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The market value of AI in the health care industry is predicted to reach $6.6 billion by 2021. Artificial intelligence is increasingly growing in popularity throughout various industries. Most of us associate AI with things like robots, Alexa and self-driving cars. But AI is a lot more than that. AI experts see it as a revolutionary technology that could benefit many industries. The impact of AI in the health care sector is genuinely life-changing. It is driving innovations in clinical operations, drug development, surgery and data management…….…Continue reading….
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Source: Forbes
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Accurate and early diagnosis of diseases is still a challenge in healthcare. Recognising medical conditions and their symptoms is a complex problem. AI can assist clinicians with its data processing capabilities to save time and improve accuracy. Through the use of machine learning, artificial intelligence can be able to substantially aid doctors in patient diagnosis through the analyis of mass electronic health records (EHRs).
AI can help early prediction, for example, of Alzheimer’s disease and dementias, by looking through large numbers of similar cases and possible treatments. Doctors’ decision making could also be supported by AI in urgent situations, for example in the emergency department. Here AI algorithms can help prioritise more serious cases and reduce waiting time. Decision support systems augmented with AI can offer real-time suggestions and faster data interpretation to aid the decisions made by healthcare professionals.
In 2023 a study reported higher satisfaction rates with ChatGPT-generated responses compared with those from physicians for medical questions posted on Reddit’s r/AskDocs. Evaluators preferred ChatGPT’s responses to physician responses in 78.6% of 585 evaluations, noting better quality and empathy. The authors noted that these were isolated questions, not in the context of an established patient-physician relationship.
Recent developments in statistical physics, machine learning, and inference algorithms are also being explored for their potential in improving medical diagnostic approaches. 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 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.[21] 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.
Improvements in natural language processing 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 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, 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 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.
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.
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. 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. A key limitation in early studies evaluating AI were omissions of data comparing algorithmic performance to humans.
Examples of studies which assess AI performance relative to physicians includes how AI is noninferior to humans in interpretation of cardiac echocardiograms and that AI can diagnose heart attack better than human physicians in the emergency setting, reducing both low-value testing and missed diagnoses. In cardiovascular tissue engineering and organoid studies, AI is increasingly used to analyze microscopy images, and integrate electrophysiological read outs.
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