Artificial Intelligence In US Healthcare
Many industries have been disrupted by the influx of new technologies, especially machine learning and artificial intelligence (AI), and healthcare is no different. AI is reshaping healthcare, and its use is becoming a reality in many medical fields and specialties. In the global healthcare market (the total value of products and services sold), AI was valued at $2.4 billion in 2019 and is projected to reach $31.02 billion in 2025.
The increase in the inflow of patient health-related digital data, growing pressure to cut down healthcare spending, an aging population, rising demand for personalized medicine, and the Covid-19 pandemic are some of the key factors driving AI adoption in the healthcare industry. New technologies powered by AI have the potential to help hospitals and health systems improve patient experience and outcomes by reducing administrative tasks and mining and processing medical information for faster and more accurate decisions, better diagnosis, and more effective treatment planning.
AI Application in Healthcare
- Managing medical records and other data: Since the first step in health care is compiling and analyzing information (like medical records), data management is the most widely used application of artificial intelligence. Robots collect, store, re-format, and trace data to provide faster, more consistent access.
- Achieving time and cost savings by automating administrative tasks: AI applications can help remove or minimize time spent on routine, repetitive administrative tasks, which can take up a significant portion of a healthcare practitioner’s time. For example, Olive AI is designed to automate a selection of tedious, high-volume administrative tasks in various departments of a health care organization, such as revenue cycle. Olive uses machine learning to find patterns among its data feeds, allowing the tool to identify workflows and processes suitable for optimization.
- Support in clinical decisions: Health professionals must consider every crucial information while diagnosing patients. AI can harness vast amounts of data—from genomic, biomarker, and phenotype data to health records and delivery systems. More than ever, AI is playing a key role in clinical decision support as it delivers data to providers to aid in diagnosing, treatment planning, and health management.
- Medical imaging and diagnostics: Radiological images obtained by MRI machines, CT scanners, and x-rays offer non-invasive visibility into the human body’s inner workings. But many diagnostic processes still rely on physical tissue samples obtained through biopsies, which carry risks, including the potential for infection. Deep learning medical tools can improve radiology diagnoses by analyzing medical data. These tools allow clinicians to better understand and define the aggressiveness of cancers. In some cases, these tools can replace the need for tissue samples with “virtual biopsies,” which would aid clinicians in identifying the phenotypes and genetic properties of tumors. AI can also review and translate mammograms 30 times faster, with up to 99% accuracy, eliminating unnecessary biopsies.
- Drug research and discovery: The path from the research lab to the patient is long and costly. According to the California Biomedical Research Association, it takes an average of 12 years for a drug to travel from the research lab to the patient. Only five in 5,000 of the drugs that begin preclinical testing ever make it to human testing, and just one of these five is ever approved for human usage. Furthermore, on average, it will cost a company US $359 million to develop a new drug from the research lab to the patient. Drug research and discovery is one of the more recent applications for AI in healthcare. By directing the latest advances in AI to streamline the drug discovery and drug repurposing processes, there is the potential to significantly cut the time to market for new drugs and their costs.
- AI aids in precision medicine: Precision medicine is an emerging approach for disease treatment and prevention that considers individual variability in genes, environment, and lifestyle for each person. Artificial intelligence algorithms can take precision medicine to the next level by increasing the accuracy and prediction of outcomes through mining large quantities of genetic, clinical, social, lifestyle, and preference data across broad, heterogeneous populations.
The Future of AI in Healthcare
The integration of AI solutions with healthcare offers great potential, but there’s still a lot of ground to cover. The first obvious step will be to address the major challenges preventing the general adoption of AI in the healthcare space, and when that happens, only then can we expect exponential growth in healthcare AI. For more information on our IT services, contact Alvarez Technology Group today.
