The Future of AI in Healthcare: Benefits, Uses, and Examples
Imagine a world where doctors had the most up-to-date diagnostic tools at their fingertips.
This future isn’t as far off as you might anticipate. AI in healthcare has the power to transform how providers work. The combination of deep learning algorithms and real-time information processing allows AI to provide the most up-to-date information to providers. This can lead to more accurate diagnoses and significantly improved patient care. Learn how AI in healthcare will revolutionize how providers work and improve patient outcomes.
The Rapid Growth of AI Adoption in Healthcare
Artificial intelligence is growing in popularity across a number of industries. Perhaps the most interesting use of AI, however, is in the healthcare industry. Using artificial intelligence in medicine has the power to revolutionize everything from treatment options to surgical procedures.
Research shows that, in addition to impacting patient outcomes, AI in healthcare also has the potential to save providers across the United States billions of dollars per year.
Already, approximately one in five healthcare providers has adopted some level of artificial intelligence. As AI in healthcare becomes more commonplace and both providers and patients become more comfortable with its applications, healthcare AI adoption is likely to grow exponentially.
Main Benefits of AI in Healthcare
The healthcare industry is in constant flux. Decisions have to be made quickly and accurately; lives are at stake. AI in healthcare can make a major difference, improving the quality of patient care significantly. Here are just a few of the key benefits of AI in healthcare.
Improved Diagnosis and Disease Prevention
AI software for healthcare has the power to improve precision medicine. Predictive analysis technology, for example, can use information about patients — including medical records — to determine which diseases or conditions they are most likely to contract. Providers can then use this information to help improve diagnosis and prevent patients from acquiring medical conditions down the road.
AI healthcare management software can also keep an eye on disease progressions throughout populations. This can warn medical providers of potential disease outbreaks, allowing them to quarantine patients effectively and design treatment response plans in real time.
Personalized Treatment Plans
AI software for healthcare has the power to improve patient care by providing patients with personalized treatment plans.
Consider, for example, the fact that providers already know that different bodies respond differently to the same foods. This information has real impacts on which diet plans work for different body types. AI software has the ability to learn over time. It can integrate its knowledge of individual patients with knowledge about how different body types work to recommend treatment plans unique to an individual’s physiology.
Enhanced Drug Discovery
AI is great for quickly analyzing large sets of data. When it comes to drug discovery, this capability means that developers are better able to work through data sets to predict how efficient potential drug candidates might be. This means that researchers can spend less time on the analysis phase of drug discovery and more time designing specific molecules and organizing clinical trials.
Over time, using AI in this way has the power to drastically improve drug discovery and production techniques. This could help researchers make drastic improvements within the healthcare industry, with the power to eradicate diseases and improve disease treatment and management.
Key Uses of AI in Healthcare
Artificial intelligence in healthcare isn’t just used for one purpose. There are a number of critical uses for AI in the healthcare sector, from completing repetitive tasks to improving health equity across diverse patient populations. Here are some of the key ways providers can use AI in healthcare to bolster their clinical practices.
Medical Imaging and Radiology
Medical images can be hard to interpret. AI healthcare tools are designed to help separate the wheat from the chaff when it comes to these images. AI tools can, for example, distinguish diseases from healthy body parts. Qualitative assessments and machine learning technology empower these tools to create models for what a healthy scan looks like and detect anomalies with surprising accuracy.
Using AI for medical imaging and radiology is helpful because it relies on qualitative assessment rather than human judgment. This means that there is less of a chance for human biases to seep into how medical images are read and reported on. As a result, patients may receive a higher quality of care when AI tools are involved.
Natural Language Processing in EHRs
Electronic health records are rich with real-world data based on real medical patients. Unfortunately, combing through medical records is a laborious process. Even if personnel could be dedicated to the task, it would be next to impossible to locate patterns that would lead to improved diagnostics.
NLP in healthcare, on the other hand, is up to the task. NLP software, when combined with healthcare compliance software, can work through medical records to find patterns. It can then analyze those patterns and make predictions about future healthcare outcomes. These predictions can be turned into models for providing quality healthcare in the future.
AI in Genomic Medicine
Using AI in genomic medicine is still in its infancy, but initial results are promising. For example, some researchers have used medical images combined with AI software to accurately identify genetic disorders. Researchers have also used genomic medicine to identify the primary type of cancer a patient has based on a liquid biopsy.
While these examples are of early use cases, the implications are vast. This goes beyond using AI for healthcare automation and indicates that AI may one day be as good as — if not better than — physicians at identifying and diagnosing genomic conditions. This could lead to earlier detection of medical diseases, better treatment plans, and improved overall patient care.
Real-World Examples of AI in Healthcare
AI in healthcare is rapidly evolving. As we entire a time when the need for providers is higher than the number of trained professionals in the field, turning to AI is a natural next step in lowering the healthcare burden. AI tools may even be able to help patients receive remote healthcare access, limit healthcare costs, and improve overall outcomes.
Here are just a few real-world examples of how AI and healthcare can work together to improve patient care.
AI-Powered Diagnostic Tools
Patient diagnostics is generally based on a number of different scans and tests, including ultrasound, magnetic resonance imaging, mammography, genomics, computed tomography scan, and so on. With traditional medical techniques, each of these images would be evaluated separately, and the information would be combined with a physician’s expert opinion to develop a diagnosis.
AI-powered diagnostic tools, however, have been successfully used to expedite this process, allowing patients to begin treatment plans sooner than if a doctor didn’t use AI technology. One reason this works so well is that AI tools can analyze larger amounts of data at a time while searching for patterns and anomalies. This makes for a much more nuanced understanding of test results, with less room for human error or implicit biases to rear their heads.
Drug Discovery Success Stories
The pharmaceutical industry has begun pairing with AI companies, and initial results are promising. For example, the first AI-designed drug molecule entered human clinical trials in 2020.
Researchers are using AI to help with drug discovery in a number of ways, including molecular simulations, predictions of drug properties, candidate drug prioritization, and synthesis pathway generations. So far, results have shown that AI can handle these tasks at a fraction of the speed and cost of traditional research methodologies.
AI-Enhanced Medical Imaging
AI technology is excellent at extracting information from medical images. In fact, a recent study showed that AI models were 98.56% accurate in classifying brain tumors based on MRI-scanned images. This is a significant time-saving technique when compared to the fact that traditional classifying techniques take about 40 minutes to complete.
AI-enhanced medical imaging works because AI is exceptionally good at locating patterns and anomalies. Machine learning technology enables AI systems to become intimately familiar with what normal scan results should look like. In contrast, abnormal scans stand out, and the AI is efficient at pinpointing scan discrepancies and determining their causes.
Use NLP and AI to Improve Healthcare Outcomes
AI in healthcare is a powerful tool that can reduce human errors by cutting down on implicit biases in the healthcare field and improving data analysis techniques. Combined with natural language processing technology, AI tools have the power to revolutionize the healthcare industry, leading to more personalized healthcare treatment options for patients, speedier authorizations, and better decision-making capabilities for providers.
Consensus can help. Learn more about how healthcare automation and data prediction software can improve the care you offer patients — all without putting patient information at risk. Request a demo today to see how our AI tools can bolster your practice today.