Problematic aspects of medical artificial intelligence. Part 1

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Abstract

BACKGROUND: Artificial intelligence, like medicine, is a dynamically developing field that can be considered both a science and an art. This makes it much more difficult to use artificial intelligence compared to other technologies that come with a user manual.

Research and start-ups in the field of medical artificial intelligence are rapidly multiplying: the popularity of smart mobile devices, networked applications and remote digital services is growing. However, there are still some problems that complicate the widespread use of artificial intelligence algorithms in everyday clinical practice. The reasons for this are the high cost of operating neural network platforms and the limited qualifications of some medical professionals in the field of computer technology. These are only temporary difficulties, though, which should and will be gradually resolved.

CONCLUSION: This article focuses on the most sensitive points that are currently hindering the accelerated progress of machine learning in healthcare.

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About the authors

Vitalii A. Berdutin

State Research Center — Burnasyan Federal Medical Biophysical Center of Federal Medical Biological Agency

Email: vberdt@gmail.com
ORCID iD: 0000-0003-3211-0899
SPIN-code: 8316-7111

MD, Cand. Sci. (Medicine)

Russian Federation, Moscow

Tatyana E. Romanova

Privolzhsky Research Medical University

Email: drmedromanova@gmail.com
ORCID iD: 0000-0001-6328-079X
SPIN-code: 4943-6121

MD, Cand. Sci. (Medicine)

Russian Federation, Nizhny Novgorod

Sergey V. Romanov

Privolzhsky District Medical Center of the Federal Medical and Biological Agency

Email: director@pomc.ru
ORCID iD: 0000-0002-1815-5436
SPIN-code: 9014-6344

MD, Dr. Sci. (Medicine)

Russian Federation, Nizhny Novgorod

Olga P. Abaeva

State Research Center — Burnasyan Federal Medical Biophysical Center of Federal Medical Biological Agency

Author for correspondence.
Email: abaevaop@inbox.ru
ORCID iD: 0000-0001-7403-7744
SPIN-code: 5602-2435

MD, Dr. Sci. (Medicine), Professor

Russian Federation, Moscow

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2. Fig. 1. DeepLocker — Artificial Intelligence-Powered Concealment: 1 — Target Class Concealment (does not reveal what kind of target is looking for (e.g. person, organization); 2 — Target Instance Concealment (if the target class is an individual, it does not reveal who it is looking for); 3 — Malicious Intent Concealment (payload is fully encrypted concealing how the final attack is executed)

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