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Whenever there is an innovation in tech that disrupts the status quo, we see a similar pattern to it. Moisejevs explains in his article how after the PC, Mac, and smartphone were developed we saw an immediate rise in the usage as it became more popular and more uses were found for them. However, shortly afterward, we saw a similar growth in malware for these systems [6]. ML and AI are transitioning from their infancy stage to the growth phase, which means that in the next few years, we will continue to see more and more applications of AI in healthcare products, in sheer numbers and the depth of role that it plays. However, as these AI numbers increase so too will malware, and in the healthcare setting, this could have devastating effects on the patients.

When these concerns about malware are applied to healthcare, it is best to view it in two categories; similar to how AI is. There is the digital side that has to do with data, patterns, and ML, and there is the physical side. On the digital side, the primary concern is the protection of data. The reason for this is because all decisions made via AI stems from having reliable data. To give an example, many HCPs use AI to help diagnose patients. If a patient were to come in and have various scans and tests performed, unreliable data may cause the patient to be misdiagnosed, or possibly not diagnosed at all. Another example comes from the EMR. If the patient is taking chronic medication, and the data is corrupted, it might misinterpret the pattern and believe that the patient just picked up their medication from the pharmacy recently when in fact they are due for a refill. If this happens, it may cause issues for the patient because insurance will not pay for another refill since according to the EMR, the patient has plenty of medication.