Risks of Artificial Intelligence (AI) in Medicine

cyber-attack against a government 12,13 . Another issue is data bias. During the collection of the data, intentionally or unintentionally, certain minorities, races, ethnicities, or genders may be significantly misrepresented. Therefore, these algorithms are biased and inadequately represent the general population 14,15 . This bias effect could be magnified by the reluctance of medical practitioners, hospitals, or other health organizations, to provide the medical files of their patients due to fears of security leaks. Another significant danger of medical data misuse is the data poisoning effect, which refers to the deliberate manipulation of medical data to introduce errors or biases in healthcare. This has serious consequences on the accuracy and reliability of medical recommendations. This could also affect the outcomes of clinical trials or insurance claims 11 . Finally, when AI uses different epidemiological data models, as was seen during the COVID-19 epidemic, this could lead to different conclusions