Artificial intelligence (AI) and big data have been around for a while, but over the last few years have become dominant technologies in almost every industry. In the healthcare IT sector, AI and big data analysis tools are expected to play an expanding and major role, touching every part of health care.
According to Research and Markets, the computer vision market is expected to see a 47.54 percent CAGR. A segment of $3.62 billion is projected to increase at a compound annual growth rate of more than 47 percent and will reach $25.32 billion by 2023.
Much of this growth will be credited for the use of computer vision for independent vehicles, increased reality, manufacturing, and healthcare-based applications. As a result, it will play a significant role in bringing market worth.
The healthcare sector is flourishing at a quick pace globally, which is indeed a good sign. Managing patient health and research into preventing, managing and curing diseases are also increasing continuously. With such high demands, technologies to help are evolving or being developed.
The healthcare sector, which prioritizes treating patients and addressing their concerns, has in the past considered cybersecurity an afterthought.
AI and Big Data: Boosting Healthcare Performance
Hackers increasingly are targeting health care, which makes cybersecurity the biggest concern for every organization within the sector. One report noted that 90 percent of hospitals surveyed have experienced a cyberattack within the last five years.
AI and big data can help. Along with enhancing administrative duties and patient healthcare outcomes, AI and big data are great tools to cybersecurity and safety of the patient data.
The emerging threats against healthcare sectors could effectively be detected through the machine learning apps. Most of these apps are designed to indicate new malware via using historical data and the malware patterns.
However, there are certain barriers for complete implementation of this technology in healthcare IT. HIPAA regulations protect the rights of accessing huge data sets which are necessary for automating the process of this app technology.
Efficient Responding to a Security Breach
As compared to the conventional patterns, the AI could more efficiently eradicate the threats after a security breach. AI is capable of continuous and automatic monitoring of network behavior so that anomalies within the network could be marked.
As soon as the threat is detected, the issue is forwarded for human insight or an autonomous action could be triggered to minimize the impact of a breach. For instance, with the help of AI-powered automation, traffic can be segmented defensively to separate sensitive data based on certain security protocols automatically.
To Eradicate Attack Risk From Medical Devices
Smart devices are more prone to hacker invasion as compared to conventional medical tools. AI could be helpful in this area, too. It is a significant AI advantage, as it is reported that currently there are 3.7 million connected medical devices being used in the United States.
From pacemakers to insulin pumps and other medical electronics, internet-connected devices are providing huge medical benefits to patients. However, these devices are also prone to attack, with millions being publically discoverable.
AI could be used to implement data encryption and for malware detection in these devices, particularly the automatic indication of malware—which would free healthcare organizations from relying on manufacturers to ensure security is updated.
Are Potential Security Detriments Being Neglected?
There are many healthcare security and compliance challenges that, unfortunately, can’t be resolved by AI and big data. Beyond security risks are challenges such as a cultural change in human behavior, creative solutions in investigations and balanced human ethical judgment that AI and big data can’t resolve.
The advancements and ease provided by the AI come with security challenges. Implementing AI and big data to their full potential is not an easy task. To maintain the accuracy and to avoid potential cybersecurity threats, manufacturers and healthcare providers need to work together.
Healthcare use of AI and big data won’t continue to grow if the risks are not taken seriously.
Critical Training in Healthcare
Security leaders must train healthcare staff and physicians to eradicate the chances of vulnerability exploitation. Many security experts and analysts believe there is a need for bidirectional education.
First, the security leaders should understand and experience the routine tasks performed by individual healthcare providers. Those insights could be used to enhance personal training and to determine the most vulnerable access points for threats.
Protecting Healthcare Data
In AI-driven health care, data is the most important element. Practitioners also can use big data to access vital patient data and to optimize treatment through machine learning technology.
However, recent attacks on health care indicate data loss due to the use of AI-assisted technology in hospitals. For instance, the most prominent ransomware attack in 2017, WannaCry, also attacked the NHS, affecting individuals’ private health data and causing destructive consequences.
Such cybersecurity attacks can be mitigated with the appropriate security protocols, which are optimized according to AI technology and big data advancement. Also, organizations must have a secure infrastructure for processing patient information.
Lack of Appropriate AI and Big Data Integration
As mentioned before, data is the lifeblood of AI and big data foundation, and the increasing amount of data requires appropriate integration simultaneously. Unfortunately, most healthcare data remain dispersed, which undermines the efficiency of AI and big data, thanks to a lack of organized and integrated datasets.
The common shortcomings associated with AI and big data could be easily managed if there is adequate recognition for built-in biases and errors in AI. Engineers could design adaptable, dynamic AI algorithms focusing on integrating new data and enhancing efforts to organize better integrated, broad-based datasets.