As Artificial Intelligence, Machine Learning and Big Data are utilized in a broad array of applications in healthcare centers, cognitive computing systems, for instance, are utilized to make possible repetitive processes and to make sample analyses in the context of radiological diagnostics. Other instances are the Deep Learning Model of Stanford University and Google Brain that are utilized to find out the most reasonable time of the death of gravely unwell patients and as a result, the best treatments, including allowing patients to be transferred to analgesic care in good time in order to give them a dignified farewell.
Surgical robots or other robotic-based systems, on the other side, are another field of application. Some folks deem that medical robots will be crucial for high-accuracy surgical treatments in the coming years. However, they have the potency to advance post-operative rehabilitation and to offer extremely competent logistical support in clinics. AI and Machine Learning, without qualm, become more sophisticated, but the utilization of robots in terms of treatment will only boost. In the healthcare sector, the use of AI is the cause of some alarm among patients who are concerned about privacy and precision. Software-based treatment options, in which the essential evaluation is only partially made by a doctor, are particularly focus of skepticism. This is hardly unexpected, as on the one hand very susceptible data is processed and on the other, machines are entering into an area that was earlier only occupied by humans. In case of privacy, patients should find the console in the fact data protection law not only places specific needs on software-based systems in hospitals, but it also put down general guidelines that should be perceived in the context of AI and Big Data.
Health data may only be processed if allowed by law or with the consent of the data subject. For the time being, specific legislation is unlikely to deploy so folks are looking at consent. In order to attain effectual GDPR consent, transparency is indispensable. The individual must be able to comprehend not only how their personal data is processed, but also who the recipients of the data are. This is a huge challenge for big data applications because the reasons for the processing are not always determinable in advance. AI solutions may be able to learn in a different way based on diverse data sources. But this can be challenging for consent purposes as it may be complex to give details to the patient what exactly happens to their data.