Generative AI’s value in the healthcare sector in 2022 was an astonishing $1.06 billion worldwide. By 2032, this statistic is expected to shoot to an impressive $30.4 billion, showcasing the rapidly growing importance of GenAI in this industry. Based on a recent survey, about 72% of healthcare experts trust AI to enable administrative task automation, thus allowing clinicians to focus mainly on patient care.
Generative AI isn’t only transforming the healthcare sector and reshaping the core of medical research and patient treatment. Here are the six generative AI use cases in healthcare:
Six Generative AI Use Cases in Healthcare
1. Automating Administrative Tasks
It is one of the most noteworthy GenAI use cases in healthcare. Popular studies show that physicians’ burnout rate in the US has extended to 62%. Doctors affected by this particular condition are highly likely to be engaged in incidents endangering patients and also inclined to have suicidal thoughts and alcohol abuse.
Luckily, generative AI within the healthcare field can partially relieve the burden off the medical experts’ shoulders by automating or streamlining administrative tasks. It can minimize costs simultaneously affiliated with administration, which, based on HealthAffairs, are 15%-30% of total accounts for healthcare expenditure.
Generative AI can extract information from patient’s medical records and fill the related health registries, generate treatment recommendations, reply to doctors’ queries, examine medical insurance claims and also predict which will be rejected, compose surveys to collect patient feedback, analyze feedback, generate actionable insights to enhance care delivery, produce customized follow-ups & appointment reminders, and also create structured health reports.
2. Facilitating Simulation & Medical Training
GenAI in the healthcare sector can generate realistic simulations replicating a massive category of health conditions, letting medical professionals and students practice in a controlled and risk-free environment. Apart from any negative impact of AI in healthcare, Gen AI can help generate patient models with multiple diseases or simulate a medical procedure or surgery.
Traditional training includes pre-programmed scenarios or layouts, which are restrictive. On the other hand, AI can instantly produce patient cases and transform them in real time, responding to the trainees’ decisions. It creates a more authentic and challenging learning experience.
3. Assisting in Clinical Diagnosis
Another significant use case for Gen AI healthcare is that it can generate fine-quality medical images. By utilizing generative AI tools, hospitals can improve the conventional diagnostic abilities of AI. This technology can transform low-quality scans into extraordinary-resolution medical images that have great details, implement anomaly detection artificial intelligence algorithms, and offer the outcomes to radiologists.
Researchers can also train GenAI models on lab tests, medical images, and relevant patient data to diagnose and detect early onsets of various health conditions. Such algorithms can easily spot lung & skin cancer, quickly Alzheimer’s signs, diabetic retinopathy, hidden fractures, and more. Furthermore, AI models can unveil biomarkers that cause specific disorders and predict disease breakthroughs.
Diagnosticians can shift to generative AI in the healthcare industry if they have queries instead of finding answers in medical books. AI algorithms have the capacity to process vast amounts of information and generate remarks faster while saving doctors precious time.
4. Producing Synthetic Medical Data
Medical research depends on retrieving massive chunks of data on multiple health conditions. This information is painfully lacking when discussing rare diseases. Additionally, such data is costly to collect, and higher privacy laws govern its sharing and usage.
AI use in healthcare and medicine can create synthetic data samples that augment health datasets without complying with privacy regulations. That’s because the healthcare data isn’t associated with particular individuals. Top AI applications in healthcare can generate scans, HER data, etc.
5. Customizing Treatment Protocols
Generative AI is reshaping the realm of customized medicine. A generative AI services company in healthcare can help generate personalized treatment recommendations through AI algorithms.
For instance, in oncology, AI can analyze medical patients’ genetic structure to recommend highly effective cancer treatment, reducing side effects and also increasing survival rates. Such a level of customization guarantees more cost-efficient and effective healthcare delivery.
On the other hand, AI can review cancer patients’ genetic mutations, current health status, and previous treatment progress to suggest a customized treatment plan. With this tailored approach, medical professionals can expand the chances of a more successful treatment while minimizing absurd side effects.
6. Analysis of Health Contingency
Generative AI has become paramount in predicting disastrous health events, including pandemics. During the COVID-19 pandemic, generative AI healthcare models were contributory in analyzing large datasets to predict the news of the virus and its overall impact.
AI models are necessary to recognize new antibodies for infectious diseases, know their origins, and form data-driven and insightful responses. This method improves global healthcare readiness, offering robust AI platforms for risk mitigation and prediction.
For example, a team of researchers from the University of Oxford and Harvard Medical School developed an artificial intelligence tool called EVEscape. This generative core tool uses biological and evolutionary data to predict how specific viruses can transform to escape the patient’s immune system. In the COVID-19 pandemic period, EVEscape predicated the most concerning and latest variants. Based on research, this model can assist in developing therapies and vaccines for SARS-CoV-2 and related swiftly mutating viruses.
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