Risks of Artificial Intelligence (AI) in Medicine

Abstract

Artificial Intelligence (AI) is the most rapidly advancing science affecting the majority of human activities in a rather beneficial way.AI has modified human life improving communications, traveling, car industry, commerce, construction, and agriculture 1,2 .AI using Deep Reinforcement Learning Techniques is rapidly progressing and it is predicted that by the year of 2050, or even earlier, it will reach the level of general human intelligence 3 .Although all this progress is welcomed, it is foreseen that it may be followed by significant threats to humanity 4,5 .Medicine is one of the sciences that have been beneficially affected by improved accuracy of the diagnosis, epidemiology, staging-severity, prognosis, and treatment of numerous diseases 6,7 .In addition, AI has played a crucial role in the discovery of new drugs and revolutionized medical education, medical information, and medical practice.It is obvious that the aim of AI in medicine is to promote human welfare, equality and advanced healthcare and preserving the autonomy security and transparency of the data of individual patients.However, the development and implementation of Ais present significant risks in the field of practicing medicine as well as in biological research 8,9 .The following risks and dangers will be discussed: 1) as exist at present, misuse of the data, risks during the development and/or implementation of AI, risks of medical education; 2) as may present shortly, replacement of various medical tests and examinations or medical specialties; and 3) when AI may become stronger (Super AI) than human intelligence.Finally, a proposal based on Medical Ethics will be presented for AI to remain beneficial to humanity in the future 10 . Risks from AI in medicine: presentData AI systems use specific algorithms that need large datasets to improve their accuracy (specificity/sensitivity).This process is at great risk, as far as the security, privacy, and confidentiality of the sensitive individual patient's data, is concerned 11 .Today, the danger of hacking of such datasets has increased tremendously due to the interest of the pharmaceutical or insurance companies.In addition, the hacking of medical files could be a part of a cyberattack 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. Development of AI algorithms in medicineAn inaccurate medical algorithm could affect a large number of patients.

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