# IoT for Smart Cities: Machine Learning Approaches in Smart Healthcare--A Review
## 1 Introduction  
There is hardly an industry that does not benefit from increasing IT resources and self-learning computer networks--but in the healthcare sector, AI can be used in different ways, because AI approaches help with treatment and especially with operations, but also support medical staff in diagnostics and prophylaxis. In the USA, for example, there are already operating rooms in which all steps of an operation are meticulously recorded and the treating physicians are also given detailed support in deciding on the next steps and even the individual incisions to be made. The patient is tracked at all times by sensors built into the room and thus optimally cared for. The work of specialists, for example, that of a radiologist, will also change as a result. Especially with imaging processes that can be easily evaluated and standardized technically, the technology is already doing a remarkable job. Since it has evaluated millions of cases, it can judge with greater accuracy than the treating doctor with the naked eye. Even if the decision-making authority will always remain with the doctor, thanks to artificial intelligence he will receive a valuable second opinion that can underpin his own anamnesis or add further aspects. The more data the system has from the patient himself and the more other cases it knows, the more certain the statements and recommendations for action will be. It will therefore be desirable in the future to have as much data as possible available and to evaluate it, considering all anonymization measures. In general, data from wearables in everyday diagnostics will support the patient and the treating doctor in making the right decisions more quickly and making recommendations for action. For example, a patient who wakes up in the morning with an increased pulse and blood pressure can better decide whether to see a doctor or whether the malaise is likely to go away on its own in the course of the morning. The challenge we face today is that we are struggling with various interfaces between medical subsystems that have to ensure interoperability with one another. With the help of a blockchain, patient data records can be standardized, reliably exchanged, evaluated and stored in a forgery-proof manner. This means that those responsible for medical data do not have to harmonize several different systems in parallel, but can rely on a reliable standard [1]. A blockchain as a basic technology will also be used in other industries and sectors where the exchange of reliable, unchangeable data is important. We would be an important step closer to the digital patient file, which provides the treating doctor with all relevant data (and thus also benefits the patient). In a broader perspective we can refer to blockchain technology in the IoT as a \"secure by design\" system that can be used to address security concerns in IoT applications, considering the impressive features of blockchain technology such as immutability, transparency, auditability, data encryption and operational resilience, as explained by [2]. Networked smart home applications will also be helpful in this context. The patient, who gets up in the morning and stands in front of a mirror equipped with cameras, will be able to establish a connection with the attending physician if he has symptoms. Based on the patient data, he then decides in real time which steps are sensible. However, the whole thing goes even further: in the worst case, fall sensors installed in the carpet and in the house or suitable healthcare wearables could detect when a patient falls and is lying on the floor or when help is required for other reasons. Automated in the context of a smart city, the corresponding control center could be informed and an ambulance sent, with the appropriate hospital with the appropriate capacity being selected based on the diagnosis.