During this pandemic we have seen how vulnerable our health care systems are: imprecise alert responses, insufficient medical supplies, overloaded medical staff, not enough hospital beds and delayed treatment. Artificial Intelligence can offer a number of advantages over traditional clinical systems and decision-making techniques. This can be done using deep learning and various machine learning algorithms, which provide more precision and allow humans to gain unprecedented insights into diagnostics, care processes, treatment variability and patient outcomes.

It is anticipated that there will be significant growth in artificial intelligence healthcare markets in areas like hospital workflow, medical imaging and diagnosis, therapy planning, virtual assistants and drug discovery.

Drug Discovery
The goal of drug discovery is to identify medicines that can help prevent or treat a particular disease. These medicines are chemically synthesized molecules that can specifically bind to a target molecule involved in a disease. Researchers carry out a large number of screening of molecules to identify the one with potential to become a promising compound and put it through rounds of tests to develop it as an effective drug. This process can be expensive and time consuming. Even when a new drug shows potential in laboratory testing, it may fail when moved into clinical trials. In fact, less than 10% of drug candidates make it to the market after Phase 1 trials. Analytics and statistical models can be used to reduce trial and error in drug discovery. AI could reduce at least a year of development of many drugs, which would be worth billions.

Disease Diagnosis
Artificial Intelligence can be helpful in early diagnosis of various chronic diseases like cancer, heart diseases, Parkinson, Alzheimer’s and diabetes. For example, Diabetic Retinopathy which is a complication of diabetes, is one of the reasons many people lose their vision.  It is difficult to detect in early stages. Diabetic retinopathy does not have any visual symptoms, but can be detected in retinal screenings. Deep learning algorithms like neural networks can be trained to recognize patterns from retinal screening. Similarly, we can understand CT scans, MRIs, pathology slides and colonoscopies using computer vision to diagnose diseases. For example PathAI and Buoy Health.

Treatment
Data from large patient populations and a variety of sources like DNA and RNA sequencing, proteomics, metabolomics and epigenetics can be compared to historic groups of those who have been diagnosed and treated for diseases. Predictive models such as deep learning can discover complex patterns in data and be helpful in determining patient risk and optimal treatment. In diseases like cancer, AI can be used to understand the underlying mechanism of the disease.

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Current Applications
Following are the ways in which AI is changing healthcare:

Robot-assisted surgery process
Robot-assisted surgery is considered minimally invasive, hence patients can recover faster. A study involving orthopedic patients showed that robot-assisted techniques can reduce complications by five times as compared to when surgeons operated alone.  It could reduce hospital stay by 21% and create $40 billion in annual savings.

Dosage Error Reduction
Dosage errors makeup 37% of preventable medical errors and generate $16 billion in savings. AI can help in determining the correct dosage of a medicine to a patient.

Virtual Nurses
AI-powered nurses could save $20 billion annually and 20% of the time nurses spend on patient maintenance tasks. Virtual nurses can interact with patients, ask them questions about their health, assess their symptoms and direct them with appropriate care settings. An example is Sensely’s virtual nurse “Molly”.

Fraud Detection
Errors and fraud are an expensive problem for healthcare organisations and insurers. Neural Networks and Data Mining can be used to find anomalies and patterns associated with medical reimbursement fraud. This could improve speed and accuracy of Medicare claims and save $17 billion in annual savings.

Automated Image Diagnosis
Image analysis is a time consuming task for humans. An MIT-led research team has developed a machine-learning algorithm that can analyse 3D scans in real-time. AI image analysis could support remote areas where healthcare providers are not available.

Workflow and Administrative Tasks
Automating administrative tasks allow easier integration with existing technologies. This maximizes the time given by nurses and doctors to their jobs and more crucial tasks.

Treatment Design
A partnership between Cleveland Clinic and IBM, uses IBM’s Watson to analyse thousands of medical papers using Natural Language Processing to inform physicians of treatment plans. This can provide patients with a more efficient and personalized treatment experience.

Connected Machines
Medical institutions function on a variety of connected machines which are able to transfer information from one another. AI helps make this process more intuitive. It makes consolidating data for further analysis easier.

References:
https://www.sciencedirect.com/science/article/abs/pii/S0167629616000291?vi%20a%3Dihub
https://www.sciencedirect.com/science/article/abs/pii/S1529943017308513

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