In the last two decades or so, artificial intelligence (AI) has made significant breakthroughs and has become an integral part of healthcare, transforming the way diseases are managed, diagnosed, and treated…
From clinical decision-making to personalised treatment options, AI has been leveraging ML (machine learning) techniques and advanced algorithms to deliver novel capabilities. However, the critical question remains – how far is too far when it comes to ethical considerations of the use of AI in healthcare?
What about potential AI bias, data privacy and security, informed consent, and maintaining patient autonomy? This article does a deep dive into how AI has made a groundbreaking shift in the healthcare sector, the ethical dilemmas it’s given rise to, and how these are being and can be addressed.
Data Privacy Issues: Protecting Patient Data
Healthcare providers, biopharma companies, insurance agencies, and other stakeholders don’t simply store the usual components of consumer data like preferences, demographics, and the like. Unsurprisingly, a patient’s healthcare file also adds data on existing health concerns, symptoms, and treatments.
Even well-established healthcare privacy regulations like HIPAA (Health Insurance Portability and Accountability Act) don’t cover tech companies. Such concerns usually protect only patient health data that comes from organisations providing healthcare services, such as hospitals, medical clinics, and insurance companies.
That is one of the chief reasons for ethical concerns related to collecting and handling patient data in AI-driven healthcare. For instance, the security and confidentiality of patient information should be ensured to protect patients from unauthorised access or any negative impacts of data breaches.
Moreover, it shouldn’t be assumed that patients know about the implications of sharing their data with AI systems. Rather, they should be explicitly told about the same, while also being given the option to opt out if they wish to.
Addressing Algorithmic Bias
AI models have been highly useful in uncovering hidden disease patterns by analysing highly diverse clinical datasets. They are able to not only identify, characterise, and predict illnesses, but could even potentially alter the path of severe diseases.
However, most systems rely heavily on historical data fed into the initial algorithm for generating new methodologies and protocols, which could result in severe inherent biases in this data becoming more pronounced and causing flawed results. These biases could impact AI healthcare algorithms significantly, exacerbating already existing socioeconomic and racial disparities.
For instance, genetics testing companies do more than just tell you about your ancestry, providing valuable information about one’s genetic predisposition to certain health risks. While this is great for using this data to take preventative action and plan for the future, it raises flags in the insurance industry. Among other things, insurance companies could misuse this information to bias selection processes and charge higher premiums.
Moreover, since certain demographics are more predisposed to certain diseases, there’s a huge risk of incorrectly-trained insurance algorithms demonstrating overtly sexist and racist behaviour if left to their own devices. This could deepen the already existing problem of unequal healthcare access.
Patient Autonomy: Informed Decision-Making
Patient autonomy is the cornerstone of healthcare and medical ethics, having become all the more critical in the age of AI. However, there are several challenges involved in obtaining informed consent in AI-driven healthcare and ensuring patient autonomy.
For instance, older patients with multiple chronic health conditions could be sceptical of computers and technology, rejecting AI-based modalities outright. Ethical systems should first educate the patients about the benefits of AI tools before initiation. Obtaining informed consent for AI systems requires concise and clear communication about how it will be used and what the potential impact could be.
After all, patients might not entirely comprehend how their data will and could be used, raising concerns about data privacy. Additionally, there should be an option for patients to opt out of these modalities if they want to. This transparency will increase patient trust, giving them the autonomy to decide their healthcare needs.
Finding The Right Balance
There’s no denying the massive positive strides that AI has made in the healthcare realm, whether it’s something as basic yet critical as ensuring safe deliveries to something as complicated as revolutionising genomics. In the end, a balance needs to be struck between healthcare AI advancements and the ethical considerations surrounding them. As powerful as AI is, companies should remember to prioritise human interactions as they drive the very essence of healthcare.
Since healthcare professionals make up a major part of this system, they need to ensure that AI-driven healthcare decisions align with the best interests of the patients. They need to critically evaluate the recommendations given by the AI tools to ensure that they align with their clinical expertise. Of course, above all, we require diligent and purpose-driven development of ethical regulations and frameworks to address the rapidly changing and evolving AI in the healthcare landscape.
