Hyderabad: To understand the patient’s disease, the doctor usually analyzes several physical indicators as well as the reports generated after the diagnostic tests.
The advent of artificial intelligence (AI) is set to change practices that have prevailed for decades. AI can correlate patient symptoms and diagnostic results using health records in a database to identify the cause of illness in seconds – helping doctors save time and increasing hospital operational efficiency.
According to a detailed report published in August by the Indian School of Business (ISB), the real transformation of AI in healthcare is in patient care and clinical research.
In medical imaging, it speeds up diagnosis, while in surgeries, AI-powered robotics increases accuracy. During the pandemic, AI played an important role in tracking outbreaks and assisting in treatment planning. Initiatives such as Wadhwani AI’s media scanning solution for disease surveillance and Artellus’ image recognition for early diagnosis highlight the versatility of the technology.
In rural India the doctor to patient ratio is extremely low, with one doctor treating 2,000 people. The advent of AI is a morale-booster for doctors. The central government is reportedly keen to promote AI applications in the healthcare sector. Many state governments are investing heavily in AI rollout in the healthcare sector.
An advanced healthcare AI can help government or private hospitals roll out telemedicine to make healthcare more accessible, especially in remote areas.
AI can also revolutionize preventive healthcare as it can identify patterns quickly, which will help doctors start treatment sooner.
In 2022, the Indian healthcare industry was estimated at $372 billion. It is expected to grow at a rate of about 20 percent by the year 2030. The use of AI will boost the growth rate.
But how does AI work and how can AI be used for healthcare?
AI uses various advanced techniques such as machine learning, natural language processing (NLP), expert systems, and image and signal processing for diagnosis.
Machine learning automates data analysis to identify patterns, while NLP helps process medical records and transcriptions. Expert systems, on the other hand, specialize in solving specific problems, such as diagnosing chronic conditions. Image and signal processing refines medical imaging data, allowing for accurate and timely diagnosis.
NITI Aayog has used AI for early detection of diabetes and eye related diseases. Radiology was one of the first fields to adopt AI.
Startups like Doji, which developed India’s first AI-powered remote patient monitoring system, and Agnito, a speech recognition tool for medical transcription, are setting benchmarks in healthcare innovation.
IIIT Hyderabad is also using AI to detect sleep quality and sleep-related disorders.
AI can study sleep states at a precise level, Deep Learning (DL) can automate sleep state data through supervised and unsupervised learning models, Professor S Bapi Raju, principal researcher and Head of Cognitive Science Lab, IIIT Hyderabad said.
AI can be a boon for remote ICU support, says Dr DVR Seshadri, director of ISB’s Center for Business Markets.
“Once technologies such as remote ICU assistance (cloud physician), breast cancer detection (healing) or scanning through millions of X-rays to detect tuberculosis, many companies will inevitably spring to use these technologies on a wider scale,” He said.
However, widespread adoption of AI in healthcare is not without challenges.
Lack of a comprehensive electronic health record (EHR) and high infrastructure costs are barriers. There is also a significant skills gap as skilled professionals in both healthcare and AI are scarce. Additionally, concerns about data privacy, accountability, and ethical use persist.
A more widespread concern is the possibility of AI replacing doctors.
However, Dr Seshadri allayed these concerns: “This is highly unlikely in the near future. At the end of the day, the patient wants the reassurance of a human doctor. While AI tools can be used to increase the effectiveness of doctors, it cannot replace doctors anytime soon.”
“Many disciplines are converging on a large scale. It can be difficult to separate the use of AI in disease surveillance from its other applications. Regulatory systems generally lag behind the use of these technologies to prevent such convergence, and so we. Can’t be denied for the purpose,” explained Dr Seshadri.
AI Applications in Healthcare:
AI uses various advanced techniques such as machine learning (ML), natural language processing (NLP), expert systems, and image and signal processing for diagnosis.
Machine learning automates data analysis to identify patterns, while NLP helps process medical records and transcriptions.
Expert systems, on the other hand, specialize in solving specific problems, such as diagnosing chronic conditions. Image and signal processing refines medical imaging data, allowing for accurate and timely diagnosis.