Healthcare
Artificial intelligence is revolutionizing medicine. From automation of mundane tasks to the discovery of new drugs, the applications of AI in healthcare are limitless. The use of AI in manufacturing Covid-19 vaccines is a glowing example of its revolutionizing potential. However, this is just the beginning. The global AI in healthcare market size is expected to grow from $4.9 billion in 2020 to $45.2 billion by 2026. This growth is expected to impact multiple subfields of medicine including dermatology, radiology, disease diagnosis and drug interaction.
What AI Can Do?
- Cardiac Arrhythmia Detection
- Medical Image Analysis
- Medicine Side Effects Prediction
Cardiac arrhythmia is a global health problem and results in severe health conditions such as cardiac arrests. Significant number of lives can be saved by timely detection of cardiac arrhythmia. Currently, cardiac arrhythmia is detected by a careful reading of electrocardiogram (ECG) by a specifically trained doctor. ECG records often contain long cardiac activity samples (sometimes in order of days) as required for identifying and assessing arrhythmia. The analysis is performed by the aid of trained professionals, which makes this process time-consuming, laborious, subjective, and expensive. In contrast, a deep learning-driven solution enables the detection of cardiac arrhythmias from ECGs with very high accuracy. The technology is highly scalable and can become available at large-scale.
Radiological scans are an important part of the diagnostic process of several different diseases. Using advanced image processing methods, scalable software-based solutions are developed which leverage recent advances in computer solutions to analyze X-rays, CT scans and ultrasound scans for disease detection. These solutions can considerably reduce the workload of a radiologist and help improve overall detection rate. Like many other areas, AI is revolutionizing the field of medical imaging. Many classic signal and image processing techniques are being replaced by data-driven models, and many inverse problems related to imaging are being solved by super-fast AI algorithms.
A side effect is typically defined as an unfavorable secondary impact that happens in addition to the drug's or medication's intended therapeutic effect. The severity of side effects varies based on the person's condition, age, weight, gender, ethnicity, and overall health. The assessment of the side effects for a new drug is a time consuming, complicated and often invasive process. Unlike the conventional technologies which are mainly slow, machine learning is becoming a prominent and fast technology in this area, and several top research centers such as the Harvard medical school are currently investing on it. For example, the AI technology can be used to identify the proteins associated with adverse drug side effects.