IBM Watson Health

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.[35] Other projects include medical image analysis of tumor progression and the development of programmable cells.[36]

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.[37] A second project with the NHS involves analysis of medical images collected from NHS patients to develop computer vision algorithms to detect cancerous tissues.[38]

Intel's venture capital arm Intel Capital recently invested in startup Lumiata which uses AI to identify at-risk patients and develop care options.[39]

Artificial intelligence in healthcare is the use of complex algorithms and software to emulate human cognition in the analysis of complicated 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. These algorithms 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 it has been told explicitly, (2) and algorithms are black boxes; algorithms can predict extremely precise, but not the cause or the why.[40]

The primary aim of health-related AI applications is to analyze relationships between prevention or treatment techniques and patient outcomes.[41] 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,[42][43] and National Health Service,[44] have developed AI algorithms for their departments. Large technology companies such as IBM[45] and Google,[44] and startups such as Welltok and Ayasdi,[46] 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.[47] Companies are developing predictive analytics solutions that help healthcare managers improve business operations through increasing utilization, decreasing patient boarding, reducing length of stay and optimizing staffing levels.[48]

The following medical fields are of interest in artificial intelligence research:

The ability to interpret imaging results with radiology 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 could detect pneumonia at that specific site, in those patients involved, with a better average F1 metric (a statistical metric based on accuracy and recall), than the radiologists involved in that trial.[49] The radiology conference Radiological Society of North America has implemented presentations on AI in imaging during its annual meeting. The emergence of AI technology in radiology is perceived as a threat by some specialists, as the technology can achieve improvements in certain statistical metrics in isolated cases, as opposed to specialists.[50][51]

Recent advances have suggested the use of AI to describe and evaluate the outcome of maxillo-facial surgery or the assessment of cleft palate therapy in regard to facial attractiveness or age appearance.[52][53]

In 2018, a paper published in the journal Annals of Oncology mentioned that skin cancer could be detected more accurately by an artificial intelligence system (which used a deep learning convolutional neural network) than by dermatologists. On average, the human dermatologists accurately detected 86.6% of skin cancers from the images, compared to 95% for the CNN machine.[54]

There are many diseases out there but there also many ways that AI has been used to efficiently and accurately diagnose them. Some of the diseases that are the most notorious such as Diabetes, and Cardiovascular Disease (CVD) which are both in the top ten for causes of death worldwide have been the basis behind  a lot of the research/testing to help get an accurate diagnosis. Due to such a high mortality rate being associated with these diseases there have been efforts to integrate various methods in helping get accurate diagnosis’.

An article by Jiang, et al (2017)[55] demonstrated that there are multiple different types of AI techniques that have been used for a variety of different diseases. Some of these techniques discussed by Jiang, et al include: Support vector machines, neural networks, Decision trees, and many more. Each of these techniques are described as having a “training goal” so “classifications agree with the outcomes as much as possible…”.[55]

To demonstrate some specifics for disease diagnosis/classification there are two different techniques used in the classification of these diseases include using “Artificial Neural Networks (ANN) and Bayesian Networks (BN)”.[56] From a review of multiple different papers within the timeframe of 2008-2017[56] observed within them which of the two techniques were better.  The conclusion that was drawn was that “the early classification of these  diseases can be achieved developing machine learning models such as Artificial Neural Network and Bayesian Network.”  Another conclusion Alic, et al (2017)[56] was able to draw was that between the two ANN and BN that ANN was better and could more accurately classify diabetes/CVD with a mean accuracy in “both cases (87.29 for diabetes and 89.38 for CVD).

The increase of Telemedicine, has shown the rise of possible AI applications.[57] The ability to monitor patients using AI may allow for the communication of information to physicians if possible disease activity may have occurred.[58] 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.

Electronic health records are crucial to the digitalization and information spread of the healthcare industry. However logging all of this data comes with its own problems like cognitive overload and burnout for users. EHR developers are now automating much of the process and even starting to use natural language processing (NLP) tools to improve this process. One study conducted by the Centerstone research institute found that predictive modeling of EHR data has achieved 70–72% accuracy in predicting individualized treatment response at baseline. Meaning using an AI tool that scans EHR data it can pretty accurately predict the course of disease in a person.

Improvements in Natural Language Processing led to the development of algorithms to identify drug-drug interactions in medical literature.[59][60][61][62] Drug-drug interactions pose a threat to those taking multiple medications simultaneously, and the danger increases with the number of medications being taken.[63] To address the difficulty of tracking all known or suspected drug-drug interactions, machine learning algorithms have been created to extract information on interacting drugs and their possible effects from medical literature. Efforts were consolidated in 2013 in the DDIExtraction Challenge, in which a team of researchers at Carlos III University assembled a corpus of literature on drug-drug interactions to form a standardized test for such algorithms.[64] Competitors were tested on their ability to accurately determine, from the text, which drugs were shown to interact and what the characteristics of their interactions were.[65] Researchers continue to use this corpus to standardize the measure of the effectiveness of their algorithms.[59][60][62]

Other algorithms identify drug-drug interactions from patterns in user-generated content, especially electronic health records and/or adverse event reports.[60][61] Organizations such as the FDA Adverse Event Reporting System (FAERS) and the World Health Organization’s VigiBase allow doctors to submit reports of possible negative reactions to medications. Deep learning algorithms have been developed to parse these reports and detect patterns that imply drug-drug interactions.[66]