Artificial intelligence in healthcare
Artificial intelligence (AI) in healthcare is the use of algorithms and software to approximate human cognition in the analysis of complex medical data. Specifically, AI is the ability for computer algorithms to approximate conclusions without direct human input.
What distinguishes AI technology from traditional technologies in health care is the ability to gain information, process it and give a well-defined output to the end-user. AI does this through machine learning algorithms, which can recognize patterns in behavior and create its own logic. In order to reduce the margin of error, AI algorithms need to be tested repeatedly. AI algorithms behave differently from humans in two ways: (1) algorithms are literal: if you set a goal, the algorithm can’t adjust itself and only understand what is has been told explicitly, (2) and algorithms are black boxes; algorithms can predict extremely precise, but not the cause or the why. 
The primary aim of health-related AI applications is to analyze relationships between prevention or treatment techniques and patient outcomes. AI programs have been developed and applied to practices such as diagnosis processes, treatment protocol development, drug development, personalized medicine, and patient monitoring and care. Medical institutions such as The Mayo Clinic, Memorial Sloan Kettering Cancer Center, Massachusetts General Hospital, and National Health Service, have developed AI algorithms for their departments. Large technology companies such as IBM and Google, and startups such as Welltok and Ayasdi, have also developed AI algorithms for healthcare. Additionally, hospitals are looking to AI solutions to support operational initiatives that increase cost saving, improve patient satisfaction, and satisfy their staffing and workforce needs. Companies like Hospital IQ are developing predictive analytics solutions that help healthcare leaders improve business operations through increasing utilization, decreasing patient boarding, reducing length of stay and optimizing staffing levels.
Research in the 1960s and 1970s produced the first problem-solving program, or expert system, known as Dendral. While it was designed for applications in organic chemistry, it provided the basis for a subsequent system MYCIN, considered one of the most significant early uses of artificial intelligence in medicine. MYCIN and other systems such as INTERNIST-1 and CASNET did not achieve routine use by practitioners, however.
The 1980s and 1990s brought the proliferation of the microcomputer and new levels of network connectivity. During this time, there was a recognition by researchers and developers that AI systems in healthcare must be designed to accommodate the absence of perfect data and build on the expertise of physicians. Approaches involving fuzzy set theory, Bayesian networks, and artificial neural networks, have been applied to intelligent computing systems in healthcare.
Medical and technological advancements occurring over this half-century period that have enabled the growth healthcare-related applications of AI include:
- Improvements in computing power resulting in faster data collection and data processing
- Increased volume and availability of health-related data from personal and healthcare-related devices
- Growth of genomic sequencing databases
- Widespread implementation of electronic health record systems
- Improvements in natural language processing and computer vision, enabling machines to replicate human perceptual processes
- Enhanced the precision of robot-assisted surgery
Various specialties in medicine have shown an increase in research regarding AI.
The specialty that has gained the greatest attention is the field of Radiology. An ability to interpret imaging results may aid clinicians in detecting a minute change in an image that a clinician might accidentally miss. A study at Stanford created an algorithm that can detect pneumonia better than radiologists can. The radiology conference Radiological Society of North America has implemented a large part of its schedule to the use of AI in imaging. The emergence of AI technology in radiology is perceived as a threat by some specialists, as the technology can perform certain tasks better than human specialists, changing the role radiologists have currently. 
The increase of Telemedicine, has shown the rise of possible AI applications. The ability to monitor patients using AI may allow for the communication of information to physicians if possible disease activity may have occurred. A wearable device may allow for constant monitoring of a patient and also allow for the ability to notice changes that may be less distinguishable by humans.
The subsequent motive of large based health companies merging with other health companies, allow for greater health data accessibility. Greater health data may allow for more implementation of AI algorithms.
A large part of industry focus of implementation of AI in the healthcare sector is in the clinical decision support systems.  As the amount of data increases, AI decision support systems become more efficient. Numerous companies are exploring the possibilities of the incorporation of big data in the health care industry.
The following are examples of large companies that have contributed to AI algorithms for use in healthcare.
IBM's Watson Oncology is in development at Memorial Sloan Kettering Cancer Center and Cleveland Clinic. IBM is also working with CVS Health on AI applications in chronic disease treatment and with Johnson & Johnson on analysis of scientific papers to find new connections for drug development.
Microsoft's Hanover project, in partnership with Oregon Health & Science University's Knight Cancer Institute, analyzes medical research to predict the most effective cancer drug treatment options for patients. Other projects include medical image analysis of tumor progression and the development of programmable cells.
Google's DeepMind platform is being used by the UK National Health Service to detect certain health risks through data collected via a mobile app. A second project with the NHS involves analysis of medical images collected from NHS patients to develop computer vision algorithms to detect cancerous tissues.
IDx's first solution, IDx-DR founded by Michael Abramoff, is the first and only FDA authorized AI system for the autonomous detection of diabetic retinopathy. As an autonomous, AI-based system, IDx-DR is unique in that it makes an assessment without the need for a clinician to also interpret the image or results, making it usable by health care providers who may not normally be involved in eye care. IDx is a leading AI diagnostics company on a mission to transform the quality, accessibility, and affordability of healthcare world-wide.
Medvice provides real time medical advice to clients, who can access and store their Electronic Health Records (EHRs) over a decentralized blockchain. Medvice uses machine learning aided decision making to help physicians predict medical red flags (i.e. medical emergencies which require clinical assistance) before serving them. Predictive Medical Technologies uses intensive care unit data to identify patients likely to suffer cardiac incidents. Modernizing Medicine uses knowledge gathered from healthcare professionals as well as patient outcome data to recommend treatments. "Compassionate AI Lab" uses grid cell, place cell and path integration with machine learning for the navigation of blind people. Nimblr.ai uses an A.I. Chatbot to connect scheduling EHR systems and automate the confirmation and scheduling of patients.
Infermedica's free mobile application Symptomate is the top-rated symptom checker in Google Play. The company also released the first AI-based voice assistant symptom checker for three major voice platforms: Amazon Alexa, Microsoft Cortana, and Google Assistant.
Digital consultant apps like Babylon Health's GP at Hand, Ada Health, and Your.MD use AI to give medical consultation based on personal medical history and common medical knowledge. Users report their symptoms into the app, which uses speech recognition to compare against a database of illnesses. Babylon then offers a recommended action, taking into account the user’s medical history. Entrepreneurs in healthcare have been effectively using seven business model archetypes to take AI solution to the marketplace. These archetypes depends on the value generate for the target user (e.g. patient focus vs. healthcare provider and payer focus) and value capturing mechanisms (e.g. providing information or connecting stakeholders).
The use of AI is predicted to decrease medical costs as there will be more accuracy in diagnosis and better predictions in the treatment plan as well as more prevention of disease .
Other future uses for AI include Brain-computer Interfaces (BCI) which are predicted to help those with trouble moving, speaking or with a spinal cord injury. The BCIs will use AI to help these patients move and communicate by decoding neural activates 
Virtual nursing assistants are predicted to become more common and these will use AI to answer patient’s questions and help reduce unnecessary hospital visits. They will be useful as they are available 24/7 and may eventually be able to give wellness checks with the use of AI and voice 
As technology evolves and is implemented in more workplaces, many fear that their jobs will be replaced by robots or machines. The U.S. News Staff (2018) writes that in the near future, doctors who utilize AI will “win out” the doctors who don’t. AI will not replace healthcare workers but instead allow them more time for bed side cares. AI may avert healthcare worker burn out and cognitive overload. Overall, as Quan-Haase (2018) says, technology “extends to the accomplishment of societal goals, including higher levels of security, better means of communication over time and space, improved health care, and increased autonomy” (p.43). As we adapt and utilize AI into our practice we can enhance our care to our patients resulting in greater outcomes for all.
Expanding Care to Developing Nations
With an increase in the use of AI, more care my become available to those in developing nations. AI continues to expand in its abilities and as it is able to interpret radiology, it may be able to diagnose more people with the need for less doctors as there is a shortage in many of these nations.  The goal of AI is to teach others in the world, which will then lead to improved treatment, and eventually greater global health. Using AI in developing nations who do not have the resources will diminish the need for outsourcing and can use AI to improve patient care. 
While research on the use of AI in healthcare aims to validate its efficacy in improving patient outcomes before its broader adoption, its use may nonetheless introduce several new types of risk to patients and healthcare providers, such as algorithmic bias, Do not resuscitate implications, and other machine morality issues. These challenges of the clinical use of AI has brought upon potential need for regulations.
Currently no regulations exist specifically for the use of AI in healthcare. In May 2016, the White House announced its plan to host a series of workshops and formation of the National Science and Technology Council (NSTC) Subcommittee on Machine Learning and Artificial Intelligence. In October 2016, the group published The National Artificial Intelligence Research and Development Strategic Plan, outlining its proposed priorities for Federally-funded AI research and development (within government and academia). The report notes a strategic R&D plan for the subfield of health information technology is in development stages.
The only agency that has expressed concern is the FDA. Bakul Patel, the Associate Center Director for Digital Health of the FDA, is quoted saying in May 2017.
“We're trying to get people who have hands-on development experience with a product's full life cycle. We already have some scientists who know artificial intelligence and machine learning, but we want complementary people who can look forward and see how this technology will evolve.”
The joint ITU - WHO Focus Group on AI for Health has built a platform for the testing and benchmarking of AI applications in health domain. As of November 2018, eight use cases are being benchmarked, including assessing breast cancer risk from histopathological imagery, guiding anti-venom selection from snake images, and diagnosing skin lesions.
Investments from the US government in healthcare initiatives that will rely on AI include its $1B proposed budget for the Cancer Moonshot and $215M proposed investment in the Precision Medicine Initiative.
- Clinical decision support system
- Computer-aided diagnosis
- Computer-aided simple triage
- IBM Watson Healthcare
- DeepMind Healthcare
- Medical image computing
- Speech recognition software in healthcare
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