Securing AI-powered medical devices is critical for the future of healthcare

09/15/2021

The medical and healthcare industry is adopting the technology at its peak, and AI-powered medical devices are nourishing their way. Therefore, it is worthwhile to invest in artificial intelligence, machine learning, augmented reality, or mixed reality to enlighten the in-depth treatment and research practices. 

In comparison to other industries, the healthcare business takes a long time to assimilate innovation. 70% of health care systems have yet to implement a structured program. According to the latest survey, 68 percent of health system leaders aim to increase their investment in AI during the next five years. It will assist them in achieving their business goals and the strategic plan. The expenditure is likely to be substantial; by 2028, the worldwide AI in the healthcare sector is projected to be valued at $120.2 billion.

Fraud detection, voice-assisted charting, identification, virtual outpatient monitoring, and other administrative and clinical application factors are just a few of the possibilities for technology in healthcare. In addition, AI has extraordinary potential for linked medical devices and telemedicine. Both are essential components of the Internet of Medical Things (IoMT) because it allows quicker assessment, ingestion, identification, and selection.

Google, for example, just released a breakthrough dermatological app driven by AI. It employs image recognition algorithms to give professional, tailored assistance by recommending various skin problems based on images supplied by patients.

To detect and cure cancer patients, a Philips gadget uses AI findings. Furthermore, due to an AI-powered, automated real-time advanced alarm score system, Amwell's new telehealth technology allows clinicians to get signals on their patients' health conditions. 

A higher risk of securing IoMT

While AI has a great deal of ability in healthcare, it also has certain limits. The main issue that hasn't gotten much attention is how to protect integrated AI-powered medical devices. In addition, it has a massive risk from more common and complicated cybersecurity threats. However, AI can help a lot, but at the same time, the risk factor will not be neglected. It can be the reason to exploit and manipulate the system. 

For instance, a similar algorithm used AI-powered medical devices to further consistently and swiftly identify cancer. Yet, a malicious attacker might leverage it to assault that device. To give you an example, a 2019 research from Ben-Gurion University showed how AI-savvy cybercriminals could alter CT and MRI findings of lung cancer patients, giving them total authority over the quantity, size, and position of tumors. 

As a result, two very different radiologists and AI systems have issues distinguishing between the changed and accurate images. This type of hacking has the possibility to have a negative influence on patients' health. Not only this, but ransomware attacks, data hacking, insurance fraud, and other problems occur for both patients and doctors.

Bad actors generally only require an emulator to hijack AI from a device, software, or any hardware. Emulators allow one computer system to function like another — and a snippet of programming from the system being attacked.

For the related sectors, cyber risks are unquestionably a severe and growing problem. Cyberattacks on IoT devices ascended substantially in 2019, with over 2.9 billion incidents recorded. In the coming ten years, it's expected that 50 billion medical devices will be integrated into medical institutions. As a result, it will be providing the IoMT (Internet of Medical Things) business a more attractive target for hacker attacks.

Regardless of the consequences of a cyberattack, research reveals that several manufacturers cannot implement Security by Design. It is because of a lack of understanding and expertise. Merely 13% of IoMT leaders consider their company is exceptionally prepared to minimize uncertainties, as per the latest survey taken, while 70% say they are only slightly equipped at best.

How can AI-powered medical devices be kept safe?

Although AI and machine learning algorithms are costly and time-consuming to develop, they are simple to reproduce once completed. As a result, permitting and prohibiting access to a system is a crucial initial step in defending devices from attackers. Bad actors require accessibility to the device's information, or a digital counterpart, for their algorithms to interpret to assault an AI-based system properly. 

Machine learning 'lifting,' or data replication, is obtainable in most instances. The automated system responds to hundreds of queries without being identified as a security risk. The bad actors may merely utilize AI to recreate the system or program by answering these questions, even whether it's a complicated medical device software or procedure.

As a result, restricting access is critical, and it entails the following steps:

Accessibility Control

Create access control levels, including logins and passwords, to verify that only those with permission to see the data may see it. It's the same as placing a padlock on a doorway.

Anomaly detection

Anomaly detection can be included to monitor unexpected use tendencies inside the primary interaction stream. This type of protection identifies unusual activity so that the business may respond adequately. For example, a bot generating a large number of queries may constitute a bizarre trend. Security specialists can discern the difference between someone who is genuinely using the equipment or device and someone else who is probing it in this approach.

In addition to authentication mechanisms and anomaly detection, it's critical to protect linked devices against decrypting. Developers can employ various strategies and methods to make the software in their devices impossible to emulate. Thus, it is ultimately enhancing their security. 

These countermeasures should be integrated into smart devices during the Research and development stage, as it is far more challenging to include cybersecurity after a product has been released. Moreover, healthcare technology companies must demonstrate that their AI-powered medical devices are regulatory-ready, especially as the regulatory environment begins to adapt and change. 

Approximately 80 percent of MedTech professionals feel that regulatory compliance is the most critical business advantage of adopting a robust cybersecurity strategy. However, only four out of ten respondents said they were highly aware of or informed about upcoming EU and US cyberattacks.

Using an evaluation tool can assist companies in evaluating their regulatory readiness and identifying any flaws. In addition, it addresses all the issues before the product is released to the official. Thus, machine learning has the potential to be utilized for both beneficial and malevolent reasons. 

As more AI-powered medical devices become available, malicious hackers will proliferate. As a result, manufacturers must include comprehensive protection measures in the design process more than ever before to assure the integrity and security of healthcare organizations, practitioners, and patients.

Cyber threats and vulnerabilities

Medical devices that use Artificial intelligence and Machine learning will need to be more cautious regarding cybersecurity, especially if the devices are interconnected or otherwise exchanging health data remotely to meet new legal standards for authentic measuring performance.  

Private record keeping and transmission is a severe cyber risk to healthcare systems and AI-powered medical devices. Therefore, devices should be built to maintain adequate cybersecurity at all levels, mainly if collecting or altering information might have influenced how the new devices work, as with nearly all AI/ML applications.

Digital health technology as a need

Digital health breakthroughs aim to reduce time, improve productivity, and merge technology in previously unheard-of manners in healthcare coverage. These breakthroughs might bring together new medicine and the internet of things (IoT), blockchain and electronic medical records (EMRs), and medicine and augmented reality (AR).  

Telemedicine technology to enhance connectivity with doctors and patients is one example of an Internet-of-things implementation. It's owing to a reduction in the risk of contracting infectious diseases, as well as a variety of intelligent wearable sensors that may gather information at the human level.

As a consequence of COVID-19, the market for telemedicine services increased, with a higher number of professionals depending on advanced technologies to provision digital healthcare to clients. As a result, groundbreaking Internet of Things (IoT) healthcare applications tend to develop.

Data comparability is a constant problem given the immense volumes of data generated from several aspects that store and code material uniquely. The evolution of digital health and wellbeing can also help healthcare professionals. 

Digital health offers the ability to improve people's detection and control of medical conditions while preventing illness and lowering healthcare expenditures. It may also personalize medication for each patient.

Conclusion

It is not wrong to say the future will rely on big data technology extracted from AI-powered medicine devices. But on the other hand, the security threats are not overwhelming to control. Therefore, Healthcare and AI-powered medical devices can not survive if the system cannot secure them and provide healthy cybersecurity over the server. 

Taking preventive measures by building authenticated software would help escape from cyberattacks, ransomware, and breach data. Holding patients' data is as risky as their lives. The destructive attackers can disturb the whole device by just using emulators. The tech-savvy practitioner should invent the concept of securing data at the level of emulators so it will be integrated to be hacked with these types of security issues.