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A New Era for the Healthcare Industry Where Data Science Improves Lives

It is a known fact that the biggest transformative technologies for this decade would be Data Science and Artificial Intelligence. They are targeted to transform the working patterns, lifestyles, global productivity to create huge revenues. Gartner has stated that the global AI-based economic activity is set to increase from about $1.2 trillion in 2018 to about $3.9 Trillion by 2022. By 2030, Mckinsey has predicted the global economic-activity in healthcare to reach $13 Trillion.

The adaptability to data science that we see in the current era is powered by Machine Learning (ML) technologies such as Deep Convolutional Networks, Feedforward Neural Network, Radial Basis Function Neural Network, Recurrent Neural Network (RNN), Deep Reinforcement Learning (DRL), etc. Healthcare is one of the significant fields that is regarded as eminently suitable for AI applications.

Artificial Intelligence in Radiology and Pathology

Deep Learning algorithms in healthcare are used in data sets for electronically stored medical imaging data. Machine learning in radiology help in identifying patterns and anomalies. The machines can detect suspicious spots such as skin cancers, lesions, tumours, and brain bleeds in a patient just like a highly trained radiologist using Deep Learning algorithms. This results in better time efficiency for radiologists easing the pressure from handling the huge deluge of digital medical data. The image processing technology using AI services works successfully to a greater extent with 3D radiological images assisting us in precise surgery planning, navigation, and efficient tumour-contouring for radiotherapy planning. MRI and other advanced imaging systems are used for early cancer detection with ML algorithms. These advances in AI aids healthcare with speed, efficacy and accuracy.

Physical Robots for Surgery Assistance

Surgical robots assist human surgeons by enhancing the ability to see and navigate in a procedure, creating precise and minimal incisions, leading to less pain with optimal stitch and wound. The applications of AI and ML in digital surgery robots bring in infinite possibilities in healthcare such as:

  • A software-centric approach of robot’s aids with the massive distributed processing.

  • Data-driven insights and guidance are performed based on the history of surgeries a patient had undergone (executed by both machines and humans) and their dynamic outcomes

  • AI-generated virtual reality for real-time guidance

  • Telemedicine’s possibility and remote surgery for simple procedures.

Robots and AI have created the 21st century surgeon which holds better skills and improve patient outcomes.

Drug discovery

The pharma industry greatly benefits with AI and ML algorithms implemented in drug discovery. It involves all kinds of therapeutic domains such as metabolic diseases, cancer treatments, immuno-oncology drugs, etc. AI techniques accelerate the discovery of drugs by acting as a catalyst. AI algorithms in drug discovery are used to analyse huge volumes of biological data from patients and differentiate the diseased and healthy cells to identify the cancer mechanisms. AI systems analyse the multi-channel data such as research papers, patents, clinical trials, and patient records using the Bayesian inference, Markov chain models, reinforcement learning, and natural language processing (NLP). The key goal is to find patterns and construct high-dimensional representations that are stored in the cloud for the drug-discovery process. The use of machine learning in preliminary drug discovery has various purposes from initial evaluation of drug compounds to predicting success rate based on biological factors.

Disease Identification and Diagnosis

The most prevalent application in Healthcare industry is disease identification and diagnosing, also known as classification using Machine Learning algorithms. This supervised learning approach determines whether a patient has a specific disease based on their features describing their symptoms. The features can be represented in the form of medical images, text, data, or even signals. In a few cases, the objective of Machine learning is to make a diagnosis between two classes; however, they also diagnose when there are multiple classes. The current research projects include dosage trials for intravenous tumour treatment and detection and management of cancer using AI in disease diagnosis.

Smart Electronic Health Records

Document classification helps in sorting patient queries via email using support vector machines. The optical character recognition transforms sketched handwriting into digitized characters. These are essential ML-based technologies to advance the collection and digitization of electronic health information. MATLAB’s ML handwriting recognition technologies and Google’s Cloud Vision API for optical character recognition are just two innovative applications in this area.

Pandemic Prediction

The pandemic outbreaks are predicted around the world using ML and AI technologies by collecting data from satellites, historical information, real-time social media updates, and other relevant sources. The support vector machines and artificial neural networks have been used previously to predict malaria outbreaks, by considering data such as temperature, average rainfall per month, number of positive cases, and other data points.

Predicting outbreak severity specifically impacts third-world countries, which often lack medical infrastructure, educational avenues, and access to treatments. AI and ML technologies work its way for efficient results.

AI in healthcare is never a dull moment. The advances made every day using data science benefits billions of patients, frontline workers, doctors, surgeons and many more to improve their lives with a regular check on their basic health and well-being.

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