Application of AI and ML in Healthcare: Current Preparedness and Scope of Development

By: Ms Ashvini Danigond | Founder & CEO | Manorama Infosolutions Pvt Ltd

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The terms such as artificial intelligence (AI) and machine learning (ML) are more than buzzwords today. While often the two are used interchangeably, however, they are not the same. AI pertains to the simulation of human intelligence in machines making it possible for them to learn from experiences, adapt to new inputs and perform human-like tasks. On the other hand, ML is an application of AI that provides systems with the ability to automatically learn from experiences and improve themselves without any coding. AI and ML are on the cusp of introducing a significant breakthrough in the complex world of the healthcare system, while helping to make it high-powered and error-free. The key role for the introduction of AI and ML technologies in healthcare is to enhance the provider and patient experience.

Effective deployment of AI and ML tools in healthcare

AI when deployed can suitably help in capturing and storing significant amounts of data, this includes patient data collected on mobile health apps, and thus helping in analysis by healthcare providers.

It would be possible to reduce unnecessary personal visits to a doctor with the deployment of AI, which holds the potential to augment diagnoses. In fact, AI can help in identifying diseases based on facial features, retina scans, X-rays and speech as well. The accuracy rate in medical analysis can rise with the usage of AI.  For example, by leveraging patient data, AI is helping healthcare facilities identify patients in the early stages of sepsis. With the help of ML, custom dashboards to display risk scores and automatic alerts that notify caregivers of potential trouble, an AI-guided approach allows medical experts to get in front of the condition and even predict an adverse event.

Similarly, it is quite a task to diagnose diseases manually, ML plays a significant role in identifying the patient’s disease, monitor health, and suggest necessary steps to be initiated in order to prevent it. It can include anything from minor diseases to major ones, including cancer which could be hard to detect in the early stages.

In the field of discovering or manufacturing a new drug, the advancements in ML technology can lead to stimulating the process involving a number of compounds that are required to be put to the test with only one result proving to be useful in the end.

Medical practitioners are able to suggest proper diagnosis with the help of machine ML techniques such as deep learning by finding microscopic deformities in the scanned images within the patients. Earlier, techniques like X-ray and CT scan were enough to inspect minor irregularities, however, with the increasing strain of diseases, there had been a need to inspect them properly.

With the explosion of patient data in the form of genetic information and electronic health records (EMRs), doctors with the help of ML technologies are able to provide personalized treatment to individual patients as per their precise needs.

Health insurance fraud costs a lot to insurers and genuine policyholders alike.  The application of AI and ML in insurance platforms can help fraud detection and prevention, thus helping insurance companies in analyzing the signs of frauds before a claim is paid. This way it is able to improve the percentage of genuine transactions while reducing the costs associated with fraud claims investigations.

AI and ML tools are also aiding in combating physician burnout who have to take care of the clerical tasks, time constraints and inefficient work environments.

Further, ML can be an effective tool to improve hospital patient flow and alleviate capacity strain burdens. The goal of ML is not only to create predictive models, but to ultimately improve, and in some cases fix, the challenges that arise from poor hospital patient flow, including the overcrowding of health facilities.

Today, various AI/ ML tools are being successfully deployed to use in monitoring and predicting outbreaks such as Covid-19 across the world. AI and ML can be effectively used to set up command centres to support optimal decision-making in the case of a healthcare-crisis, while serving as a hub for monitoring performance, and to learn and launch healthcare programs effectively.

Scope of development of Al and ML tools in the future

In the Indian context, the AI and ML market so far has been classified as an emerging technology market. The scope for growth and development, however, remains vast. It is expected that the higher use of AI-based inventory and order planning will lead to smart and efficient procurement. Moreover, e-procurement among the government healthcare providers is also going to witness a surge with better adoption of online quotations. Going forward, AI and ML technology will play an important role in pre-diagnosis and validation in remote consultations.  A key challenge, however, would pertain to the development and usage of AI and ML tools in a way that is transparent and compatible with the larger public interest, while stimulating and driving new innovations in the healthcare sector building sustainability in the long run.

Ashvini Danigond

The author of this article, Ms Ashvini Danigond is the Founder & CEO of Manorama Infosolutions Pvt Ltd (MIPL)