Artificial intelligence has come a long way since we were first introduced to robots like R2-D2 and C-3PO in the Star Wars universe. As AI evolves, so does its prevalence in the healthcare space. From risk assessment to diagnosis and treatment of diseases, technology, when used collaboratively with the physician, can help provide highly impactful and effective patient care. Here's a look at some of the ways in which artificial intelligence has begun to reshape the future of healthcare and the potential challenges these advancements pose.
Machine & Medical Device Intelligence
From cars that can parallel park themselves without driver assistance to smartphones that provide limitless information - like videos of breaking news stories or e-commerce apps that can feed your new fitness addiction with a tap of your finger - the consumer marketplace relies heavily on smart devices.
In the healthcare space, medical devices are an essential part of monitoring patient vitals during surgery or while in the ICU. When incorporated into the medical environment, AI has early detection capabilities; identifying complications or deterioration in the infancy stages to lower costs associated with hospital-acquired conditions and increase the rate of successful outcomes. Artificial intelligence can also be used for predictive modeling, to create customized treatment regimens, and build responsive implants or prosthetics with advanced capabilities.
IBM Watson Health and Israel-based company MedyMatch Technology are putting artificial intelligence to work. Together, they’re developing an algorithm that combines patient data, deep learning, clinical insights, and machine vision to detect bleeding in the brain after head trauma or a stroke. Researchers are also aiming to train AI to identify tuberculosis, one of leading ten causes of death across the globe, on chest X-rays – which would help screening efforts in TB-prone regions that lack access to radiologists.
There are countless cases similar to these in which algorithmic intelligence is being introduced to medical devices and equipment to improve the patient experience, quality of care delivered, and provide more timely solutions.
Uniting Man and Machine
Brain-computer interfaces use AI to measure and decipher neuron activity in a manner that translates into action or communicates information. This cutting-edge technology creates a direct window into the human mind, with life-changing applications for patients’ with neurological disorders or who have sustained injuries to the nervous system. Brain-computer interfaces can potentially help these individuals overcome limitations, giving them the ability to move, speak, and have meaningful interactions with others without using a monitor or keyboard.
For the 250,000-500,000 people across the globe who suffer a spinal chord injury each year, are diagnosed with ALS, or have experienced a stroke, this technology could open the doorway for these individuals to live a life they feared they had lost for good.
EHR-Based Risk Data
While electronic health records hold a treasure trove of valuable patient information, the process of extracting data for predictive modeling purposes requires an exorbitant amount of time and can be unreliable. As a resource for risk stratification and predictive analytics, EHRs can be a major headache for healthcare institutions, as patient records can be outdated, incomplete, and pose overall data integrity issues.
At HIMSS18, major EHR players like Epic, AllScripts, and eClinicalWorks announced plans to incorporate AI and machine-learning in forthcoming versions of their software solutions to improve quality of data and care outcomes. New tools will include things like voice-based virtual assistants and telemedicine portals, so patients and healthcare institutions can leverage digital interactions to enhance accessibility. Additionally, these EHR systems plan to have an AI-based resource that will assemble data to provide clinical recommendations. Besides the misinterpretation of data, one of the biggest challenges this kind of predictive modeling will face is ensuring the algorithms used don’t include any hidden biases.
Bridging Knowledge Gaps
Although advancements can enhance public understanding - like doc.ai, which uses robots to interpret blood tests and other lab results to communicate findings with patients via the app – it can also create a lot of growing pains for hospitals and facilities looking to adopt these new technologies. With physician burnout already a pressing issue, adding user training for new software and systems into the mix can potentially amplify burnout. And let’s not overlook the pretty lofty investment healthcare institutions will need to make for proper implementation and adequate training. Even with the challenges that AI will surely raise in the healthcare arena, there's no denying the incredible value this type of technology and machine-learning can bring to the medical space.