How is Artificial Intelligence being used in healthcare?
Artificial Intelligence, or AI, is having a dramatic effect on the healthcare sector. At its core, artificial intelligence seeks to mimic the unique processing capacity of the human brain. Using algorithms, pattern matching, deep learning, cognitive computing, and heuristics, AI is able to quickly sort through masses of raw data. This is incredibly helpful in the medical field. In addition to the millions of Electronic Health Records (EHRs) at the center of our healthcare system, medical practitioners must also incorporate data from studies, data from testing, and past patient records when diagnosing and treating a case. AI can use predictive models to find irregularities or similarities in raw data that doesn’t have to be pre-sorted. This helps doctors improve diagnosis accuracy, patient care, and outcomes. AI’s ability to find meaningful relationships in data is being used as a powerful tool to aid in drug development as well as patient monitoring and treatment plans. Artificial Intelligence is becoming more common in many parts of the healthcare system, and it is estimated that $36.8 billion will be invested in AI systems across the US by 2025. AI is poised to be the main force that drives improvement across the healthcare industry.
Why is this a big deal?
Artificial Intelligence will be the engine of change by organizing masses of data and giving relevance to data points, which will ultimately improve reliability and objectivity in diagnoses. AI will provide context for patient data more quickly than ever before, allowing doctors to identify and treat diseases accurately, minimizing misdiagnosis and lowering the mortality rate. In addition, the costs for drug development will be lower, as we will more accurately be able to predict the drugs’ effects in certain patients. This all leads to an increase in doctors’ facetime with patients. They become freed from analyzing mountains of data and more able to focus on care and healing.
What concerns with cybersecurity arise when using AI?
When we open up patient records to artificial intelligence, we are opening up our systems to outside attacks. With sensitive information at risk, healthcare providers must be very careful that their rate of system upgrade does not outpace their security improvements. Installing new systems that sort sensitive patient data must be tested from all endpoints to ensure there are no flaws or vulnerabilities to attack. AI dramatically increases the complexity of assessing security threats. These new systems could be a point of entry for malware that will be difficult for systems designed to monitor human behavior to detect.
What upgrades in cybersecurity are necessary to protect against these concerns?
Greater use of AI in healthcare systems means that we need greater use of AI in cybersecurity software to match it. Our main protection will be anomaly detection. This will mean installing these detection programs across all endpoints in the system. Anomaly detection works in the same way that the AI identifies meaningful relationships in patient data. It monitors the system and senses potential threats whenever there is unusual behavior. Anomaly detection can do more than discover malware within a system. It can also identify where the cyber attacks are coming from and what kind of attacks being perpetrated. Predictive analytics for malware detection can also stop problems before they start. These analytics can identify suspicious files and prevent them from opening, stopping problems before they start. Properly planned and configured, these new cyber security measures act like the immune system for a healthcare company.
What are the challenges in implementing new AI/Cyber Security Procedures in healthcare systems?
Establishing new and heightened security procedures require behavior monitoring, to make sure users are complying with new systems. While some users may think that increased security measures are intrusive at first, compliance is paramount. When cybersecurity systems are implemented without factoring in the human element and allowing time for training, it can often lead to users falling back on unauthorized apps and outdated but familiar systems. These non-sanctioned entrances into the system leave it vulnerable to a breach. There are human users in your system in addition to AI, and it can take time and planning to make sure your innovations don’t outpace your cyber security procedures. A coordinated strategy that considers both human and artificial intelligence creates a healthcare system that is more accurate, faster, and cheaper for patient treatment.
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