Artificial Intelligence In Healthcare
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Artificial intelligence in healthcare is an overarching term used to describe the use of machine-learning algorithms and software, or artificial intelligence (AI), to mimic human cognition in the analysis, presentation, and comprehension of complex medical and health care data. Specifically, AI is the ability of computer algorithms to approximate conclusions based solely on input data. The primary aim of health-related AI applications is to analyze relationships between clinical techniques and patient outcomes. AI programs are applied to practices such as
diagnostics Diagnosis is the identification of the nature and cause of a certain phenomenon. Diagnosis is used in many different disciplines, with variations in the use of logic, analytics, and experience, to determine "cause and effect". In systems engineer ...
, treatment protocol development,
drug development Drug development is the process of bringing a new pharmaceutical drug to the market once a lead compound has been identified through the process of drug discovery. It includes preclinical research on microorganisms and animals, filing for re ...
,
personalized medicine Personalized medicine, also referred to as precision medicine, is a medical model that separates people into different groups—with medical decisions, practices, interventions and/or products being tailored to the individual patient based on the ...
, and patient monitoring and care. What differentiates AI technology from traditional technologies in healthcare is the ability to gather data, process it, and produce a well-defined output to the end-user. AI does this through
machine learning Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine ...
algorithms In mathematics and computer science, an algorithm () is a finite sequence of rigorous instructions, typically used to solve a class of specific problems or to perform a computation. Algorithms are used as specifications for performing c ...
and deep learning. These processes can recognize patterns in behavior and create their own logic. To gain useful insights and predictions, machine learning models must be trained using extensive amounts of input data. AI algorithms behave differently from humans in two ways: (1) algorithms are literal: once a goal is set, the algorithm learns exclusively from the input data and can only understand what it has been programmed to do, (2) and some deep learning algorithms are
black box In science, computing, and engineering, a black box is a system which can be viewed in terms of its inputs and outputs (or transfer characteristics), without any knowledge of its internal workings. Its implementation is "opaque" (black). The te ...
es; algorithms can predict with extreme precision, but offer little to no comprehensible explanation to the logic behind its decisions aside from the data and type of algorithm used. As widespread use of AI in healthcare is relatively new, research is ongoing into its application in various fields of medicine and industry. Additionally, greater consideration is being given to the unprecedented ethical concerns related to its practice such as data privacy, automation of jobs, and representation biases.


History

Research in the 1960s and 1970s produced the first problem-solving program, or expert system, known as
Dendral Dendral was a project in artificial intelligence (AI) of the 1960s, and the computer software expert system that it produced. Its primary aim was to study hypothesis formation and discovery in science. For that, a specific task in science was chose ...
. 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 In mathematics, fuzzy sets (a.k.a. uncertain sets) are sets whose elements have degrees of membership. Fuzzy sets were introduced independently by Lotfi A. Zadeh in 1965 as an extension of the classical notion of set. At the same time, defined ...
theory,
Bayesian network A Bayesian network (also known as a Bayes network, Bayes net, belief network, or decision network) is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG). Bay ...
s, and
artificial neural network Artificial neural networks (ANNs), usually simply called neural networks (NNs) or neural nets, are computing systems inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected unit ...
s, have been applied to intelligent computing systems in healthcare. Medical and technological advancements occurring over this half-century period that have enabled the growth of healthcare-related applications of AI to include: * Improvements in
computing power In computing, computer performance is the amount of useful work accomplished by a computer system. Outside of specific contexts, computer performance is estimated in terms of accuracy, efficiency and speed of executing computer program instructio ...
resulting in faster data collection and data processing * Growth of genomic sequencing databases * Widespread implementation of electronic health record systems * Improvements in
natural language processing Natural language processing (NLP) is an interdisciplinary subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to pro ...
and computer vision, enabling machines to replicate human perceptual processes * Enhanced the precision of robot-assisted surgery * Improvements in deep learning techniques and data logs in rare diseases AI algorithms can also be used to analyze large amounts of data through electronic health records for disease prevention and diagnosis. Medical institutions such as
The Mayo Clinic The Mayo Clinic () is a nonprofit American academic medical center focused on integrated health care, education, and research. It employs over 4,500 physicians and scientists, along with another 58,400 administrative and allied health staf ...
,
Memorial Sloan Kettering Cancer Center Memorial Sloan Kettering Cancer Center (MSK or MSKCC) is a cancer treatment and research institution in the borough of Manhattan in New York City, founded in 1884 as the New York Cancer Hospital. MSKCC is one of 52 National Cancer Institute– ...
, and the British
National Health Service The National Health Service (NHS) is the umbrella term for the publicly funded healthcare systems of the United Kingdom (UK). Since 1948, they have been funded out of general taxation. There are three systems which are referred to using the " ...
, have developed AI algorithms for their departments. Large technology companies such as IBM and
Google Google LLC () is an American multinational technology company focusing on search engine technology, online advertising, cloud computing, computer software, quantum computing, e-commerce, artificial intelligence, and consumer electronics. ...
, have also developed AI algorithms for healthcare. Additionally, hospitals are looking to AI software to support operational initiatives that increase cost saving, improve patient satisfaction, and satisfy their staffing and workforce needs. Currently, the United States government is investing billions of dollars to progress the development of AI in healthcare. Companies are developing technologies that help healthcare managers improve business operations through increasing utilization, decreasing patient boarding, reducing length of stay and
optimizing Mathematical optimization (alternatively spelled ''optimisation'') or mathematical programming is the selection of a best element, with regard to some criterion, from some set of available alternatives. It is generally divided into two subfi ...
staffing levels.


Clinical applications


Cardiovascular

Artificial intelligence algorithms have shown promising results in accurately diagnosing and risk stratifying patients with concern for coronary artery disease, showing potential as an initial triage tool, though few studies have directly compared the accuracy of machine learning models to clinician diagnostic ability. Other algorithms have been used in predicting patient mortality, medication effects, and adverse events following treatment for acute coronary syndrome. Wearables, smartphones, and internet-based technologies have also shown the ability to monitor patients' cardiac data points, expanding the amount of data and the various settings AI models can use and potentially enabling earlier detection of cardiac events occurring outside of the hospital. Another growing area of research is the utility of AI in classifying heart sounds and diagnosing valvular disease. Challenges of AI in cardiovascular medicine have included the limited data available to train machine learning models, such as limited data on social determinants of health as they pertain to
cardiovascular disease Cardiovascular disease (CVD) is a class of diseases that involve the heart or blood vessels. CVD includes coronary artery diseases (CAD) such as angina and myocardial infarction (commonly known as a heart attack). Other CVDs include stroke, h ...
.


Dermatology

Dermatology is an imaging abundant speciality and the development of deep learning has been strongly tied to image processing. Therefore, there is a natural fit between the dermatology and deep learning. There are 3 main imaging types in dermatology: contextual images, macro images, micro images. For each modality, deep learning showed great progress. Han et al. showed keratinocytic skin cancer detection from face photographs. Esteva et al. demonstrated dermatologist-level classification of skin cancer from lesion images. Noyan et al. demonstrated a
convolutional neural network In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of artificial neural network (ANN), most commonly applied to analyze visual imagery. CNNs are also known as Shift Invariant or Space Invariant Artificial Neural Netwo ...
that achieved 94% accuracy at identifying skin cells from microscopic Tzanck smear images. Recent advances have suggested the use of AI to describe and evaluate the outcome of maxillo-facial surgery or the assessment of
cleft palate A cleft lip contains an opening in the upper lip that may extend into the nose. The opening may be on one side, both sides, or in the middle. A cleft palate occurs when the palate (the roof of the mouth) contains an opening into the nose. The te ...
therapy in regard to facial attractiveness or age appearance. In 2018, a paper published in the journal
Annals of Oncology The ''Annals of Oncology'' is a peer-reviewed medical journal of oncology, published by Elsevier. It is the official journal of the European Society for Medical Oncology. The editor-in-chief An editor-in-chief (EIC), also known as lead editor o ...
mentioned that skin cancer could be detected more accurately by an artificial intelligence system (which used a deep learning convolutional neural network) than by
dermatologist Dermatology is the branch of medicine dealing with the skin.''Random House Webster's Unabridged Dictionary.'' Random House, Inc. 2001. Page 537. . It is a speciality with both medical and surgical aspects. A dermatologist is a specialist medical ...
s. On average, the human dermatologists accurately detected 86.6% of skin cancers from the images, compared to 95% for the CNN machine.


Gastroenterology

AI can play a role in various facets of the field of gastroenterology.
Endoscopic An endoscopy is a procedure used in medicine to look inside the body. The endoscopy procedure uses an endoscope to examine the interior of a hollow organ or cavity of the body. Unlike many other medical imaging techniques, endoscopes are insert ...
exams such as esophagogastroduodenoscopies (EGD) and
colonoscopies Colonoscopy () or coloscopy () is the endoscopic examination of the large bowel and the distal part of the small bowel with a CCD camera or a fiber optic camera on a flexible tube passed through the anus. It can provide a visual diagnosis (''e.g ...
rely on rapid detection of abnormal tissue. By enhancing these endoscopic procedures with AI, clinicians can more rapidly identify diseases, determine their severity, and visualize blind spots. Early trials in using AI detection systems of early gastric cancer have shown sensitivity close to expert endoscopists.


Infectious diseases

AI has shown potential in both the laboratory and clinical spheres of
infectious disease An infection is the invasion of tissues by pathogens, their multiplication, and the reaction of host tissues to the infectious agent and the toxins they produce. An infectious disease, also known as a transmissible disease or communicable di ...
medicine. As the
novel coronavirus Novel coronavirus (nCoV) is a provisional name given to coronaviruses of medical significance before a permanent name is decided upon. Although coronaviruses are endemic in humans and infections normally mild, such as the common cold (caused by ...
ravages through the globe, the United States is estimated to invest more than $2 billion in AI-related healthcare research by 2025, more than 4 times the amount spent in 2019 ($463 million).
Neural network A neural network is a network or circuit of biological neurons, or, in a modern sense, an artificial neural network, composed of artificial neurons or nodes. Thus, a neural network is either a biological neural network, made up of biological ...
s have been developed to rapidly and accurately detect a host response to
COVID-19 Coronavirus disease 2019 (COVID-19) is a contagious disease caused by a virus, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The first known case was COVID-19 pandemic in Hubei, identified in Wuhan, China, in December ...
from
mass spectrometry Mass spectrometry (MS) is an analytical technique that is used to measure the mass-to-charge ratio of ions. The results are presented as a ''mass spectrum'', a plot of intensity as a function of the mass-to-charge ratio. Mass spectrometry is use ...
samples. Other applications include support-vector machines identifying
antimicrobial resistance Antimicrobial resistance (AMR) occurs when microbes evolve mechanisms that protect them from the effects of antimicrobials. All classes of microbes can evolve resistance. Fungi evolve antifungal resistance. Viruses evolve antiviral resistance. P ...
, machine learning analysis of blood smears to detect
malaria Malaria is a mosquito-borne infectious disease that affects humans and other animals. Malaria causes symptoms that typically include fever, tiredness, vomiting, and headaches. In severe cases, it can cause jaundice, seizures, coma, or death. S ...
, and improved point-of-care testing of
Lyme disease Lyme disease, also known as Lyme borreliosis, is a vector-borne disease caused by the ''Borrelia'' bacterium, which is spread by ticks in the genus ''Ixodes''. The most common sign of infection is an expanding red rash, known as erythema migran ...
based on antigen detection. Additionally, AI has been investigated for improving diagnosis of meningitis,
sepsis Sepsis, formerly known as septicemia (septicaemia in British English) or blood poisoning, is a life-threatening condition that arises when the body's response to infection causes injury to its own tissues and organs. This initial stage is follo ...
, and
tuberculosis Tuberculosis (TB) is an infectious disease usually caused by '' Mycobacterium tuberculosis'' (MTB) bacteria. Tuberculosis generally affects the lungs, but it can also affect other parts of the body. Most infections show no symptoms, in ...
, as well as predicting treatment complications in
hepatitis B Hepatitis B is an infectious disease caused by the '' Hepatitis B virus'' (HBV) that affects the liver; it is a type of viral hepatitis. It can cause both acute and chronic infection. Many people have no symptoms during an initial infection. ...
and
hepatitis C Hepatitis C is an infectious disease caused by the hepatitis C virus (HCV) that primarily affects the liver; it is a type of viral hepatitis. During the initial infection people often have mild or no symptoms. Occasionally a fever, dark urine, a ...
patients.


Oncology

AI has been explored for use in
cancer Cancer is a group of diseases involving abnormal cell growth with the potential to invade or spread to other parts of the body. These contrast with benign tumors, which do not spread. Possible signs and symptoms include a lump, abnormal b ...
diagnosis, risk stratification, molecular characterization of tumors, and cancer drug discovery. A particular challenge in oncologic care that AI is being developed to address is the ability to accurately predict which treatment protocols will be best suited for each patient based on their individual genetic, molecular, and tumor-based characteristics. Through its ability to translate images to mathematical sequences, AI has been trialed in cancer diagnostics with the reading of imaging studies and
pathology Pathology is the study of the causes and effects of disease or injury. The word ''pathology'' also refers to the study of disease in general, incorporating a wide range of biology research fields and medical practices. However, when used in ...
slides. In January 2020, researchers demonstrated an AI system, based on a
Google Google LLC () is an American multinational technology company focusing on search engine technology, online advertising, cloud computing, computer software, quantum computing, e-commerce, artificial intelligence, and consumer electronics. ...
DeepMind algorithm, capable of surpassing human experts in
breast cancer Breast cancer is cancer that develops from breast tissue. Signs of breast cancer may include a lump in the breast, a change in breast shape, dimpling of the skin, milk rejection, fluid coming from the nipple, a newly inverted nipple, or a re ...
detection {{Unreferenced, date=March 2018 In general, detection is the action of accessing information without specific cooperation from with the sender. In the history of radio communications, the term " detector" was first used for a device that detected ...
. In July 2020, it was reported that an AI algorithm developed by the University of Pittsburgh achieves the highest accuracy to date in identifying prostate cancer, with 98% sensitivity and 97% specificity.


Pathology

For many diseases,
pathological Pathology is the study of the causes and effects of disease or injury. The word ''pathology'' also refers to the study of disease in general, incorporating a wide range of biology research fields and medical practices. However, when used in th ...
analysis of cells and tissues is considered to be the gold standard of disease diagnosis. AI-assisted pathology tools have been developed to assist with the diagnosis of a number of diseases, including breast cancer,
hepatitis B Hepatitis B is an infectious disease caused by the '' Hepatitis B virus'' (HBV) that affects the liver; it is a type of viral hepatitis. It can cause both acute and chronic infection. Many people have no symptoms during an initial infection. ...
,
gastric cancer Stomach cancer, also known as gastric cancer, is a cancer that develops from the lining of the stomach. Most cases of stomach cancers are gastric carcinomas, which can be divided into a number of subtypes, including gastric adenocarcinomas. Lymph ...
, and
colorectal cancer Colorectal cancer (CRC), also known as bowel cancer, colon cancer, or rectal cancer, is the development of cancer from the colon or rectum (parts of the large intestine). Signs and symptoms may include blood in the stool, a change in bowel m ...
. AI has also been used to predict genetic mutations and prognosticate disease outcomes. AI is well-suited for use in low-complexity pathological analysis of large-scale
screening Screening may refer to: * Screening cultures, a type a medical test that is done to find an infection * Screening (economics), a strategy of combating adverse selection (includes sorting resumes to select employees) * Screening (environmental), a ...
samples, such as colorectal or
breast cancer Breast cancer is cancer that develops from breast tissue. Signs of breast cancer may include a lump in the breast, a change in breast shape, dimpling of the skin, milk rejection, fluid coming from the nipple, a newly inverted nipple, or a re ...
screening, thus lessening the burden on pathologists and allowing for faster turnaround of sample analysis. Several deep learning and artificial
neural network A neural network is a network or circuit of biological neurons, or, in a modern sense, an artificial neural network, composed of artificial neurons or nodes. Thus, a neural network is either a biological neural network, made up of biological ...
models have shown accuracy similar to that of human pathologists, and a study of deep learning assistance in diagnosing
metastatic Metastasis is a pathogenic agent's spread from an initial or primary site to a different or secondary site within the host's body; the term is typically used when referring to metastasis by a cancerous tumor. The newly pathological sites, then, ...
breast cancer in lymph nodes showed that the accuracy of humans with the assistance of a deep learning program was higher than either the humans alone or the AI program alone. Additionally, implementation of
digital pathology Digital pathology is a sub-field of pathology that focuses on data management based on information generated from digitized specimen slides. Through the use of computer-based technology, digital pathology utilizes virtual microscopy. Glass slide ...
is predicted to save over $12 million for a university center over the course of five years, though savings attributed to AI specifically have not yet been widely researched. The use of augmented and
virtual reality Virtual reality (VR) is a simulated experience that employs pose tracking and 3D near-eye displays to give the user an immersive feel of a virtual world. Applications of virtual reality include entertainment (particularly video games), educ ...
could prove to be a stepping stone to wider implementation of AI-assisted pathology, as they can highlight areas of concern on a pathology sample and present them in real-time to a pathologist for more efficient review. AI also has the potential to identify
histological Histology, also known as microscopic anatomy or microanatomy, is the branch of biology which studies the microscopic anatomy of biological tissues. Histology is the microscopic counterpart to gross anatomy, which looks at larger structures vis ...
findings at levels beyond what the human eye can see, and has shown the ability to utilize
genotypic The genotype of an organism is its complete set of genetic material. Genotype can also be used to refer to the alleles or variants an individual carries in a particular gene or genetic location. The number of alleles an individual can have in a ...
and
phenotypic In genetics, the phenotype () is the set of observable characteristics or traits of an organism. The term covers the organism's morphology or physical form and structure, its developmental processes, its biochemical and physiological proper ...
data to more accurately detect the tumor of origin for metastatic cancer. One of the major current barriers to widespread implementation of AI-assisted pathology tools is the lack of prospective, randomized, multi-center controlled
trials In law, a trial is a coming together of parties to a dispute, to present information (in the form of evidence) in a tribunal, a formal setting with the authority to adjudicate claims or disputes. One form of tribunal is a court. The tribun ...
in determining the true clinical utility of AI for pathologists and patients, highlighting a current area of need in AI and healthcare research.


Primary care

Primary care has become one key development area for AI technologies. AI in primary care has been used for supporting decision making, predictive modelling, and business analytics. Despite the rapid advances in AI technologies, general practitioners' view on the role of AI in primary care is very limited–mainly focused on administrative and routine documentation tasks. There are only few examples of AI decision support systems that were prospectively assessed on clinical efficacy when used in practice by physicians. But there are cases where the use of these systems yielded a positive effect on treatment choice by physicians.


Psychiatry

In psychiatry, AI applications are still in a phase of proof-of-concept. Areas where the evidence is widening quickly include predictive modelling of diagnosis and treatment outcomes, chatbots, conversational agents that imitate human behaviour and which have been studied for anxiety and depression. Challenges include the fact that many applications in the field are developed and proposed by private corporations, such as the screening for suicidal ideation implemented by Facebook in 2017. Such applications outside the healthcare system raise various professional, ethical and regulatory questions. Another issue is often with the validity and interpretability of the models. Small training datasets contain bias that is inherited by the models, and compromises the generalizability and stability of these models. Such models may also have the potential to be discriminatory against minority groups that are underrepresented in samples.


Radiology

AI is being studied within the field of
radiology Radiology ( ) is the medical discipline that uses medical imaging to diagnose diseases and guide their treatment, within the bodies of humans and other animals. It began with radiography (which is why its name has a root referring to radiat ...
to detect and diagnose diseases through Computerized Tomography (CT) and
Magnetic Resonance Magnetic resonance is a process by which a physical excitation (resonance) is set up via magnetism. This process was used to develop magnetic resonance imaging and Nuclear magnetic resonance spectroscopy technology. It is also being used to ...
(MR) Imaging. It may be particularly useful in settings where demand for human expertise exceeds supply, or where data is too complex to be efficiently interpreted by human readers. Several deep learning models have shown the capability to be roughly as accurate as healthcare professionals in identifying diseases through medical imaging, though few of the studies reporting these findings have been externally validated. AI can also provide non-interpretive benefit to radiologists, such as reducing noise in images, creating high-quality images from lower doses of radiation, enhancing MR image quality, and automatically assessing image quality. Further research investigating the use of AI in
nuclear medicine Nuclear medicine or nucleology is a medical specialty involving the application of radioactive substances in the diagnosis and treatment of disease. Nuclear imaging, in a sense, is " radiology done inside out" because it records radiation emi ...
focuses on image reconstruction, anatomical landmarking, and the enablement of lower doses in imaging studies.


Systems applications


Disease diagnosis

An article by Jiang, et al. (2017) demonstrated that there are several types of AI techniques that have been used for a variety of different diseases, such as support vector machines, neural networks, and decision trees. Each of these techniques is described as having a "training goal" so "classifications agree with the outcomes as much as possible…". 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)". It was found that ANN was better and could more accurately classify diabetes and CVD. Through the use of Medical Learning Classifiers (MLC's), Artificial Intelligence has been able to substantially aid doctors in patient diagnosis through the manipulation of mass
Electronic Health Records An electronic health record (EHR) is the systematized collection of patient and population electronically stored health information in a digital format. These records can be shared across different health care settings. Records are shared throu ...
(EHR's). Medical conditions have grown more complex, and with a vast history of electronic medical records building, the likelihood of case duplication is high. Although someone today with a rare illness is less likely to be the only person to have had any given disease, the inability to access cases from similarly symptomatic origins is a major roadblock for physicians. The implementation of AI to not only help find similar cases and treatments, but also factor in chief symptoms and help the physicians ask the most appropriate questions helps the patient receive the most accurate diagnosis and treatment possible.


Telemedicine

The increase of telemedicine, the treatment of patients remotely, has shown the rise of possible AI applications. AI can assist in caring for patients remotely by monitoring their information through sensors. A wearable device may allow for constant monitoring of a patient and the ability to notice changes that may be less distinguishable by humans. The information can be compared to other data that has already been collected using artificial intelligence algorithms that alert physicians if there are any issues to be aware of. Another application of artificial intelligence is chat-bot therapy. Some researchers charge that the reliance on chatbots for mental healthcare does not offer the reciprocity and accountability of care that should exist in the relationship between the consumer of mental healthcare and the care provider (be it a chat-bot or psychologist), though. Since the average age has risen due to a longer life expectancy, artificial intelligence could be useful in helping take care of older populations. Tools such as environment and personal sensors can identify a person's regular activities and alert a caretaker if a behavior or a measured vital is abnormal. Although the technology is useful, there are also discussions about limitations of monitoring in order to respect a person's privacy since there are technologies that are designed to map out home layouts and detect human interactions.


Electronic health records

Electronic health records (EHR) are crucial to the digitalization and information spread of the healthcare industry. Now that around 80% of medical practices use EHR, the next step is to use artificial intelligence to interpret the records and provide new information to physicians. One application uses natural language processing (NLP) to make more succinct reports that limit the variation between medical terms by matching similar medical terms. For example, the term heart attack and
myocardial infarction A myocardial infarction (MI), commonly known as a heart attack, occurs when blood flow decreases or stops to the coronary artery of the heart, causing damage to the heart muscle. The most common symptom is chest pain or discomfort which may ...
mean the same things, but physicians may use one over the over based on personal preferences. NLP algorithms consolidate these differences so that larger datasets can be analyzed. Another use of NLP identifies phrases that are redundant due to repetition in a physician's notes and keeps the relevant information to make it easier to read. Other applications use concept processing to analyze the information entered by the current patient's doctor to present similar cases and help the physician remember to include all relevant details. Beyond making content edits to an EHR, there are AI algorithms that evaluate an individual patient's record and predict a risk for a disease based on their previous information and family history. One general algorithm is a rule-based system that makes decisions similarly to how humans use flow charts. This system takes in large amounts of data and creates a set of rules that connect specific observations to concluded diagnoses. Thus, the algorithm can take in a new patient's data and try to predict the likeliness that they will have a certain condition or disease. Since the algorithms can evaluate a patient's information based on collective data, they can find any outstanding issues to bring to a physician's attention and save time. 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. These methods are helpful due to the fact that the amount of online health records doubles every five years. Physicians do not have the bandwidth to process all this data manually, and AI can leverage this data to assist physicians in treating their patients.


Drug Interactions

Improvements in
natural language processing Natural language processing (NLP) is an interdisciplinary subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to pro ...
led to the development of algorithms to identify drug-drug interactions in medical literature. Drug-drug interactions pose a threat to those taking multiple medications simultaneously, and the danger increases with the number of medications being taken. 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 University Charles III of Madrid ( es, Universidad Carlos III de Madrid) (UC3M) is a public university in the Community of Madrid, Spain. Established in 1989, UC3M is an institution with a distinctly international profile. It offers a broad rang ...
assembled a corpus of literature on drug-drug interactions to form a standardized test for such algorithms. 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. Researchers continue to use this corpus to standardize the measurement of the effectiveness of their algorithms. Other algorithms identify drug-drug interactions from patterns in
user-generated content User-generated content (UGC), alternatively known as user-created content (UCC), is any form of content, such as images, videos, text, testimonials, and audio, that has been posted by users on online platforms such as social media, discussion f ...
, especially electronic health records and/or adverse event reports. Organizations such as the FDA Adverse Event Reporting System (FAERS) and the World Health Organization's
VigiBase VigiBase is a World Health Organization's (WHO) global Individual Case Safety Report (ICSR) database that contains ICSRs submitted by the participating member states enrolled under WHO's international drug monitoring programme. It is the single lar ...
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.


Industry

The trend of large health companies merging allows for greater health data accessibility. Greater health data lays the groundwork for the implementation of AI algorithms. A large part of industry focus of implementation of AI in the healthcare sector is in the
clinical decision support system A clinical decision support system (CDSS) is a health information technology, provides clinicians, staff, patients, or other individuals with knowledge and person-specific information, to help health and health care. CDSS encompasses a variety of ...
s. As more data is collected, machine learning algorithms adapt and allow for more robust responses and solutions. Numerous companies are exploring the possibilities of the incorporation of
big data Though used sometimes loosely partly because of a lack of formal definition, the interpretation that seems to best describe Big data is the one associated with large body of information that we could not comprehend when used only in smaller am ...
in the healthcare industry. Many companies investigate the market opportunities through the realms of "data assessment, storage, management, and analysis technologies" which are all crucial parts of the healthcare 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 Memorial Sloan Kettering Cancer Center (MSK or MSKCC) is a cancer treatment and research institution in the borough of Manhattan in New York City, founded in 1884 as the New York Cancer Hospital. MSKCC is one of 52 National Cancer Institute– ...
and
Cleveland Clinic Cleveland Clinic is a nonprofit American academic medical center based in Cleveland, Ohio. Owned and operated by the Cleveland Clinic Foundation, an Ohio nonprofit corporation established in 1921, it runs a 170-acre (69 ha) campus in Cleveland, ...
. IBM is also working with
CVS Health CVS Health Corporation (previously CVS Corporation and CVS Caremark Corporation) is an American healthcare company that owns CVS Pharmacy, a retail pharmacy chain; CVS Caremark, a pharmacy benefits manager; and Aetna, a health insurance prov ...
on AI applications in chronic disease treatment and with
Johnson & Johnson Johnson & Johnson (J&J) is an American multinational corporation founded in 1886 that develops medical devices, pharmaceuticals, and consumer packaged goods. Its common stock is a component of the Dow Jones Industrial Average and the company i ...
on analysis of scientific papers to find new connections for drug development. In May 2017, IBM and
Rensselaer Polytechnic Institute Rensselaer Polytechnic Institute () (RPI) is a private research university in Troy, New York, with an additional campus in Hartford, Connecticut. A third campus in Groton, Connecticut closed in 2018. RPI was established in 1824 by Stephen Van ...
began a joint project entitled Health Empowerment by Analytics, Learning and Semantics (HEALS), to explore using AI technology to enhance healthcare. *
Microsoft Microsoft Corporation is an American multinational technology corporation producing computer software, consumer electronics, personal computers, and related services headquartered at the Microsoft Redmond campus located in Redmond, Washing ...
's Hanover project, in partnership with
Oregon Health & Science University Oregon Health & Science University (OHSU) is a public research university focusing primarily on health sciences with a main campus, including two hospitals, in Portland, Oregon. The institution was founded in 1887 as the University of Oregon Med ...
's Knight Cancer Institute, analyzes medical research to predict the most effective
cancer Cancer is a group of diseases involving abnormal cell growth with the potential to invade or spread to other parts of the body. These contrast with benign tumors, which do not spread. Possible signs and symptoms include a lump, abnormal b ...
drug treatment options for patients. Other projects include medical image analysis of tumor progression and the development of programmable cells. *
Google Google LLC () is an American multinational technology company focusing on search engine technology, online advertising, cloud computing, computer software, quantum computing, e-commerce, artificial intelligence, and consumer electronics. ...
's DeepMind platform is being used by the UK
National Health Service The National Health Service (NHS) is the umbrella term for the publicly funded healthcare systems of the United Kingdom (UK). Since 1948, they have been funded out of general taxation. There are three systems which are referred to using the " ...
to detect certain health risks through data collected via a mobile app. A second project with the NHS involves the analysis of medical images collected from NHS patients to develop computer vision algorithms to detect cancerous tissues. *
Tencent Tencent Holdings Ltd. () is a Chinese multinational technology and entertainment conglomerate and holding company headquartered in Shenzhen. It is one of the highest grossing multimedia companies in the world based on revenue. It is also the wo ...
is working on several medical systems and services. These include AI Medical Innovation System (AIMIS), an AI-powered diagnostic medical imaging service; WeChat Intelligent Healthcare; and Tencent Doctorwork * Intel's venture capital arm
Intel Capital Intel Capital is a division of Intel Corporation, set up to manage corporate venture capital, global investment, mergers and acquisitions. Intel Capital makes equity investments in a range of technology startups and companies offering hardware, s ...
recently invested in startup Lumiata which uses AI to identify at-risk patients and develop care options. *
Neuralink Neuralink Corporation is a neurotechnology company that develops implantable brain–computer interfaces (BCIs). Founded by Elon Musk and a founding team of seven other scientists and engineers, the company's headquarters is in the Pioneer Bu ...
has come up with a next-generation
neuroprosthetic Neuroprosthetics (also called neural prosthetics) is a discipline related to neuroscience and biomedical engineering concerned with developing neural prostheses. They are sometimes contrasted with a brain–computer interface, which connects the ...
which intricately interfaces with thousands of neural pathways in the brain. Their process allows a chip, roughly the size of a quarter, to be inserted in the place of a chunk of a skull by a precision surgical robot to avoid accidental injury . Digital consultant apps like 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 buzzword.html" ;"title="wikipedia:Use plain English#Buzzwords">buzzword">wikipedia:Use plain English#Buzzwords">buzzwordto the marketplace. These archetypes depend on the value generated 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).
IFlytek iFlytek (), styled as iFLYTEK, is a partially state-owned Chinese information technology company established in 1999. It creates voice recognition software and 10+ voice-based internet/mobile products covering education, communication, music, int ...
launched a service robot "Xiao Man", which integrated artificial intelligence technology to identify the registered customer and provide personalized recommendations in medical areas. It also works in the field of medical imaging. Similar robots are also being made by companies such as UBTECH ("Cruzr") and
Softbank is a Japanese multinational conglomerate holding company headquartered in Minato, Tokyo which focuses on investment management. The Group primarily invests in companies operating in technology, energy, and financial sectors. It also runs the ...
Robotics ("Pepper"). The Indian startup
Haptik Haptik is an Indian enterprise conversational AI platform founded in August 2013, and acquired by Reliance Industries Limited in 2019. The company develops technology to enable enterprises to build conversational AI systems that allow users ...
recently developed a
WhatsApp WhatsApp (also called WhatsApp Messenger) is an internationally available freeware, cross-platform, centralized instant messaging (IM) and voice-over-IP (VoIP) service owned by American company Meta Platforms (formerly Facebook). It allows us ...
chatbot which answers questions associated with the deadly coronavirus in
India India, officially the Republic of India (Hindi: ), is a country in South Asia. It is the seventh-largest country by area, the second-most populous country, and the most populous democracy in the world. Bounded by the Indian Ocean on the so ...
. With the market for AI expanding constantly, large tech companies such as Apple, Google, Amazon, and Baidu all have their own AI research divisions, as well as millions of dollars allocated for acquisition of smaller AI based companies. Many automobile manufacturers are beginning to use machine learning healthcare in their cars as well. Companies such as BMW, GE, Tesla,
Toyota is a Japanese multinational automotive manufacturer headquartered in Toyota City, Aichi, Japan. It was founded by Kiichiro Toyoda and incorporated on . Toyota is one of the largest automobile manufacturers in the world, producing about 10 ...
, and
Volvo The Volvo Group ( sv, Volvokoncernen; legally Aktiebolaget Volvo, shortened to AB Volvo, stylized as VOLVO) is a Swedish multinational manufacturing corporation headquartered in Gothenburg. While its core activity is the production, distributio ...
all have new research campaigns to find ways of learning a driver's vital statistics to ensure they are awake, paying attention to the road, and not under the influence of substances or in emotional distress.


Expanding care to developing nations

Artificial intelligence continues to expand in its abilities to diagnose more people accurately in nations where fewer doctors are accessible to the public.  Many new technology companies such as
SpaceX Space Exploration Technologies Corp. (SpaceX) is an American spacecraft manufacturer, launcher, and a satellite communications corporation headquartered in Hawthorne, California. It was founded in 2002 by Elon Musk with the stated goal of ...
and the
Raspberry Pi Foundation The Raspberry Pi Foundation is a British charity and company founded in 2009 to promote the study of basic computer science in schools, and is responsible for developing the Raspberry Pi single-board computers. Foundation The Raspberry Pi Foun ...
have enabled more developing countries to have access to computers and the internet than ever before. With the increasing capabilities of AI over the internet, advanced machine learning algorithms can allow patients to get accurately diagnosed when they would previously have no way of knowing if they had a life-threatening disease or not. Using AI in developing nations who do not have the resources will diminish the need for outsourcing and can improve patient care. AI can allow for not only diagnosis of patient is areas where healthcare is scarce, but also allow for a good patient experience by resourcing files to find the best treatment for a patient. The ability of AI to adjust course as it goes also allows the patient to have their treatment modified based on what works for them; a level of individualized care that is nearly non-existent in developing countries.


Regulation

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 Algorithmic bias describes systematic and repeatable errors in a computer system that create " unfair" outcomes, such as "privileging" one category over another in ways different from the intended function of the algorithm. Bias can emerge from ...
,
Do not resuscitate A do-not-resuscitate order (DNR), also known as Do Not Attempt Resuscitation (DNAR), Do Not Attempt Cardiopulmonary Resuscitation (DNACPR), no code or allow natural death, is a medical order, written or oral depending on country, indicating tha ...
implications, and other machine morality issues. These challenges of the clinical use of AI has brought upon potential need for regulations. Currently, there are regulations pertaining to the collection of patient data. This includes policies such as the Health Insurance Portability and Accountability Act (
HIPAA The Health Insurance Portability and Accountability Act of 1996 (HIPAA or the Kennedy– Kassebaum Act) is a United States Act of Congress enacted by the 104th United States Congress and signed into law by President Bill Clinton on August 21, 1 ...
) and the European General Data Protection Regulation (
GDPR The General Data Protection Regulation (GDPR) is a European Union regulation on data protection and privacy in the EU and the European Economic Area (EEA). The GDPR is an important component of EU privacy law and of human rights law, in parti ...
). The GDPR pertains to patients within the EU and details the consent requirements for patient data use when entities collect patient healthcare data. Similarly, HIPAA protects healthcare data from patient records in the United States. In May 2016, the
White House The White House is the official residence and workplace of the president of the United States. It is located at 1600 Pennsylvania Avenue NW in Washington, D.C., and has been the residence of every U.S. president since John Adams in 1800. ...
announced its plan to host a series of workshops and formation of the
National Science and Technology Council The National Science and Technology Council (NSTC) is a council in the Executive Branch of the United States. It is designed to coordinate science and technology policy across the branches of federal government. History The National Science and ...
(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 Health information technology (HIT) is health technology, particularly information technology, applied to health and health care. It supports health information management across computerized systems and the secure exchange of health informatio ...
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 Artificial Intelligence for Health The ITU-WHO Focus Group on Artificial Intelligence for Health (AI for Health) is an inter-agency collaboration between the World Health Organization and the ITU, which created a benchmarking framework to assess the accuracy of AI in health. Th ...
(FG-AI4H) 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. In January 2021, the FDA published a new Action Plan, entitled Artificial Intelligence/Machine Learning I/MLBased Software as a Medical Device
aMD Advanced Micro Devices, Inc. (AMD) is an American multinational semiconductor company based in Santa Clara, California, that develops computer processors and related technologies for business and consumer markets. While it initially manufactur ...
Action Plan. This plan lays out the FDA's future plans for regulation of medical devices that would include artificial intelligence in their software. There are five main actions the FDA plans to take to increase regulation: 1. Tailored Regulatory Framework for Ai/M:-based SaMD, 2. Good Machine Learning Practice (GMLP), 3. Patient-Centered Approach Incorporating Transparency to Users, 4. Regulatory Science Methods Related to Algorithm Bias & Robustness, and 5. Real-World Performance(RWP). This plan was in direct response to stakeholders' feedback on a 2019 discussion paper also published by the FDA. According to the U.S. Department of Health and Human Services, the Office for Civil Rights (OCR) has issued guidance on the ethical use of AI in healthcare. The guidance outlines four core ethical principles that must be followed: respect for autonomy, beneficence, non-maleficence, and justice. Respect for autonomy requires that individuals have control over their own data and decisions. Beneficence requires that AI be used to do good, such as improving the quality of care and reducing health disparities. Non-maleficence requires that AI be used to do no harm, such as avoiding discrimination in decisions. Finally, justice requires that AI be used fairly, such as using the same standards for decisions no matter a person’s race, gender, or income level. The OCR also has issued rules and regulations to protect the privacy of individuals’ health information. These regulations require healthcare providers to follow certain privacy rules when using AI. The OCR also requires healthcare providers to keep a record of how they use AI and to ensure that their AI systems are secure. Overall, the U.S. has taken steps to protect individuals’ privacy and ethical issues related to AI in healthcare The U.S. is not the only country to develop or initiate regulations of data privacy with AI. Other countries have implemented data protection regulations, more specifically with company privacy invasions. In Denmark, the Danish Expert Group on Data Ethics has adopted recommendations on 'Data for the Benefit of the People'. These recommendations are intended to encourage responsible use of data in the business sector, with a focus on data processing. The recommendations include a focus on equality and non-discrimination with regards to bias in AI, as well as human dignity. The importance of human dignity is stressed, as it is said to outweigh profit and must be respected in all data processes The European Union has implemented the General Data Protection Regulation (GDPR) to protect citizens' personal data, which applies to the use of AI in healthcare. In addition, the European Commission has established guidelines to ensure the ethical development of AI, including the use of algorithms to ensure fairness and transparency. With GDPR, the European Union was the first to regulate AI through data protection legislation. The Union finds privacy as a fundamental human right, it wants to prevent unconsented and secondary uses of data by private or public health facilities. By streamlining access to personal data for health research and findings, they are able to instate the right and importance of patient privacy. In the United States, the Health Insurance Portability and Accountability Act (HIPAA) requires organizations to protect the privacy and security of patient information. The Centers for Medicare and Medicaid Services have also released guidelines for the development of AI-based medical applications.


Ethical concerns


Data collection

In order to effectively train Machine Learning and use AI in healthcare, massive amounts of data must be gathered. Acquiring this data, however, comes at the cost of patient privacy in most cases and is not well received publicly. For example, a survey conducted in the UK estimated that 63% of the population is uncomfortable with sharing their personal data in order to improve artificial intelligence technology. The scarcity of real, accessible patient data is a hindrance that deters the progress of developing and deploying more artificial intelligence in healthcare.


Automation

According to a recent study, AI can replace up to 35% of jobs in the UK within the next 10 to 20 years. However, of these jobs, it was concluded that AI has not eliminated any healthcare jobs so far. Though if AI were to automate healthcare related jobs, the jobs most susceptible to automation would be those dealing with digital information, radiology, and pathology, as opposed to those dealing with doctor to patient interaction. Automation can provide benefits alongside doctors as well. It is expected that doctors who take advantage of AI in healthcare will provide greater quality healthcare than doctors and medical establishments who do not. AI will likely not completely replace healthcare workers but rather give them more time to attend to their patients. AI may avert healthcare worker burnout and cognitive overload AI will ultimately help contribute to progression of societal goals which include better communication, improved quality of healthcare, and autonomy. Recently, there have been many discussions between healthcare experts in terms of AI and elder care. In relation to elder care, AI bots have been helpful in guiding older residents living in assisted living with entertainment and company. These bots are allowing staff in the home to have more one-on-one time with each resident, but the bots are also programmed with more ability in what they are able to do; such as knowing different languages and different types of care depending on the patient’s conditions. The bot is an AI machine, which means it goes through the same training as any other machine - using algorithms to parse the given data, learn from it and predict the outcome in relation to what situation is at hand


Bias

Since AI makes decisions solely on the data it receives as input, it is important that this data represents accurate patient demographics. In a hospital setting, patients do not have full knowledge of how predictive algorithms are created or calibrated. Therefore, these medical establishments can unfairly code their algorithms to discriminate against minorities and prioritize profits rather than providing optimal care. There can also be unintended bias in these algorithms that can exacerbate social and healthcare inequities.  Since AI's decisions are a direct reflection of its input data, the data it receives must have accurate representation of patient demographics. White males are overly represented in medical data sets. Therefore, having minimal patient data on minorities can lead to AI making more accurate predictions for majority populations, leading to unintended worse medical outcomes for minority populations. Collecting data from minority communities can also lead to medical discrimination. For instance, HIV is a prevalent virus among minority communities and HIV status can be used to discriminate against patients. In addition to biases that may arise from sample selection, different clinical systems used to collect data may also impact AI functionality. For example, radiographic systems and their outcomes (e.g., resolution) vary by provider. Moreover, clinician work practices, such as the positioning of the patient for radiography, can also greatly influence the data and make comparability difficult. However, these biases are able to be eliminated through careful implementation and a methodical collection of representative data.


See also

*
Artificial intelligence Artificial intelligence (AI) is intelligence—perceiving, synthesizing, and inferring information—demonstrated by machines, as opposed to intelligence displayed by animals and humans. Example tasks in which this is done include speech re ...
*
Glossary of artificial intelligence This glossary of artificial intelligence is a list of definitions of terms and concepts relevant to the study of artificial intelligence, its sub-disciplines, and related fields. Related glossaries include Glossary of computer science, Glossary o ...
*
Full body scanner A full-body scanner is a device that detects objects on or inside a person's body for security screening purposes, without physically removing clothes or making physical contact. Unlike metal detectors, full-body scanners can detect non-metal o ...
(i.e. Dermascanner, ...) *
BlueDot BlueDot Inc. is a Canadian software company. The company's flagship product is Insights, a software-as-service used to map the spread of infectious diseases. Description BlueDot was founded in 2013. According to the company's founder, BlueDot' ...
*
Clinical decision support system A clinical decision support system (CDSS) is a health information technology, provides clinicians, staff, patients, or other individuals with knowledge and person-specific information, to help health and health care. CDSS encompasses a variety of ...
*
Computer-aided diagnosis Computer-aided detection (CADe), also called computer-aided diagnosis (CADx), are systems that assist doctors in the interpretation of medical images. Imaging techniques in X-ray, MRI, Endoscopy, and ultrasound diagnostics yield a great deal o ...
*
Computer-aided simple triage Computer-aided simple triage (CAST) are computerized methods or systems that assist physicians in initial interpretation and classification of medical images. CAST is a sub-class of computer-aided diagnosis (CAD). CAST software systems perform a fu ...
*
Google DeepMind DeepMind Technologies is a British artificial intelligence subsidiary of Alphabet Inc. and research laboratory founded in 2010. DeepMind was acquired by Google in 2014 and became a wholly owned subsidiary of Alphabet Inc, after Google's restru ...
*
IBM Watson Health Merative, formerly IBM Watson Health, is a standalone company as of 2022. Merative offers products and services that help clients facilitate medical research, clinical research, Real world evidence, and healthcare services, through the use of a ...
*
Medical image computing Medical image computing (MIC) is an interdisciplinary field at the intersection of computer science, information engineering, electrical engineering, physics, mathematics and medicine. This field develops computational and mathematical methods fo ...
* Michal Rosen-Zvi *
Speech recognition software in healthcare Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers with the m ...
*
The MICCAI Society The MICCAI Society is a professional organization for scientists in the areas of Medical Image Computing and Computer Assisted Interventions. Due to the multidisciplinary nature of these fields, the society brings together researchers from several ...


References


Further reading

* * * * * * * * * * {{Health care
Healthcare Health care or healthcare is the improvement of health via the prevention, diagnosis, treatment, amelioration or cure of disease, illness, injury, and other physical and mental impairments in people. Health care is delivered by health profe ...
Health software Medical devices