- AI systems must ensure fairness and inclusivity by avoiding biased treatment of patient data and promoting diversity in their development teams, while also prioritising reliability, safety, and robust privacy measures to safeguard sensitive health information.
- Additionally, AI should uphold patient autonomy and ensure equitable access to its benefits, particularly for underserved communities, to prevent exacerbating healthcare disparities.
The integration of artificial intelligence into healthcare holds immense promise for improving patient outcomes, enhancing diagnostic accuracy, and streamlining administrative processes. However, alongside these advancements come significant ethical considerations that must be addressed to ensure AI is used responsibly and equitably.
Fairness and inclusivity
AI systems must not only handle patient data in a balanced and equitable manner but also avoid treating similar groups of people differently. To eliminate bias in both research and clinical practice, inclusivity should be a core principle in the design of AI systems. To foster fairness and inclusivity, engineers, developers, and coders should not only exhibit inclusive behaviours but also hail from diverse backgrounds with a range of experiences. The design and development of ethical AI technology systems must involve input and feedback from individuals with research, clinical, administrative, and operational backgrounds, as this will mutually benefit patients and facilitate the adoption of such technologies.
Also read: AI governance: Ethical, legal, and global imperatives
Reliability and safety
Another critical ethical concern is the reliability and safety of AI technology, given its potential impact on research and clinical decision-making, including differential diagnosis. For instance, AI applications in emergency departments might encompass critical and time-sensitive tasks such as clinical image analysis, intelligent monitoring, predictive algorithms for clinical outcomes, and population and social media analyses for public health surveillance. To facilitate the widespread adoption of AI technologies that could improve patient outcomes, additional research studies are necessary. Moreover, the absence of publication and reporting guidelines for AI in healthcare exacerbates the challenges in evaluating and adopting these technologies.
Also read: What are some ethical considerations when using generative AI?
Privacy and security
AI technology utilised in research and clinical practice must comply with the privacy and security requirements for patient data. Adhering to these standards is critical for legal compliance and ethical conduct. An AI technology system must incorporate robust privacy and security measures, given its access to vast amounts of protected health information and data, which are essential for improving human health and well-being. When contemplating the use of AI in healthcare, it is essential to utilise technology that employs strategies and techniques such as homomorphic encryption, methods to separate data from personally identifiable information, and safeguards against tampering, misuse, or hacking. These protective measures available today will enhance the privacy and security of a patient’s data, while enabling actionable insights for researchers and clinicians.
Impact on patient autonomy
The use of AI in healthcare can also affect patient autonomy. Automated decision-making tools might influence patient choices without adequate explanation or consent. Patients have the right to understand the basis of medical recommendations and to make informed decisions about their care. It is essential to maintain patient-centric approaches that empower individuals to participate actively in their healthcare decisions, even as AI technologies become more prevalent.
Equity and access
The last element to consider ethically is the equitable distribution of AI benefits. While AI has the potential to improve healthcare access and quality, disparities in access to these technologies can exacerbate existing inequalities. Rural and underserved communities may not benefit equally from AI advancements, further widening the gap in healthcare outcomes. Policymakers and healthcare providers must ensure that AI technologies are accessible to all segments of the population, regardless of socioeconomic status.
