3 Use Cases for Artificial Intelligence in Healthcare
Artificial intelligence is bringing about major innovations in healthcare in a number of different ways. While so much regarding artificial intelligence involves the future, but within healthcare, it is not just plans or intentions, but AI is already a concrete reality in the field.
It has all been set in motion by advances in machine learning and fueled by the increasing availability of healthcare data. AI aims to simulate human cognitive functions. It can replace human judgment in certain functional areas of healthcare and provide a major assist to physicians with clinical decisions. A large volume of healthcare data can be run through sophisticated mathematical algorithms to identify and utilize insights that assist in clinical practice. Learning and self-correcting abilities within the formulas can also improve accuracy based on feedback. Up-to-date medical information from journals, textbooks, and clinical practices can be easily assimilated and that helps reduce diagnostic and therapeutic errors in human clinical practice.
For example, artificial intelligence allows physicians to understand specific problems before deciding on a solution. In the area of cardiac intervention, using arrhythmia as an example, you can create a map of the heart to pinpoint the exact problem causing an irregular heartbeat. Mapping provides the exact anatomical structure of the arteries. This is a major assist in planning interventions with a catheter. Mapping can determine the exact kind of catheter to be used and the exact behavior of the arteries at the specific point where you have to do the intervention. Mapping sometimes can be used during an operation itself. With images from the fluoroscopy on hand, AI provides analysis of those images in order to get timely and precise information about the location and the structure of the arteries.
Another challenging application for artificial intelligence is spinal surgery, which generally involves putting in screws into the vertebrae so precision is crucial. Precision does not come about due to what the surgeon sees because it’s a percutaneous procedure in many cases. Utilizing pre-op scanning, along with information provided by the x-ray, artificial intelligence assists in the operating room by detailing exactly where the vertebra line up. It happens through algorithms combining those two sources of information which allow the surgeon to accurately navigate to the exact point of insertion.
The use of algorithms during the planning phase provides the same benefits of precision for hip or knee replacement. Mapping also provides very good segmentation of the bones prior to the operation. It helps decisions on a specific implant for a particular patient, avoiding future suffering because generic methods might produce an implant that does not fully fit the knee or hip.
Utilizing multiple CTs and using deep learning, artificial intelligence also helps provide exact segmentation of the pulmonary airways, even the tiny airways found in nether regions of the lungs. It is a prerequisite procedure in planning an intervention surgery, such as a biopsy or, in some cases, an ablation. The mapping of the lungs again allows a surgeon to go and plan precise actions with the catheter (and in some cases with a robotic catheter). Accuracy is very important because you don’t want to miss and take the biopsy from adjacent places instead of the lesion itself and the AI process also reduces the possibility of puncturing blood vessels, the fissure between the lobes, et cetera.
Lastly, in the area of pharmaceutical development, artificial intelligence is used in assessing the influence of new drugs. A lot of effort is invested by collecting CTs that are done for the patient during the use of a new drug. AI algorithms take all of that information to produce an automatic scoring (which is called RECIST) that analyze and measures the impact of the drug (an example being whether a lesion has disappeared or shrunk or whether it stayed the same). Through AI, results of these scans can be determined within a few hours, as opposed to several months as before, providing better and more immediate decision making.
You can imagine many other instances where mapping and the amalgamation of data can assist in healthcare endeavors. But there is still a shortage of trained engineers that can develop new algorithms to solve ever more difficult situations. However, utilizing skilled engineers on an outsourcing basis to accomplish new algorithm cultivation is an important option for healthcare project managers. In the last five years, there has been a big change in the healthcare market where the understanding of the value of AI and computer vision has been made crystal clear and will result in the integration of newer and better AI technology into the healthcare system. Artificial intelligence has been around for 30 years but there is so much more work to be done.
The current examples described above point to artificial intelligence providing extremely fast and almost unlimited image analysis capabilities. Taken together, AI removes a seemingly endless bottleneck of tedious doctor tasks and will continue to provide accurate assistance in surgery (both pre-op and in-op). And through its speedy compilation of patient anatomical data augmented by observations and healthcare correlations that generally aren’t easy to be found, artificial intelligence will also foster more patient-specific healthcare.