Revolutionizing Healthcare using Artificial Intelligence

Treatment and prevention of rare and dangerous diseases often depends on detecting the symptoms at the right time. In many cases, early diagnosis can result in complete cure. Conversely, a late or wrong diagnosis can have damaging or potentially fatal results. Human skills and experience are limited and hard-to-earn when it comes to examining images and samples and making reliable decisions.

Developing pharmaceuticals through clinical trials can take more than a decade and cost billions of dollars. Making this process faster and cheaper could change the world.

The adoption of Artificial intelligence(AI) is on the rise and solving a variety of problems for patients and healthcare industry.  The primary aim of health-related AI applications is to analyze relationships between prevention or treatment techniques and patient outcomes. All that we want to do is, give data to the computer, and let the computer learn on its own.

Artificial Intelligence(AI)-How it Works?

AI robot or computer gathers facts about a situation through sensors or human input. The computer compares this information to stored data and decides what the information signifies. The computer runs through various possible actions and predicts which action will be most successful based on the collected information. Of course, the computer can only solve problems it’s programmed to solve, it doesn’t have any generalized analytical ability.

 Machine Learning

Machine Learning is a subset of Artificial Intelligence. If Artificial Intelligence aims to make computers smart. Machine Learning takes the stance that we should give data to the computer, and let the computer learn on its own.

The internet came with a huge amount of digital information being generated, stored, and made available for analysis. Machine Learning algorithms have been the most effective at leveraging all of this Big Data.

AI programs have been developed and applied to practices such as,

Managing Medical Records-Data management is the most widely used application of artificial intelligence and digital automation. Robots collect, store, re-format, and trace data to provide faster, more consistent access.

Early and precise diagnosis-AI algorithms can quickly ingest millions of samples in short order and glean useful patterns

Treatment protocol development-Artificial Intelligence can find patterns in the treatment process to guide doctors in better caring for patients. By looking into the outcome and data from past treatments, machine learning algorithms can provide success guidelines and recommendations that can increase survival rates.

Drug developmentThere are situations where the available medicines can be redesigned to treat any new diseases and to find those combinations through clinical trials would be time consuming. AI devices uses patterns, on the existing medicine designs, to find appropriate medicine.

Personalized medicineAI mobile apps use image analysis to control patient adherence to prescriptions. This includes making sure patients take their medication on time and perform other tasks ordained by their doctor.

Patient monitoring and care-AI algorithms and assistants can also help provide continuous and self-care to patients who need constant attention. This is especially useful in mental healthcare, where constant monitoring and analysis of patient mood can help doctors devise more successful strategies that are specific to patients.

Precision Medicine-Genetics and genomics look for mutations and links to disease from the information in DNA. With the help of AI, body scans can spot cancer and vascular diseases early and predict the health issues people might face based on their genetics

Healthcare-related applications of AI include:

  • Improvements in computing power resulting in faster data collection and data processing
  • Increased volume and availability of health-related data from personal and healthcare-related devices
  • Growth of genomic sequencing databases
  • Widespread implementation of electronic health record systems
  • Improvements in natural language processing and computer vision, enabling machines to replicate human perceptual processes
  • Enhanced the precision of robot-assisted surgery

Implementing artificial intelligence in clinical settings

1.Remote patient monitoring

Implementing AI in remote patient monitoring (RPM) systems is the first step. When medical-grade wearable devices can provide real-time, clinically accurate data, they have the potential to improve health outcomes through coaching and feedback.

2. Connectivity

Ubiquitous connectivity is a tacit necessity when implementing internet-of-things solutions, like AI enhanced remote patient monitoring devices. Devices must be connected to transmit data constantly to the cloud for integration, aggregation, and analytics.

3. Training AI

Once connectivity is achieved, AI could help improve health outcomes through its ability to expediently aggregate, cross-reference, and learn from data to determine its correlation to patient objectives. To improve health outcomes, AI’s advanced machine learning algorithms require significant training periods to analyze large datasets specific to chronic medical conditions.

4.Comprehensive business model

For AI to be implemented in clinical settings a business model that can align the economic realities of the patient, payer, and provider should be adopted.

Huge Demand for AI in Healthcare Market

  1. Identifying what is the exact diagnosis with patient symptoms is a huge challenge for the health care providers.
  2. Lack of proper medical knowledge might lead to wrong treatment plan.
  3. As experienced care providers are limited in the Health care industry.
  4. As Hospitals are involved in marketing their best treatment and cure results.
  5. Millions of dollars are wasted on clinical trials for drug development.



Prathiba Kumaravelu

Sr. Software Engineer

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About Incarnus

We are a global provider of next-generation, cloud-enabled solutions for all levels of healthcare from primary health practice to large-scale hospital networks.
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