Call
+918447748545

Published on: Jun 20, 2020
4 minute read
By: Dr Rajan Choudhary

Artificial Intelligence In Healthcare

Artificial intelligence. This phrase means different things to different people. To some, it conjures ideas of robots having the same intelligence and creativity as humans, able to do any tasks we instruct them, except better than us. To others it is a new and exciting tool, one that could revolutionise the way we work, but also the way labour is distributed in society. And for developers? They dread being asked to make an artificial intelligence system by people who have only heard buzzwords such as “machine learning” and “deep neural network” in headlines and blogs.

In this blog we will look at the basics of AI terminology, so we can understand what these terms really mean, and whether they will have an impact on healthcare.

WHAT IS ARTIFICIAL INTELLIGENCE?

Even this question is difficult to answer, as it enters the realms of philosophy and discussion over the meaning of intelligence. What makes a person intelligent? Is it their retained knowledge? Because a computer can store the entirety of known human knowledge on a disc. Is it understanding and following instructions? Or is it creativity, a skill even the average person may struggle with at times. We know one thing for sure, distilling a person’s intelligence down to a single IQ number is disingenuous and doesn’t represent true intelligence.

Similarly people define AI in different ways. A broad definition looks at the ability for a computer or programme to be able to respond autonomously to commands, to the changing environment around them, recognise audio or visual cues, process the information without strict defined rules and spit out a desired function.

The key features appear to be autonomy: the ability to function independent of a human controller or guide, and adaptability: the ability to work beyond strict rules and criteria, and function in situations or with inputs beyond their original programming.

In medicine, a “dumb” system could work with physical values, for instance blood results, compare them to a “normal range” and determine if results are abnormal (e.g if the patient has anaemia).

A smart “AI” would be able to look at a CT scan, notice subtle changes in the images, compare it against what a normal scan should look like, and identify the pathology. This is very difficult because normal scans can differ noticeably between patients, (for instance due to anatomical differences between people), and disease findings can be even more varied, unusual, abnormal. Human brains have incredibly complex pattern recognition systems – over a third of the human brain is dedicated to just visual processing. Imagine trying to re-create that in code.

At first people tried to emulate this with fixed programming. For instance, to teach a programme to recognise a bicycle, you would need to teach it to first exclude anything that is not a vehicle, then exclude anything that does not have wheels, has more than 2 wheels, has a frame connecting the two wheels, has a chain connecting the pedals and the rear wheels……and so on. All of this for a bike. Now imagine trying to code it to recognise subtle changes to cells under a microscope, to recognise cancer cells, to recognise an abnormal mass on a scan. Clearly this solution is very clunky, and simply not feasible.

MACHINE LEARNING

Modern AI systems have moved towards “machine learning”. This is a statistical technique that fits learnt models to inputted data, and “learns” by training models with known data sets. Instead of a person defining what a bicycle is, the model is flooded with thousands of pictures of bikes, and the programme forms its own rules to identify a bike. If this model is then shown a picture of a bike it will show the statistical likelihood of the picture being a bike. The system could be expanded by further training the model with pictures of motorbikes, scooters and other two wheeled forms of transport. Now if given a picture, the model can determine what type of two wheel transport it has been shown.

The healthcare application can be simple – lets look at a radiology example. Teach an AI model what normal lungs look like, then show it images of various pathologies such as pneumonia, fibrosis or even lung cancer. If fed enough images and variations of a type of disease, the AI’s statistical analysis might even find associations and patterns to identify a disease that a human radiologist would be unable to find.

NEURAL NETWORKS

A more complex form of machine learning is the neural network. Its name suggests it is analogous to the neurons in a human brain, though this analogy does not stretch much further. Neural networks split the image into various different components, analyse these components to see if it has variables and features before spitting out a decision.

The most complex forms of machine learning involve deep learning. These models utilise thousands of hidden features and has several layers of decision making and analysis before a decision is made. As computing power increases, the ability to create ever more complex models that can look at more complex 3 dimensional images full of dense information. These deep learning models have been able to identify cancer diagnoses in CT and MRI scans, diagnoses that have been missed by even the most expert consultants. They can also identify structures and patterns the human eyes cannot, and may end up being better at diagnoses than a highly trained specialist. Of course such diagnoses would still have to be checked by a doctor, as due to the medico-legal implications that could occur from incorrect diagnoses created by a computer utilising models even their programmers cannot understand.

NATURAL LANGUAGE PROCESSING

But the application of AI is not limited to identifying images and scans. One of the greatest hurdles a computer faces is trying to understand human speech. Dictation from speech to text is easy, but understanding the meaning of what was said, and trying to use that to create instructions or datasets, that’s hard. This is why the iPhone’s Siri or Google Assistant on Android phones seem so limited. They can only recognise certain set instructions such as “What is the weather” or “Set an alarm for…”. More complicated instructions or requests usually results in an error.

People don’t speak in simple sentences. If asked about their symptoms, every patient will use different sentence structures, adjectives, prioritise different symptoms depending on how it affects them, and create a narrative rather than a list of symptoms. Similarly when writing in patients notes, doctors will also use complex sentences, short-hands, structure their notes differently. Feeding this information to Siri would not output a clear diagnosis, but rather give the poor digital assistant a migraine.

Deep learning is being used to analyse natural speech to pick out the important information that will lead to a diagnosis, similar to how a medical student is trained when taking a history. If deployed successfully this would be invaluable in triaging patients based off the severity of their symptoms, and assigning them to the right specialists.

It would also have huge implications for research. Identifying data is very labour and time intensive, and the costs of trawling through patient notes can significantly limit the feasibility of research studies. A deep learning AI system could read through the notes, identify all the important symptoms, how a patient is improving on a day to day basis and other subtle parameters, and do so without human supervision through thousands of cases without boredom or fatigue. The wealth of information available could significantly improve the quality of research performed.

Artificial Intelligence and the various buzzwords can be difficult to break down and digest. And certainly this blog will not answer all of your questions, and may leave you with more questions than you started with. But understanding the basics of AI will help in appreciating the effort that goes into creating these systems, and also acknowledge the hurdles that limit AI from becoming prevalent across healthcare.

At least for now. Progress in this field is constant. By next year the AI landscape may be very different.

Dr Rajan Choudhary

HEAD OF PRODUCTS, SECOND MEDIC INC UK

Read Blog

Digital Healthcare Automation India: Enabling Smart Workflows, Faster Care, and a Modern Clinical Ecosystem

Digital healthcare automation is redefining how India delivers medical services, manages clinical operations, and coordinates patient journeys. As hospitals, clinics, and digital health platforms move toward technology-driven processes, automation has become essential for ensuring efficiency, reducing manual work, minimizing delays, and improving care accuracy. In a healthcare system where patient volumes are high and specialist availability is uneven, automation empowers organizations to deliver faster, smarter, and more consistent care.

India’s healthcare automation growth aligns with national initiatives like ABDM (Ayushman Bharat Digital Mission), growing telemedicine adoption, rising digital literacy, and the increased use of AI-based medical tools. SecondMedic integrates automation into every stage of digital care-appointments, reporting, monitoring, follow-ups, and preventive health-allowing users and clinicians to experience a seamless, intelligent healthcare ecosystem.

Digital healthcare automation India is not simply about digitizing manual tasks; it is about augmenting healthcare with intelligent workflows that respond to real-time needs. By reducing repetitive workloads, automation allows clinicians to focus on what matters most: patient care.

Why Automation Matters in Indian Healthcare

India faces significant challenges: overloaded outpatient departments, resource shortages, manual data entry errors, delayed reports, and administrative inefficiencies. Automation addresses these issues by introducing structured, rule-based processes supported by AI and digital tools.

Key systemic challenges automation helps solve:

High patient-to-doctor ratios
Slow movement of information across departments
Inconsistent follow-up and monitoring
Manual errors in documentation and reporting
Unpredictable appointment flow
Inadequate time for patient–doctor interaction

Digital automation supports a more organized, reliable, and high-performance healthcare environment.

What Is Digital Healthcare Automation?

Digital healthcare automation refers to the use of AI, software systems, connected devices, and workflow engines to automate medical and administrative procedures. These tools reduce manual intervention wherever possible and ensure accuracy, repeatability, and continuity.

Core areas of automation include:

Appointment management and scheduling
Electronic medical record updates
Auto-generation of diagnostic summaries
Automated clinical reminders
Medication and health-plan notifications
Remote monitoring and alert systems
Digital report formatting
Workflow optimization for hospital operations

SecondMedic incorporates automation across its telemedicine, diagnostics, monitoring, and preventive-care systems.

Automated Appointment Scheduling and Coordination

Appointment automation is one of the most practical innovations in India’s digital health landscape. Without automation, patients often encounter long queues, missed follow-ups, and scheduling conflicts.

Automated scheduling helps by:

Matching patients to the right doctor
Reducing wait times
Preventing double bookings
Prioritizing urgent cases
Coordinating virtual and in-person consults
Helping doctors manage daily workloads efficiently

SecondMedic’s automated scheduling engine analyzes doctor availability, user urgency, and specialty requirements to optimize appointment flow.

Automation in Diagnostics and Reporting

Medical diagnostics often involve multiple steps that traditionally require human intervention-uploading reports, comparing past results, formatting summaries, highlighting abnormalities, and generating clear interpretations.

Automation enhances diagnostic workflows by:

Auto-organizing digital medical reports
Highlighting abnormal ranges
Identifying missing test values
Summarizing patient history for doctors
Formatting structured reports instantly
Automating comparisons with past results

For AI-based imaging and lab analytics, automation helps radiologists and clinicians detect patterns faster and reduce minor reporting inconsistencies.

Remote Monitoring and Automated Alerts

Remote patient monitoring has grown rapidly in India, especially for chronic diseases. Wearable devices and home-health tools generate continuous data streams. Automation helps turn these raw inputs into actionable insights.

Monitoring automation includes:

Auto-detection of abnormal vitals
Alerts for risky trends
Medication reminders
Follow-up triggers
Predictive alerts using AI
Aggregated health reports for doctors

For chronic care, this ensures timely intervention and reduces emergency visits.

Enhancing Hospital and Clinic Workflows

Healthcare automation in clinical facilities improves operational efficiency and reduces administrative bottlenecks. Hospitals benefit significantly from automated workflows that ensure consistency and speed.

Applications include:

Patient flow management
Automated admission and discharge processes
Digital billing and inventory management
Lab and pharmacy integration
Nursing task automation
Centralized communication dashboards

These improvements reduce patient wait times and improve overall care delivery.

Improving Patient Engagement Through Automation

Automation supports patients by making healthcare more accessible and predictable. Many individuals struggle to remember follow-ups or understand complex medical guidance. Automated systems simplify this journey.

Key patient-facing automation benefits include:

Reminders for medications and appointments
Preventive health notifications
Personalized care tips
AI-driven chat support for common queries
Post-consultation guidance delivery
Automated sharing of doctor notes and reports

SecondMedic uses automation to ensure patients remain engaged throughout their health journey.

Automation and AI: A Powerful Combination

AI enhances healthcare automation by making it adaptive and context-aware. Instead of following fixed rules, AI learns from patterns, outcomes, and user behavior to optimize workflows.

AI strengthens automation through:

Predictive recommendations
Dynamic scheduling adjustments
Automated report summaries
Early detection of errors
Smart escalation of high-risk cases

This combination powers advanced clinical systems that support both providers and patients.

Challenges in Implementing Healthcare Automation in India

Automation requires planning, infrastructure, and careful integration. Key challenges include:

Fragmented patient data across facilities
Infrastructure limitations in rural areas
Varying digital readiness across hospitals
Need for staff training
Ensuring compliance with DPDP and ABDM standards

Despite these challenges, adoption is increasing as digital health becomes mainstream.

The Future of Digital Healthcare Automation in India

India is poised for significant automation growth, driven by advancements in AI, 5G connectivity, cloud platforms, and interoperable health records. Over the next decade, digital healthcare automation will include:

AI-powered hospital command centers
Fully automated radiology and pathology workflows
Robotic process automation (RPA) in administrative processes
Automated care coordination for chronic diseases
Voice-based digital assistants for patient queries
Smart triage algorithms integrated across telemedicine networks
Predictive automation for emergency care

SecondMedic is building a modern digital ecosystem that integrates AI, automation, and predictive healthcare tools, creating a seamless and intelligent healthcare experience for users.

Conclusion

Digital healthcare automation India is unlocking a new era of efficiency, precision, and patient-centered care. By automating clinical workflows, diagnostic tasks, and patient engagement processes, healthcare organizations can deliver faster, more reliable services. Automation supports doctors with real-time insights, reduces administrative burdens, and ensures that patients receive timely interventions.

SecondMedic continues to lead this transformation by integrating automation into virtual care, diagnostics, monitoring, and preventive health solutions, shaping the future of digital healthcare in India.

To access advanced automated digital healthcare tools, visit www.secondmedic.com

References

NITI Aayog – Digital Health India
ABDM – National Digital Health Mission
IMARC – Healthcare Automation Market India
WHO – Digital Health Workflow Automation
FICCI – Hospital Automation India

Request A Callback

+91

By using this website/app, I confirm that I am at least 18 years old and consent to the processing of my personal data in accordance with the Terms of Service, Privacy Policy, and Declaration.