AI and Healthcare: A timely partnership

November 9, 2021

Introduction

Today, almost every field is leveraging AI irrespective of the sectors or industries. Every day, AI is getting increasingly sophisticated at doing what we can do. They have the potential to do things at a low cost with high accuracy. AI has become a part of our everyday lives. Even the healthcare industry is evolving through this ever-green buzzword. According to reports and projections, the implementation market of AI in healthcare will expand from USD 6.9 billion in 2021 to USD 67.4 billion by 2027. The expected growth of CAGR will be 46.2%. In this article, you will go through the highlights and timely partnership of AI with healthcare.

AI in Healthcare

The healthcare industry is a ripe sector to clout and deploys endless technological opportunities. From medicine, diagnosis, chronic diseases, radiology to cancer, and risk assessment, every branch of healthcare can leverage AI. The pandemic has also promoted the healthcare sectors, hospitals, and pharmaceutical industries to extract benefits with the least time and cost. Let us now take a look at the beneficial aspect of AI in healthcare.

  • Aid in early detection and diagnostics: Different forms of illness require early diagnostics, or else they can cause adverse effects. Predicting the treatment prognosis by detecting the disease early and working upon it can save lives. Most of the modern prediction and detection is carried out by AI now. Healthcare researchers found that AI can more accurately predict diseases than doctors. It does not bring in any human error and thus makes them efficient in solving different prediction problems. MIT researchers have used AI systems to identify asymptomatic COVID-19 patients through coughs. That AI system has an accuracy of 98.5% to identify COVID-19 patients. Other medical areas like heart conditions, small tumors, early cancer stage, diabetes, etc., are also leveraging AI and Ai-based wearables for accurate predictions.
  • Extracting data from medical records: Most medical records, especially electronic health records (EHRs), are complicated medical records covering the patient's treatment plan and medical records. Scanning through such record systems is difficult. Also, extracting insights from these systems takes a lot of time and effort if performed manually. With the advent of artificial intelligence and machine learning, it is easy to extract data from patents records having complex medical histories. Also, when researchers or doctors want to look for visual charts of a particular disease, they can always use data science and machine learning tools that feed in such data.
  • Next-generation radiology tool development: MRI machines, CT scanners, and x-rays often render non-invasive visuals to the inner body parts. Many diagnostic processes rely on such tools. But due to non-invasive visuals, doctors face difficulty in diagnosis, leading to unsuitable treatment. Radiology tools embedded with artificial intelligence cater to accurate and detailed images of the human body at a microscopic level. Radiology tools also leverage ML models that can automatically analyse and predict the tumor type and recommend the surgery and medicine to doctors. Here artificial intelligence acts as an assistant to help doctors diagnose patients with minimum risk.
  • Artificial Intelligence in Medicine: AI and ML have vast contributions in the pharmaceutical and biomedical sectors. Developing drugs and precisely measuring the chemical composition is a spectacularly expensive task. The traditional mechanism of drug development is inefficient and costly. But, from the last decade, things went otherwise. Implementing machine learning and artificial intelligence have shaved off years of work, saving millions of dollars for the pharmaceutical industry. Machine learning and deep learning models can effortlessly identify the necessary proteins. They can analyse it digitally and simulate it through deep sequencing. Then the final and most effective composition is set to the autonomous machines for creating the medicine.
  •  Advanced training and research: AI can extract insight from collected and stored data by analyzing them. It enables researchers to conduct studies at a faster rate as compared to the manual legacy approach. These AI algorithms and ML models leverage datasets related to genetic variations, etc. Artificial intelligence has become a cutting-edge technology for both researchers and scientists. They not just help in the discovery but also innovate healthcare equipment. Current research streams that utilize AI are Dermatology, Psychiatry, Disease diagnosis, telemedicine, etc.

Conclusion

With the partnership of AI, the healthcare industry is flourishing exponentially. It is providing much of the bedrock for that evolution by enabling predictive analytics and clinical decision-making systems. Other verticals where healthcare is leveraging AI are through robots. A Bangalore-based company, creates robots to help doctors in surgery, answer questions, disinfect surfaces, and other services. These days, all these AI-enabled systems require cloud services to store and compute such data.

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