Technological advancements in healthcare have had a significant impact on how diagnostic care, patient care, and personalized treatments are delivered. From tailoring treatments based on an individual’s genetic makeup with the help of real-time data and EHRs (Electronic Health Records) to remote patient monitoring through connected devices and wearables – the advent of tech has improved the efficiency and accuracy of healthcare services. It empowered patients to participate in their healthcare.
One such technology that is breaking bounds in the healthcare industry is Gen AI.
Gen AI or Generative AI is a revolutionary aspect of artificial intelligence that uses technology to create diverse and new content, unlike other AI setups that rely on predefined rules and programming. Gen AI can intelligently learn patterns from the available data, even if it’s unstructured, and produce human-like results. Generative AI healthcare promises to improve diverse customer engagement model in the organization, like experiences, acquisitions, and educating about patient health through personalized care, virtual assistants, chatbots, etc. Not only this, but also breaks information barriers and generates medical breakthroughs.
Application of Gen AI
Generative AI-enabled solutions have a wide range of applications in healthcare, including,
Medical Imaging
Generative AI focuses on the creation of data. It could include text, video, audio, or image. Medical imaging is a significant part of diagnosis and treatment planning, from tumors to infections and fractures. Medical imaging technologies produce large volumes of image datasets that require expert analysis to detect and diagnose conditions and monitor treatment effectiveness.
Gen AI can be useful in enhancing this process by employing advanced algorithms. It can accurately interpret radiology images, X-rays, MRIs, and CT scans to assist healthcare professionals in predicting healthcare outcomes and detecting abnormalities much quicker and with exceptional accuracy. From enhancing the quality of the images, generating synthetic medical images, and augmenting existing datasets for machine learning to reducing redundant noise – Gen AI can help predict healthcare conditions, develop tailored treatment plans, and back them with accurate data.
Automating Administrative Workflow
Gen AI is not limited to clinical aspects, nor is healthcare limited to patient care and drug discovery. Healthcare administrative tasks are crucial for managing the healthcare processes of any organization despite seeming expendable.
From scheduling appointments to maintaining patent records, billing and claim processing, and regulatory checks, these tasks can consume valuable time and resources that could be better utilized for patient care. Automating these tasks can help reduce the burden on healthcare professionals and minimize manual data entry errors and inaccuracy.
Gen AI plays a crucial role in automating these administrative processes, streamlining workflows, and increasing efficiency across healthcare organizations. It can parse and extract relevant medical data, such as medical records and clinical notes from different sources and populate EHRs automatically. It not only speeds up the workflow but ensures data accuracy.
Drug Discovery & Clinical Trials
The traditional methods of drug discovery and clinical trials are notorious for their time-consuming processes, high costs, and failure rates. These discoveries often involve a multi-level scientific approach, experiments, and drug tests before a drug can be presented in the market.
Gen AI accelerates and improves drug discovery and clinical trials by analyzing huge datasets and identifying potential outcomes. By analyzing biological datasets, this AI model can predict the potential value of the drugs, their effectiveness against different diseases, and individual biomarkers. Gen AI can also analyze past data and historical clinical trials and identify different drug interactions, as well as potential drug trial candidates. It expedites not only the development of treatment plans but also opens avenues for targeted clinical trials.
AI and its models are soon set to become essential tools for diagnosis and treatment. The adoption of generative AI healthcare can help transform several processes, address challenges, and ensure the sustainability of healthcare providers. However, successful integration depends on openness to digital adoption, data readiness, data security and privacy, ongoing learning models, awareness, technical infrastructure, cost, and maintenance.