• In clinical assessment
• In healthcare chatbot
• In healthcare marketing
• In the care plan assessment
• In radiology images assessment
• In-patient admission prediction
• For real-time alerting
• For enhancing patient engagement
• To identify cancer in the early stage
• In healthcare cyber security
• To assess care requirements
• To manage staff smartly based on demands
• In supply chain management
• In identifying the most workable medicine
• In clinical trials
• In clinical trial recruitment
• To predict care costs

• Extract clinical and other data from primary and secondary sources using automated methods
• Build datasets
• Remove duplicate or malicious data from datasets
• Filter out the data using automated methods
• Apply AI and machine learning algorithms to find patterns from data
• Build programs or models using R Language or Python
• Train those programs or models on machine learning algorithms
• Capture or store rules or patterns in an understandable or graphical format

• Predicting patient inflows
• More accurate clinical assessment
• A very high cost saving
• Productive staff
• High patient engagement
• Easy risks mitigation
• High cyber security standards
• Personalized care delivery
• Effective marketing campaigns
• Enhanced patient experience
• Better asset management
• More revenue
• Early illness detection
• Higher patient safety standards
• Fast clinical decision

AI: AI is neither a programming language nor a technology. It is just the ability of a computer or any digital system to first understand, then plan and react to certain situations by itself. In other words, it enables manmade digital systems to make decisions without human intelligence.

Machine learning: It is a subset of AI. AI uses machine learning algorithms to make decisions by itself. Using machine learning algorithms and datasets, we can train any program to understand and react to any situation.

Deep learning: Deep learning is an advanced version of machine learning. In the case of machine learning, we have to manually define values such as skin colour, skin spots, redness, and spot diameters if we are a training model to identify skin cancer through images. But in the case of deep learning, it defines these values.

Problem statement: Build a program that detects the diseases with early-stage symptoms that doctors feed in EHR.

Solution: We will use a genetic algorithm (machine learning algorithm), a rule engine (computer program), and a historical dataset of diseases and their associated symptoms.

We run the genetic algorithm on the dataset.

Inspired by Charles Darwin’s theory of natural evolution, the algorithm finds and selects the fittest values (accurate values) and produces offspring of the next generation.

It continues executing the loop several times (generations) and what it produces last is the fittest or most accurate values.

With these fittest values, the rule engine makes rules in the form of If-Then.

For example, if fever > 102, runny nose = yes, weakness = yes, loss of appetite = yes, then it is COVID-19 and not flu.

Rule engine makes multiple such rules and it keeps making it with new data it gets.

Now another program compares the real-time symptom data of a patient (added by doctors in EHR) with these rules and based on matching percentages, it predicts the disease.

So, this is how, by using historical data, we make rules and by comparing rules with real-time data, we can find diseases with early symptoms of patients.

Yes, we cannot use this as a conclusion but it surely helps doctors in clinical decision-making, especially when the symptoms indicate the probability of complex neurological or cancer diseases.