Abstract

Electronic healthcare technology is widespread around the world and creates massive potential to improve clinical outcomes and transform care delivery. However, there are increasing concerns with respect to the cyber vulnerabilities of medical tools, malicious medical errors, and security attacks on healthcare data and devices. Increased connectivity to existing computer networks has exposed the medical devices/systems and their communicating data to new cybersecurity vulnerabilities. Adversaries leverage the state-of-the-art technologies, in particular artificial intelligence and computer vision-based techniques, in order to launch stronger and more detrimental attacks on the medical targets. The medical domain is an attractive area for cybercrimes for two fundamental reasons: (a) it is rich resource of valuable and sensitive data; and (b) its protection and defensive mechanisms are weak and ineffective. The attacks aim to steal health information from the patients, manipulate the medical information and queries, maliciously change the medical diagnosis, decisions, and prescriptions, etc. A successful attack in the medical domain causes serious damage to the patient’s health and even death. Therefore, cybersecurity is critical to patient safety and every aspect of the medical domain, while it has not been studied sufficiently. To tackle this problem, new human- and computer-based countermeasures are researched and proposed for medical attacks using the most effective software and hardware technologies, such as artificial intelligence and computer vision. This review provides insights to the novel and existing solutions in the literature that mitigate cyber risks, errors, damage, and threats in the medical domain. We have performed a scoping review analyzing the four major elements in this area (in order from a medical perspective): (1) medical errors; (2) security weaknesses of medical devices at software- and hardware-level; (3) artificial intelligence and/or computer vision in medical applications; and (4) cyber attacks and defenses in the medical domain. Meanwhile, artificial intelligence and computer vision are key topics in this review and their usage in all these four elements are discussed. The review outcome delivers the solutions through building and evaluating the connections among these elements in order to serve as a beneficial guideline for medical electronic hardware security.

Highlights

  • Recent advancements and progress in computer science along with the demands for great computing performance makes artificial intelligence (AI) a promising candidate for engaging in different applications and benefitting modern society [1,2,3,4,5,6]

  • We focus on: the emerging security weaknesses and threats in medical applications based on the known medical errors; the application of artificial intellifenses; and (c) security of AI/computer vision (CV) elements

  • The software, hardware, and the transmitting/processing information by the IoMT devices are all at risk by different kinds of threats and have security weaknesses that can damage the function of devices and/or the transmitting/processing data, causing intentional medical errors

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Summary

Introduction

Recent advancements and progress in computer science along with the demands for great computing performance makes artificial intelligence (AI) a promising candidate for engaging in different applications and benefitting modern society [1,2,3,4,5,6]. The major growth in machine learning, deep learning, provide high-performance algorithms and systems that understand and model medical data through multiple layers of transformations These algorithms help in extracting and learning features from data automatically at different abstract levels. The systems have unique characteristics: (a) plasticity, causing changes in system performance through learning and need of creating new concepts about the timing of learning and assignment of responsibilities for risk management; (b) unpredictability of system behavior, in response to unknown inputs due to the black box characteristics precluding deductive output prediction; and (c) need to assure the characteristics of datasets to be used for learning and evaluation Due to this merit of AI/CV-based medical applications, all aspects of this research direction should be studied comprehensively. AI/CV Technologies in Medical Applications Cyber Attacks and Defenses in Medical Domain

Medical Errors
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