How to Make AI Ethical in Healthcare

A landmark study by the journal of science in 2019, Science reported that an algorithm designed to predict healthcare needs for over 100 million individuals was found to be biased against Black patients. Picture being a patient depending on an AI diagnostic tool, only to find out that it’s twice as likely to misdiagnose your condition compared to other groups. Or imagine a hospital using your private health data to train algorithms without asking for your consent. Incidents like these break the trust the public places in healthcare systems.

Recent studies show that many people are concerned about the ethics of AI in healthcare. One survey revealed that 78% of patients are anxious about privacy breaches, while another indicated that only 13% of healthcare organizations have established comprehensive AI ethics frameworks. 

In healthcare today, artificial intelligence (AI) has become a real and present part of the industry, moving beyond futuristic predictions. The integration of AI into healthcare brings about distinct challenges as well as significant opportunities. While artificial intelligence has the ability to improve the way healthcare is provided, it also comes with the responsibility to ensure it is used in a safe, ethical, and fair manner.

The attention of those providing the AI-powered solutions in healthcare should not solely be on the technological advancements but on the ethical principles guiding these innovations. Ethical AI is not just about meeting legal requirements, but about ensuring that healthcare remains trustworthy, safe, and just. So, how can we ensure that AI in healthcare is used ethically? There are several key considerations to focus on: 

Data Protection and Privacy 

Protecting patient data is of the highest priority in healthcare. The American Hospital Association reported in 2020 that the healthcare sector generated over 2.3 trillion gigabytes of data, with an annual increase of 47% in data creation. Upholding patient privacy is not just about following regulations, but is also key to building trust and maintaining the integrity of healthcare services. Every piece of patient data is more than just numbers—it represents deeply personal health information. Handling this data ethically goes beyond complying with laws such as HIPAA; it’s about respecting individual privacy and dignity.

Between 2018 and 2022, the U.S. Department of Health and Human Services saw a 93% rise in large data breaches (from 369 to 712), many caused by ransomware attacks. With more data being produced and technology becoming more advanced, cyberattacks on healthcare organizations are expected to increase. These attacks can jeopardize patient privacy, delay treatment, reduce the quality of care, and harm the reputation and financial stability of affected organizations.

Healthcare records are filled with sensitive personal information, which is often targeted for financial gain by unauthorized users. Healthcare organizations are particularly vulnerable, but the risk is often heightened by other factors. The constant need for access to patient data may lead to weaker security protocols. Furthermore, outdated technology and systems are a fairly common problem in healthcare, which creates gaps in security that cybercriminals can exploit. Many organizations also lack the necessary resources and expertise to establish strong cybersecurity protocols.

Maintaining Human Oversight

Human oversight plays an essential role in the ethical implementation of AI in healthcare. Human agency refers to the ability of users and organizations to comprehend, interact with, and control AI-powered systems. It is crucial that AI does not operate independently but complements human expertise and decision-making. For instance, Artificial intelligence can provide suggestions based on large-scale data analysis, but the final decision should rest with the healthcare professional.

This ensures that AI supports rather than replaces human expertise, where healthcare providers bring their unique knowledge of a patient’s needs, context, and history—qualities AI cannot replicate. It also reduces the risks of becoming overly reliant on technology, which may fail to consider the complexities of individual patient situations.

To ensure proper oversight, the organizations developing AI systems can:

·       Establish strong traceability of AI decision-making processes, enabling the organization to understand how decisions are made.

·       Regularly assess the performance of AI systems to ensure accuracy, fairness, and adaptability to new data.

·       Create dedicated teams to monitor the ethical aspects of AI, particularly its impact on patient care and privacy.

The balance between AI and human roles is essential. While AI can perform tasks like data analysis and pattern recognition efficiently, it cannot replace the empathy and ethical judgment that healthcare professionals bring. The goal should be to use AI as a tool that enhances human capabilities, not one that diminishes the role of healthcare workers.

Tackling Bias in AI Systems

AI systems in healthcare may unintentionally introduce bias and unfairness, often originating from non-representative data used in training models. For example, when AI systems are trained mostly on data from certain groups, their performance might be less accurate for groups that are underrepresented. A study from 2021 found that AI algorithms used to interpret chest x-rays were more likely to miss diagnoses in underserved populations. This results in discrepancies in AI-generated outputs, potentially affecting the health outcomes of these marginalized groups.

Bias can also emerge in the way AI algorithms analyze data. For example, an algorithm designed to predict heart disease may mistakenly place too much weight on a patient's body mass index (BMI) while overlooking other health indicators. This bias comes not from the data itself but from the way the AI system interprets and prioritizes certain data points.

The impact of such biases is significant in healthcare, where equality should be a paramount. Understanding these issues is essential for developing AI systems that are fair and unbiased. It’s important to recognize that AI models are influenced by the biases present in their data and how the data is interpreted by artificially intelligent software.

Ensuring Transparency and Traceability

Transparency along with traceability is a key element in deploying AI in healthcare and are central to ethical AI practices.

Transparency means that users should be able to understand how an AI system works. This involves providing clear explanations about how the system makes decisions, the data it uses, and its limitations. Transparency builds trust among users. For instance, when artificial intelligence is used for medical documentation, both healthcare professionals and patients should have access to details about how the tool processes data and reaches conclusions. Information about the algorithms, the types of data used, and how this data is translated into healthcare insights should be accessible.

Traceability refers to the ability to track the steps taken by an AI system to reach its decisions. This includes understanding the lineage of data and the context in which it was used. Traceability is critical because healthcare decisions can have serious consequences on patient outcomes. It ensures that AI decisions are not just accurate but also explainable and accountable.

Transparency is the foundation of traceability. To ensure traceability, we need to fully understand the algorithms used in AI systems, the logic behind decisions, and how the data is processed to generate outcomes. Documenting how data is handled and fed into AI helps clarify the context and quality of the data, thus improving accountability.

Incorporating traceability in healthcare AI is crucial for ensuring these technologies are not only advanced but also transparent and accountable. As AI becomes more prevalent in healthcare, the emphasis on traceability will play a vital role in building trust, ensuring high-quality care, and maintaining ethical standards.

Implementing Informed Consent

Informed consent is an essential concept in healthcare that continuously adapts to new technologies, including AI. Traditionally, informed consent involves explaining the risks, benefits, and alternatives of a medical procedure to a patient. With AI, this concept extends to ensuring that patients fully understand how artificial intelligence applications are used in their treatment. Patients need to be informed about the data AI systems will use, how this data is processed, and the potential benefits and risks involved.

Healthcare providers must be transparent in their conversations with patients when implementing AI in their care, including any third-party involvement. This ensures patients can raise concerns about the technology, security, privacy, and the reliability of external services. Transparency is crucial in building trust and ensuring that patients understand how their information is handled.

Informed consent also involves ongoing education for both patients and healthcare providers. As AI technologies continue to evolve, so must the understanding of how they are used. By openly communicating with patients and healthcare practitioners about updates and changes to AI systems clinicians and providers can maintain trust and ensure consent remains based on the latest information.

Finally, patient autonomy is central. Patients must always have the option to opt-out of AI-assisted care if they are not comfortable, without compromising the quality of care they receive. This ensures AI in healthcare respects individual preferences and upholds ethical standards.

Integrating AI into Healthcare

The integration of AI into healthcare is a major technological breakthrough, offering countless opportunities to improve healthcare services. However, these advancements must be approached with an awareness of the ethical challenges they present. These challenges include:

·       Ensuring patient data remains secure, especially in light of the growing number of data breaches in healthcare.

·       Addressing biases to ensure AI tools do not reinforce or create new disparities.

·       Promoting transparency and traceability to build trust and allow for the identification of any errors or anomalies in artificially intelligent systems.

·       Providing patients with clear information about how AI is used in their care, including the types of data collected, the system's capabilities and limitations, and the risks involved, and ensuring that patients consent to AI's involvement in their treatment.

·       Maintaining human oversight to ensure that artificial intelligence serves as a complement to human expertise.

What if AI Could Resolve Ethical Challenges in Healthcare? 

In the healthcare industry, the use of AI in tasks that traditionally have minimal impact shows that it's not just about avoiding errors, but about finding ways to make gradual improvements. From managing patient data to aiding in early-stage diagnoses, AI is demonstrating its value by boosting both efficiency and precision. While these changes may be small, they are essential in healthcare, where even minor improvements can have a significant effect on operational performance and patient care.

With President Biden prioritizing AI regulation, the future of healthcare AI is on track for substantial change. The focus on ethical principles, transparency, and human oversight is altering the approach. Healthcare organizations, such as Providence and Cleveland Clinic, are preparing to integrate generative AI to ease the workload for clinicians and enhance patient interactions.

By focusing on collaboration, equity, transparency, and responsibility, AI has the potential to make a major positive impact on healthcare, creating a future where technology supports human health without compromising ethical guidelines.

Final Thoughts 

There are still significant ethical challenges when applying AI in healthcare. AI can reflect human biases related to race, gender, and other factors, which can result in unequal treatment. Regular monitoring is essential to identify and correct these biases. Protecting patient privacy is another critical concern, given the sensitive nature of health data. Effective safeguards must be established to prevent unauthorized access. At times, there’s a compromise between the transparency and precision of AI systems, so it is important to understand how AI makes its decisions as both transparency and accuracy are necessary. 

However, in this shifting environment, AI should be appreciated not for its perfection, but for its ability to consistently improve and refine important elements of healthcare delivery. As AI becomes an integral part of healthcare, it’s essential that all stakeholders take responsibility. Healthcare providers should advocate for AI solutions that prioritize the above-mentioned key considerations i.e., patient safety, privacy, and fairness when implementing AI in healthcare. Patients must stay informed and involved in decisions about AI in their care. Organizations must invest in secure, fair, and transparent AI technologies. And policymakers should create and enforce strong ethical guidelines.

The future is uncertain, but artificial intelligence holds immense potential. By committing to these ethical considerations, we can ensure AI’s power is harnessed for the betterment of healthcare. In this journey where AI and ethics converge, we are not just participating—we are actively shaping the future. As an AI-powered Medical Scribe solution, SOAPsuds commitment to ethical, safe, and trusted AI in healthcare is not merely a philosophy; it’s something we work toward every day.