Artificial Intelligence (AI) in healthcare has been around for over four decades. In the beginning, systems had limitations requiring extensive programming and exhibited little to no self-learning behavior. Over the past two decades, on the back of technological advances, AI and its sub-domains such as machine learning (ML), data analytics, natural language processing, and image processing have significantly evolved. Many deep learning algorithms/models, in conjunction with large volumes and high-quality clinical data, have started assisting in improved diagnosis and prognosis of diseases in ways we have not experienced before. Today, AI is disrupting the healthcare segment with low-cost improved care. The following are just a few use cases of artificial intelligence in the healthcare space.
Early detection of Alzheimer's disease
Alzheimer's disease (AD) is a chronic neurodegenerative disease and irreversible disorder. One in 10 people aged 65 and older suffer from AD. So far, there is no effective cure available to us. Early detection is of prime importance for timely treatment and slowing the progression. A study published in Nature highlights how the new ML model correlates between real-time patient data and the ADNI’s (Alzheimer's Disease Neuroimaging Initiative) real-world dataset. The dataset includes medical history, demographics, symptoms, cognitive scores, neuropathology vital signs, neuropsychological tests, and lab tests, etc. The model provides physicians with accurate decisions along with a set of explanations for every decision.
Evaluating the need for surgery
Some patients say they underwent surgery which perhaps was not required. AI together with predictive analytics has started helping in understanding the severity, progression, and associated risks in diseases that require surgery. The analysis will assist surgeons in evaluating the need for the surgery or exploring low-risk alternatives. In future, AI will be able to identify high-risk patients, assist surgeons in reducing the rate of surgical removal of a tumor or amputation or lower extremity angiography or some other surgical condition.
AI-assisted robotic surgery
A 2017 report from Accenture listed robotic surgery as the AI application in healthcare with the highest near-term value, estimating this value at 40 billion dollars. Compared to conventional surgery, robotic-assisted surgery offers superior visualization of the operating area, stabilization of instruments, mechanical advantages, and improved ergonomics for the surgeons. Surgeries require extreme precision and patience - robotic surgery would help surgeons achieve the most minute movement level precision. Robotic surgery is already changing the surgical field, and it only stands to evolve more with time. However, robotic surgery has a steep learning curve that would have to be managed by surgeons.
It takes years for a pharmaceutical company to discover a drug or a vaccine. Many machine learning methods are being used in the drug discovery process, such as support vector machines and random forest approaches. However, with recent advances in the AI field, deep learning is being applied to predict the properties of new compounds. Results show that deep learning outperforms machine learning technique random forest. By training the ML algorithm on existing chemical compounds, using a recurrent neural network (RNN), the AI model can produce new chemical compounds within the domain of the training data. In the era of big data analysis, AI & ML algorithms will result in faster, cheaper, and more effective drug discovery either from known compounds or from completely new ones.
Health monitoring and tracking
Most patients are careless about monitoring and keeping track of their health conditions, even in situations of high importance. The COVID-19 pandemic has worsened this behavior. However, the usage of AI-powered wearable devices can help patients keep track of their health conditions. These devices capture real-time health data and provide suggestions on required medication, activities, exercises, etc., that could assist them in effective health management. It is also possible to share this health data in real time with the physician, who could guide the patient in time without the patient visiting the physician.
Due to challenges experienced in patient recruitment, age-grouping, engagement, and retention, many new drugs fail during clinical trials. AI and ML can help improve all these factors and reduce trial costs by processing electronic medical and administrative records, as well as data collected from other sources, including wearable devices, sensors, and apps. Analysis helps reduces population homogeneity. Even with fewer patients, we can generate study data of statistical importance. Data collected through wearable devices is more reliable because it captures the effect of a drug on disease symptoms with other important vital parameters in real time. This data is detailed and more relevant than the data collected in the clinic. It helps reduce patient visits to the clinic, patient drop-outs from the program, and the cost of clinical trials.
Image analysis for medical diagnostics
There is always a possibility that even a well-trained and experienced physician may miss something or other in medical diagnostics. Now physicians can leverage AI-powered image analysis tools (SaMDs) to analyze medical images such as MRIs, X-rays, and CT scans. It is helping in the early detection of neurodegenerative disorders.
Healthcare industry experts have started to appreciate the power of AI and ML combined with big data. With the advancement in technology, AI and ML algorithms will mature and help in making early and accurate diagnoses of diseases, reducing the cost of treatment and care, facilitating robotic and remote surgeries, providing personalized dosage, containing epidemics and pandemics, reducing the costs of treatment, and much more.
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