All that glitters is not gold: the use of artificial intelligence in the medical field is not without flaws and risks.

The democratisation of artificial intelligence has been met with enthusiasm in many industries, and the medical field is no exception. Machine learning models and AI technology are increasingly finding uses in healthcare devices and medical applications to support diagnostics and provide an enhanced experience for patients.

On one hand, there is significant progress; on the other, there are major concerns. The use of AI in medical technology is nothing short of a revolution: it is estimated that the market size will reach $120.2 billion by 2028. But there are looming questions, such as if AI truly possesses the capability to accurately predict medical conditions or efficiently diagnose patients and offer a fully personalised approach, or if the industry will struggle with hallucinations, privacy violations, and bias that provokes inequality. And, of course, if the application of AI-based machine translation technologies can be used to our advantage, to gain efficiency at a better cost and timeline.

Benefits of machine learning in healthcare and localisation

Thanks to AI’s computational capabilities and its ability to process copious amounts of data at high speed, the use of AI has been welcomed in the medical industry, and consequently in medical localisation. Here are some subareas where it can make a substantial difference:

• Disease detection and diagnosis: by offering support for clinical reasoning, AI helps detect pathologies and speed up diagnosis by analysing patient data, in the same way as similar technologies help us to evaluate if content is suitable for machine translation processing or for detecting errors.
• Personalisation: treatment efficiency can be increased by tailoring AI to optimally align with individual patient needs, precisely because it helps us to adopt a personalised approach in providing transcription or machine translation services.
• Use in medical imaging: AI’s image recognition applications can be applied to the medical field, thereby enhancing the interpretation of scans and tests.
• Cost savings and automation: the automation of processes helps the healthcare system become less overloaded and less expensive so that we can organise processes and tasks into efficient workflows to craft faster processes.
• Improved patient outcomes: faster and more accurate diagnoses, coupled with the advent of precision medicine, offer a promising approach to disease treatment and prevention by considering individual genetic variations, environment, and lifestyle. In a similar fashion, we can achieve the most accurate results for our customers by creating and seamlessly integrating tailor-made glossaries.

Risks of automation and uncontrolled AI technology in healthcare and localisation

While the benefits are encouraging, the progress promised by AI is not devoid of risks, errors, and data breaches that need to be tackled not only by healthcare providers but also by Language Service Providers to guarantee the best quality results depending on the project.

1. Ethical implications of AI decision-making: achieving ethical decision-making requires a collaborative effort for establishing guidelines that prioritise fairness, transparency, and accountability.
2. Appropriate machine training: emphasising the importance of high-quality training for machines is crucial; poorly trained machines can provide results that are counterproductive for doctors. Analogously, machine translation engines have to be properly trained to avoid errors and improve output. Hallucinations and biases: AI can generate responses containing false or misleading information, and biases can be introduced based on the material used for training. Machine translation, if not used wisely, can lead to similar results. It is extremely important to thoroughly evaluate the extent of human work needed to reach the expected results (that’s why we provide 4 levels of postediting depending on the quality level of the requested project).

CPSL MTPE workflows offer 4 different levels of quality.

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3. AI integration/infrastructure: integrating AI with existing IT systems and infrastructures can pose challenges.
4. Privacy and data security: cybersecurity risks in healthcare are a significant concern, particularly with AI-powered devices, which is why it is important for MedTech manufacturers to ensure the regulatory readiness of their medical devices, particularly as the regulatory landscape continues to evolve, not only in their own country but in their target markets too. In the US, for instance, regulatory bodies such as the FDA and legal frameworks like the one provided by the HIPAA, as well as government agencies such as the FTC, play vital roles in ensuring safety and protecting patient information.

The FDA oversees the safety and effectiveness of AI-driven medical products, which are regulated based on intended use and the risk levels to patients. AI-enabled software undergoes FDA review according to its risk classification, similar to other medical devices.

The FTC recently approved a resolution allowing compulsory processes in non-public investigations related to products and services that use or claim to be produced using artificial intelligence (AI) or that claim to detect its use.

The Health Insurance Portability and Accountability Act of 1996 (HIPAA) also plays a pivotal role in protecting patient information. It sets national standards for safeguarding sensitive patient health data and requires that healthcare entities implement robust security measures. Compliance is enforced through audits, with significant penalties for violations, thereby highlighting the HIPAA’s crucial role in maintaining the integrity and confidentiality of patient health information.

Cybersecurity is not devoid of risks in the field of localisation either, although these risks can be eliminated with the right setup and expertise. With the use of high-tech security systems, encrypted information, and adherence to strict protocols dictated by ISO regulations, medical localisation can be performed ensuring that sensitive information is safely transmitted and stored.

How to balance innovation and security using AI in the medical field and for localisation projects

Balancing innovation and security in AI for the medical field requires a harmonious approach that prioritises patient security while embracing cutting-edge technologies. Only the rigorous testing and validation of AI systems can ensure reliability without stifling progress. Collaboration between technologists, clinicians, and regulators fosters an environment where innovation thrives within secure boundaries.

CPSL believes that this approach to putting patients first in the implementation of technology for healthcare has to be followed equally when considering technology as a solution for our clients: their needs and protection come first too. We are of the opinion that this strategy is needed in any type of operation, which is why we study each project and the particularities of an industry to provide the best workflow and technologies to be applied.

CPSL strives to ensure the integrity, availability, and confidentiality of data. Considering our extensive experience in localising remote patient monitoring tools and wearable devices, while adhering to strict safety protocols and IT security management rules, we are the ideal partner to help you navigate the use of AI in your medical devices. Contact the CPSL team if you’d like to excel when implementing AI in your medical devices and beyond.