical check-up to help spot cancer or vascular diseases in their very early stage.
- Baidu has introduced an artificial intelligence-powered chatbot called Melody to connect with patients, field medical questions, and suggest diagnoses to doctors. It is a new feature of the Baidu Doctor app it launched last year.
- Mount Sinai Health System has tapped CloudMedx to help pinpoint people at risk of congestive heart failure as part of its emerging program dubbed HealthPromise.
- The Computerized Patient Record System (CPRS) developed and implemented by the Veterans Health Administration (VHA) contains a number of modules that use first generation AI capabilities, e.g. Clinical Alerts, Clinical Reminders.
- Open AI is a non-profit AI research company that aims to collaboratively develop and promote carefully an open source AI platform and software to benefit humanity as a whole.
- VA Collaborating with Flow Health to Bring AI and Precision Medicine to Veterans. Flow Health has formed a five-year partnership with the US Department of Veterans Affairs (VA) to build a medical knowledge graph with deep learning to inform medical decision-making and train artificial intelligence (AI) to personalize care plans.
AI Market & Financial Investment
According to a recent 2016 report by Frost & Sullivan, use of AI is growing in healthcare, with the market poised to reach $6 billion by 2021, up from $600 million in 2014. Cognitive solutions such as IBM’s Watson system are capable of sifting through huge volumes of data and providing guidance and decision support to healthcare providers, thereby improving work flow and patient care.
IBM is serious about bringing Watson into the healthcare industry. To build Watson’s medical credibility, IBM has spent $4 billion dollars purchasing companies that had huge stores of medical data, from billing records to MRI images. Read IBM's Watson Recommends Same Treatment as Doctors in 99% of Cancer Cases.
Artificial Intelligence (AI) coupled with the Internet of Things (IoT) could be the answer to solve major healthcare challenges that the world is facing today. IoT can helping care to move from hospital to home in low acuity and post-operative scenarios, with wearable sensors being monitored and analyzed continuously in real time by AI systems. The number of connected IoT healthcare devices is estimated to be $2.5 trillion by 2025. For more detail, read IoT and AI: Potent combo redefining healthcare.
Artificial Intelligence (AI) technology already exists, and is being deployed in many other arenas, such as finance and business intelligence. But in health care, there’s a human and psychological barrier: can we trust AI with our health?
Some of the key issues that need to be addressed over the coming decade before Artificial Intelligence (AI) systems are widely deployed include:
Ethics - This emerging issue is concerned with the moral behavior being programmed by humans into robots and other 'smart' AI systems, e.g. Roboethics.
Privacy & Security – When AI systems are turned loose to monitor health information systems gathering data on all facets of your personal health, concerns about who has access to the data and who it is being shared with are just a few of the issues that must be adequately addressed upfront.
Jobs - The Bank of England has predicted that intelligent machines might take over 80 million American and 15 million British jobs, respectively over the next 10 to 20 years. The healthcare industry will not be immune to this change.
Legal Issues - One of the most important points of interest that needs to be hammered out first is the legality of these machines. When a doctor's gross negligence leads to a misdiagnoses and patient harm, the fault is placed squarely on the shoulders of the offending physician. What happens if an AI system were to misdiagnose a patient? Who's to blame?
- Open Source - Many new 'open source' tools are arriving that can now run on affordable hardware and allow individuals and small organizations to perform prodigious data crunching and predictive tasks. Read about H2O, OpenAI, and other machine learning and AI tools being used in healthcare at Open Health News.
Conclusions & Next Steps
As we continue to move forward with further development and deployment of AI systems, the following are some of the next steps that need to be taken:
Ethical rules and standards to be incorporated in AI systems used in the healthcare need to be extensively addressed.
The health IT industry needs to take careful, incremental steps when developing and implementing AI systems in healthcare over the next decade to avoid causing any harm.
Medical professionals need to become much more knowledgable about AI technology and systems and work closely with developers before we move forward much further.
Patients need to get accustomed to interacting with AI systems, discovering the potential benefits to themselves – to trust these new systems.
Companies developing AI healthcare solutions need to carefully communicate with the general public about the potential advantages and risks associated with the use of AI in medicine.
Healthcare oversight institutions need to carefully measure the success, shortcomings, and effectiveness of AI systems before they are widely deployed over the coming decades.
The next few decades are going to be an exciting time in healthcare. You might want to take a few minutes to read Searching for Godot – Health 4.0 by 2040.