Artificial intelligence (AI) is rapidly transforming the digital world. Today’s AI tools enable human users to access optimized potential, using machines to process vast amounts of data, recognize patterns a human may miss, and automate tasks. For these reasons alone, AI and healthcare are a perfect match. Healthcare is also unique in its scale: in addition to the 137 terabytes that health care providers generate each day, there is also the number of patients themselves. Virtually every human will access healthcare services at some point in their lives, and using artificial intelligence in healthcare helps medical professionals meet demand. 

Faster processing of medical files creates better patient outcomes, and while many medical professionals consider their work a calling, burnout is still rife across healthcare fields. AI tools can help to make patient’s lives better, without overworking medical professionals: which might be why AI was called one of the most exciting tools in healthcare by health systems leaders. Here are 5 of the most significant applications of artificial intelligenceI in healthcare to date:

1. Medical imaging and diagnostic tools

By harnessing the pattern-detection capabilities of AI, healthcare providers can now analyze X-rays, MRIs, and CT scans for abnormalities like tumors, broken bones, or signs of disease. In some research, AI tools match or even exceed the accuracy of radiologists when diagnosing conditions like breast cancer. With mainstream adoption becoming widespread, medical imaging is one area you’ll notice artificial intelligence in healthcare. Children’s hospitals such as Stanford Medicine Children’s Health and Boston Children’s Hospital are using AI and electronic health records (EHR) data to analyze patient care, predict newborn health outcomes, and use AI-powered imaging. Tools such as Google’s DeepMind can predict kidney injury up to 48 hours in advance using patient data, and AI predictions are even used to catch patterns and provide an early diagnosis for potential issues in pediatric mental health.

2. Personalized treatment plans

Healthcare providers are embracing “precision medicine”, with more treatments tailored to the patient’s genetics, lifestyle, and individual markers of health. Machine learning tools can now analyse patient data and recommend the most effective treatment, predict a poor response to a certain drug, or monitor the progression of a disease. In mainstream medicine, these personalized treatment options help to monitor emerging disease threats, show genetic markers for certain diseases (including ones that have eluded traditional models in the past), or predict whether a patient will respond to chemotherapy – potentially life saving outcomes. Precision oncology research platforms built with AI are now used at hospitals like UCLA, Baylor College of Medicine, and the Stanford Cancer Institute, further demonstrating the use of artificial intelligence in healthcare.

3. Administrative efficiency 

Scheduling, billing, and handling electronic health records (EHRs) consume significant time and can contribute to burnout: according to a survey of medical providers, payers, and consumers done by Google Cloud, clinicians are spending an average of 28 hours per week on administration. Claims staff spend between 34 and 36 hours per week on administration, and administrative tasks contributed to burnout and staff shortages in 81% of the medical staff.Google Cloud partnered with Tennessee healthcare providers HCA Healthcare and Community Health Systems to develop an AI powered nursing tool, which helps frontline nurses hand off critical information about a patient at the end of their shift. At Stanford, administrative AI tools are also used to schedule surgeries, see trends, and get a bird’s eye view of the ICU. The Boston Children’s Hospital has even tried an AI tool to predict no-shows! These applications of artificial intelligence in healthcare, particularly administrative tasks, are making a real difference in the day to day activities healthcare professionals participate in, offloading the manual work and allowing them some relief in their hectic schedules.

4. Population health

‍AI-driven analytics can help show researchers trends in the population as a whole, helping to identify at-risk groups and intervene in critical issues or trends before they start becoming a problem. With data trends across large populations, AI tools can harness the power of machines to collect millions of data points, suggest warning signs, or catch possible health crises. Tools like IBM’s Watson Health are used to track outbreaks of certain diseases and intervene before they become a crisis. 

5. Mental health support

Artificial intelligence is increasingly playing a role in healthcare, specifically in mental health care as well. In addition to early detection of some mental illnesses, 55% of mental health providers say they’ve used generative AI in their practice over the last year. And it’s not “robot doctors”, either: psychology practitioners who responded to the survey conducted by Wiley say rising demand for psychology is hurting their work-life balance, and many psychologists report using AI for generating patient materials and documents or assisting with written notes. Other artificial intelligence in healthcare trends, like predictive analytics or personalized treatment plans, are also used across mental health fields – for example, to predict severe challenges in mental health among college students.

The Future of Artificial Intelligence in Healthcare 

Adoption of AI is increasingly becoming the norm, especially when it comes to our health. With so much patient data available and so much value to be found in harnessing it, AI is fast becoming not just a time saver, but a generational tool for improving human health.