AI health

Artificial intelligence (AI) is seeing rapid adoption across many industries. With its urgent need for high precision and fast responses, the health care sector stands to gain more than most from this technology.

Medical AI applications have already started to emerge, often with promising results. As this adoption grows, it’s become increasingly clear that it could thoroughly disrupt the industry for the better. Here’s a closer look at five of the most significant use cases of AI in health care.

Early Diagnoses

Detecting and classifying health conditions early is one of the most disruptive applications of AI in medicine. It can make connections and notice trends in data earlier and more accurately than humans, so it’s ideal for preliminary diagnoses or predicting how a condition might progress.

AI models can detect some conditions with up to 99% accuracy, putting them on par or ahead of doctors. Similarly, they can predict the best course of treatment with similar precision levels and often pick up on signs before they’re noticeable to humans.

Most experts agree that final medical decisions should remain in doctors’ hands, but AI diagnoses could provide a useful starting point. These early, highly reliable analyses could help physicians determine the best treatment for a patient in less time, leading to improved patient outcomes.

Personalized Medicine

Similarly, AI can tailor treatments to patients’ unique needs. Determining an effective course of action in this field can be difficult because so many influencing factors exist. However, balancing multiple parameters in vast data sets to find the best way forward is precisely what AI excels at.

After a doctor makes a diagnosis, AI models can analyze past cases of patients with the same condition. They can look at people with similar genetic markers, socioeconomic backgrounds, preexisting conditions and other factors to see which treatment would likely yield the best results.

This personalization would result in more effective treatments and fewer follow-up visits, reducing patient costs. It can improve outcomes outside of treating diseases, too. Duke Health found that using AI to personalize smoking cessation programs was 10 times more effective than without it.

Streamlined Training

AI has medical applications outside of patient-facing operations. Hospitals’ need for fast but thorough training rises as they face staffing shortages and embrace more new technologies. AI could streamline this onboarding to boost productivity and give professionals more time for mission-critical tasks.

AI can personalize training resources for specific users to increase engagement and knowledge retention. Studies using personalization and tech adoption in medical training found that 90% of participants left feeling prepared and preferred this approach to traditional methods.

Automating the more routine parts of a workflow through AI can also help. Employees will have less to learn, and what’s left is more engaging, leading to shorter, more effective training periods.

Reducing Administrative Work

Another way to address talent shortages in health care is to use AI to manage administrative tasks. Processes like authorizing insurance, invoicing and maintaining records take much of hospital workers’ time but provide minimal medical value. AI automation lets professionals focus on actual treatment.

Doctors can treat more patients without a significant administrative burden, even with fewer resources. Some health care systems have been able to perform 7% more surgical services despite closing 20% of their operating rooms by embracing AI automation.

These efficiency gains improve profits for hospitals without sacrificing patient care. On the contrary, they make health care more accessible by reducing wait times and expanding hospitals’ throughput capacity. Automating data-heavy tasks would also reduce health record mistakes, improving treatment accuracy.

Drug Discovery

AI in health care has applications outside of hospitals, too. Medical researchers can use it to speed drug development timelines and find more effective treatment options.

Deep learning models can simulate how various compounds interact with different diseases. These simulations can reveal potential new drugs with a high degree of accuracy in a short timeframe. Pfizer and Moderna used AI to develop their vaccines against COVID-19, leading to their remarkably rapid development and release.

AI can also streamline the regulatory process during clinical trials. Predictive models can identify ideal candidates for testing, and administrative automation could ease compliance through more accurate record-keeping. As a result, pharmaceutical companies can bring new, potentially life-saving products to market faster.

AI Has Many Applications in the Medical Sector

The health care industry has only recently begun implementing AI, but its potential is already vast. As this technology develops and new use cases emerge, AI will undoubtedly disrupt many practices throughout the sector.

Medical companies and developers must prepare for this shift. AI will play a key role in tomorrow’s health care space, so learning how to use it safely and effectively is crucial. The sooner organizations can familiarize themselves with AI, the better equipped they’ll be to remain competitive in the future.