During Covid-19, AI played a considerable role in the global response to the pandemic. The technology assisted in saving lives and mitigating the impact of the pandemic on populations around the world by accelerating vaccine research, detecting outbreaks, and enhancing patient care.
The technology has come further along since then and is opening the path for transformative progress in disease diagnosis, customizing treatment plans, and enhancing patient experiences by leveraging neural networks, natural language processing, and robotics. The global AI in healthcare market valued at USD 15.1 billion in 2022, is predicted to exceed USD 187.95 billion by 2030, increasing at a CAGR of 37% from 2022 to 2030.
In this article, we’ll look at the key applications of AI in healthcare and provide tips for organizations wanting to deploy AI.
Applications of AI in healthcare
Some of the major applications of AI in healthcare include:
AI can swiftly and accurately assess patient data, such as medical images, test results, and EHRs, using machine learning algorithms to detect diseases. AI-powered diagnostic tools may detect diseases like diabetes, cancer, and cardiovascular disease at an early stage.
Google’s DeepMind Health has built an AI algorithm to accurately diagnose eye disorders such as AMD (age-related macular degeneration) and diabetic retinopathy (DR). It analyzes retinal images and detects indicators of disease using a convolutional neural network (CNN), allowing clinicians to deliver better care.
AI can help physicians provide personalized treatment plans tailored to each patient’s needs. It can evaluate enormous volumes of patient data using machine learning to suggest therapy alternatives most vital for a specific patient.
IBM Watson for Oncology, for example, employs AI to assess a patient’s medical history, lab data, and cancer pathology findings to determine the most effective therapy alternatives. The tool provides clinicians with individualized treatment recommendations by leveraging natural language processing (NLP) and machine learning.
By enabling predictive analytics, AI is being used to optimize treatment outcomes. AI can identify people at risk of getting a specific disease or suffering a medical issue by evaluating patient data. This enables healthcare personnel to intervene immediately to prevent adverse outcomes.
Hospitals can use predictive analytics to detect patients at risk of getting sepsis, a potentially fatal infection-related disease. By evaluating patient data in real time, AI can detect early warning symptoms of sepsis and alert providers, helping them to intervene before the condition becomes severe.
AI algorithms are used in robotic surgical systems to offer physicians real-time assistance and advice during surgical procedures, allowing them to execute surgeries with improved precision and accuracy.
The da Vinci Surgical System employs AI to provide surgeons with 3D views of the operative site, allowing them to execute procedures more accurately. The technology also gives haptic feedback, enabling surgeons to sense the tissue they are operating on and make more precise movements.
AI is becoming increasingly crucial in drug discovery, which has traditionally been a lengthy and costly process. AI can scan enormous volumes of data and identify possible clinical participants more efficiently and correctly than humans by applying machine learning algorithms.
For example, Insilico Medicine, a startup focusing on AI-powered drug development, has created an AI platform capable of predicting the biological activity of numerous molecules and identifying viable drug candidates in days. This can help in providing new therapies to patients faster and at a cheaper cost.
Precision medicine aims to customize treatment to a person’s genetic makeup and medical history. AI plays an essential role in precision medicine by facilitating faster and more accurate analysis of genetic data.
Mount Sinai Hospital in New York City, for example, is employing AI to develop a tailored approach to cancer therapy. Researchers can find the most effective therapy option for the patients by examining their genetic data, medical records, and other relevant data.
AI plays a huge role in genomics by allowing researchers to evaluate massive volumes of genomic data and uncover genetic variants linked to specific diseases.
For example, researchers at MIT and Harvard’s Broad Institute are employing AI to analyze genomic data from cancer patients to detect genetic alterations driving tumor growth. Researchers can design novel medicines that target the exact congenital defects causing cancer growth by identifying these mutations.
Getting started with AI in your healthcare organization
Here are some tips for healthcare organizations to get started employing AI:
Begin with a clear problem: Before using AI in healthcare, it’s critical to begin with a clear problem to solve. This could range from enhancing treatment outcomes to lowering expenses or simplifying administrative tasks. Starting with a clear problem allows you to focus your efforts and ensure that you adopt AI that corresponds with your organization’s goals.
Build a cross-functional team: AI implementations require deep collaboration among healthcare professionals, data scientists, and IT specialists. You need to build a team with expertise in developing, testing, and deploying AI solutions.
Build a secure data infrastructure: AI is as good as the quality of data fed into it. You would need a data infrastructure to collect, store, and securely manage data. Following data governance standards and making investments in cloud computing is also necessary.
Begin small and iterate: AI implementations involve a complex process that involves meticulous preparation, testing, and iteration. Before scaling up, you should start by evaluating AI solutions in a secure environment and extensively review them. This iterative strategy allows you to learn from failures quickly and continuously better your AI solutions.
Address ethical and regulatory challenges: Artificial intelligence in healthcare creates significant ethical and regulatory considerations. Healthcare organizations should be transparent about how they will use AI. This includes ensuring data the AI system uses is accurate and patients are educated about AI’s capabilities and limitations. Your AI systems must adhere to relevant standards such as HIPAA and other country-specific regulatory requirements.
AI can completely transform the way care is delivered. As AI technology evolves, we’ll see more innovative applications to improve drug discovery, patient outcomes, and treatment quality.
To learn more about deploying AI in your organization, email us at email@example.com. Intellect Data, Inc. is a software solutions company incorporating data science and AI into modern digital products. IntellectDataTM develops and implements software, software components, and software as a service (SaaS) for enterprise, desktop, web, mobile, cloud, IoT, wearables, and AR/VR environments. Locate us on the web at www.intellectdata.com.