Battelle – The algorithm is in: 5 Ways AI is transforming medicine
Back
Are you ready to see Dr. Data? Your doctor is not likely to be replaced by an algorithm anytime soon (or ever). But artificial intelligence (AI) and machine learning are already transforming medicine—and the era of AI-based healthcare applications is just beginning.
Researchers and clinicians now have access to huge volumes of data from Electronic Healthcare Records (EHRs), sophisticated sensors and imaging tools, genomic analysis, and a multitude of other sources. Sophisticated AI tools, including machine learning and predictive algorithms, can help people draw meaning from this deluge of data and see patterns that can be used to make better choices for patients.
Battelle is working to bring AI solutions to patients, providers, and medical researchers. They’re solving critical challenges in diagnostics, predictive medicine, drug development, neurotechnology and more.
Here are five ways Batelle sees AI revolutionizing medicine:
1. Diagnostics and Treatment Recommendations
AI can recognize patterns in diagnostic data that may be missed by physicians and medical analysts, directing them towards more precise and well-informed diagnoses.
An AI tool that can read and interpret medical records could find patterns and compare them to similar patient profiles. This could be especially valuable for detection of rare diseases and conditions outside of a particular doctor’s area of expertise.
In the future, AI could also analyze large numbers of EHRs to predict which patients will benefit most from a particular treatment and which may have adverse reactions to a treatment option.
2. Monitoring and Predictive Medicine
Both patients and healthcare providers now have access to an extensive array of monitoring devices. Many of these devices are programmed to send an alert to the patient, caregiver, or doctor when data falls outside of predetermined parameters. But AI will allow us to go much further. An AI health assistant could monitor data from multiple devices and look for patterns that indicate a problem is developing. An AI can even “learn” by combing through EHRs of similar patients to look for patterns that are correlated with different kinds of negative events.
Predictive AIs could be used to determine which patients may be at risk for a cardiac event, stroke, respiratory failure, kidney failure or other medical emergency.
3. Pathogen Detection and Identification
Genomic analysis is already widely used to detect and identify known pathogens. Traditional DNA analysis looks for genetic sequences that can be used as unique identifiers for different species of microbes. This makes it easy to confirm the identity of known pathogens such as E. coli or MRSA. However, it does not aid in the identification of previously unknown species that are not already in pathogen databases. AI can help us go further by analyzing the genomes of unknown microbes and emerging pathogens and predicting their behavior. Combining machine learning with human subject matter expertise, AI can predict whether a novel species represents a threat to a variety of hosts, including humans. The AI can be used to analyze microbial genomes for pathogen severity, antibiotic resistance, and infectiousness by looking at genetic sequences and the proteins they encode.
4. Drug Development
AI could accelerate development of traditional small molecule and biologic drugs. For example, AI could be used to analyze molecules and predict their potential function and behavior. AI-based virtual development tools may one day allow researchers to design candidate drugs in a virtual environment and select the candidates for synthesis and testing. This may be especially valuable for biologic drugs, whose actions and effects can be difficult to predict. AI development tools could help researchers quickly screen out formulations likely to produce adverse effects and narrow down the candidates most likely to have the effects desired, speeding up development timelines and reducing the costs and risks of drug development.
5. Bioelectronic Medicine and Neurotechnology
Some of the most exciting applications for AI in medicine are in the fields of bioelectronic medicine and neurotechnology. These fields tap into the vast amounts of data produced by the human brain and nervous system—exactly the kind of problem AI is best at solving.
Using machine learning, we can train algorithms to make sense of neurological data. Our bodies use electrical signals to send information between different parts of the brain and between the brain and the spinal cord or peripheral nervous system. Interpreting these signals requires vast amounts of processing power to filter out the noise and assign meaning to the signals we find.
This was the approach Battelle used to develop Battelle NeuroLife®, a neural bridging technology that allows a paralyzed man to consciously control his wrist, hand and fingers. The system interprets signals from the brain and sends them to a special sleeve that stimulates different nerves and muscles to evoke specific movements, bypassing the damaged portion of his spinal cord. Researchers used machine learning to train the algorithm that reads the brain signals and sends instructions to the sleeve. The man was asked to consciously think about making specific movements with his hand repeatedly until the AI algorithm “learned” which patterns of brain activity were correlated with each desired movement. Similar approaches could be applied to allow patients to control prosthetic devices, navigate physical environments in a wheelchair, or interact with virtual environments using a computer cursor.
These examples are just the beginning of the vast potential for AI in medicine. Over the next few years, we are likely to see an explosion in AI-based tools for medical applications.
For more information visit Medical Technology | Battelle Market
Submitted by Battelle