AI-driven medical software development: ethical considerations

The benefits of using AI-driven medical software development are widely discussed and documented. Experts from academic institutions and speakers during industry conferences often highlight the huge role of AI-powered software. This software assists doctors in making diagnoses and streamlining processes. It also uncovers information in medical images that may elude the human eye.

However, along with advances, it is crucial to address significant ethical considerations to ensure these technologies’ responsible and fair use. In this blog post, we will delve into the key ethical issues related to development of medical imaging software, including:

• bias,
• transparency,
• accountability,
• the implications for patient privacy.

Bias in AI-driven medical software development

AI techniques have the potential to be useful in predicting the results of medical imaging. However, bias — systematic errors – could have both beneficial and detrimental effects when incorporated into medical practice. Although artificial intelligence algorithms can reduce human interpretational cognitive biases, extensive research shows that AI systems may absorb biases. This could have unforeseen effects in clinical settings and impact patient outcomes. [1]

The data used to train AI models may not fully represent the variety of patient populations, resulting in model bias. This could result in healthcare inequalities when the model is applied to different demographic groups than the one it was primarily trained on.

By giving underrepresented groups less accurate diagnoses, bias in AI models has the potential to exacerbate already-existing healthcare disparities. This may result in mishandled or delayed treatment, which disproportionately impacts communities of colour. [2]

Additionally, it is crucial to train AI models using a variety of representative datasets to reduce this risk. Developers must continuously validate and monitor AI systems to identify and address any biases that develop over time. Furthermore, AI tools can be made just and equitable by including a wide set of stakeholders in the development process. These stakeholders should include ethicists, physicians, and representatives of various demographic groups.

The role of transparency in medicine area

Artificial intelligence systems, especially those based on complex deep learning models, are often like “black boxes.” This makes it difficult to understand the logic behind their operation. The lack of transparency can cause problems in medical situations. Understanding the reasoning behind a diagnosis or recommended therapy is critical. [3]

In addition, AI-driven medical software development is often proprietary, and outside researchers typically have limited or no access to training data and performance evaluations. Without reservation, independent auditors, researchers, or government authorities need to be able to access the underlying models and performance data to ensure that developers meet their obligations regarding performance transparency and fairness. Creating a public system for holding AI developers accountable, reliable, and consistent can help build trust. [4]

Creating explainable AI models that provide insights into the decision-making process is crucial. This will improve patient care by assisting physicians in understanding and putting their trust in AI-driven advice. Transparency is also essential to allow patients to make knowledgeable treatment decisions. They must comprehend the role AI plays in their treatment.

Accountability for AI-driven medical software development

Software for medical imaging that is AI-based is growing in strength as a tool in the healthcare industry. It can now offer suggestions for diagnosis or treatment more and more. However, it is crucial to remember that due to the nature of the doctor-patient connection, individuals at the bedside must continue to bear accountability and responsibility for any mistakes made in risk assessment or AI-based medical diagnosis. [5]

Who is the blame if an AI system makes a mistake that results in an incorrect diagnosis or improper course of treatment? Who is responsible for its implementation? The healthcare provider, the developer of the AI system, or the institution?

There is no doubt, the transparency of AI-based systems operating in healthcare is a key issue. It suggests three considerations. Firstly, clinicians should have a good understanding of the intended use of an artificial intelligence system. Secondly, to use the software appropriately, physicians must also be aware of how a particular solution performs for the population they treat.

Finally, doctors need to be aware of the dangers of automated bias. Automation bias happens when people start accepting AI-driven medical software’s results without question, disregarding uncertainties or prediction errors as necessary. In general, clinicians should apply a reasonable amount of scepticism when it comes to the results of the systems they employ. [6]

In sum, to define accountability in the application of AI in healthcare, it is imperative to provide explicit policies and norms. These rules should outline the duties of the many parties involved in the creation, implementation, and use of AI systems, along with how to handle mistakes. Healthcare practitioners need training in the proper use of AI technologies, including understanding its limitations and knowing when to disregard AI advice.

The implications for patient privacy

Medical imaging AI systems rely on vast volumes of data, frequently containing private patient information. A crucial ethical factor is making sure that this data is both secure and private.

Genetic information, medical records, and real-time monitoring data are examples of the sensitive personal health data that AI-enabled systems must gather and store. There are concerns about people’s privacy when this data is widely used for AI analysis. Identity theft, discrimination, and compromised patient confidentiality are just a few of the major outcomes that can arise from unauthorized access, data breaches, and improper treatment of sensitive health information.

Respecting laws like the General Data Protection Regulation (GDPR) in Europe and the Health Insurance Portability and Accountability Act (HIPAA) in the United States is essential. The rules for protecting patient data are outlined in these regulations. Furthermore, to protect patient confidentiality, data anonymization is crucial wherever possible. To stop data breaches, strong security measures like encryption and safe data storage must also be put in place.

Dialogue is the key

While there are many advantages to integrating AI into medical imaging, there are also significant ethical concerns. We need to address these concerns to guarantee the responsible use of the technology.

Healthcare providers and developers can ensure the responsible use of AI-driven medical imaging software by emphasizing equity, transparency, accountability, and patient privacy. This approach can help make it a force for good in the healthcare industry. Without doubt, navigating these ethical problems as technology advances will require constant communication between all stakeholders, including patients.

Resources

[1] Banerjee I., PhD, Bhattacharjee K., PhD, Burns J. L., MS, Trivedi H., MD, Purkayastha S., PhD, Seyyed-Kalantari L., PhD, Patel B. N., MD, MPH, Shiradkar R., PhD, Gichoya J., MD, MS: “Shortcuts” Causing Bias in Radiology Artificial Intelligence: Causes, Evaluation, and Mitigation July 26, 2023, doi: https://doi.org/10.1016/j.jacr.2023.06.025

[2] Trafton A.: Study reveals why AI models that analyze medical images can be biased, June 28, 2024, MIT News, https://news.mit.edu/2024/study-reveals-why-ai-analyzed-medical-images-can-be-biased-0628

[3] Pedreschi D., Giannotti F., Guidotti R., Monreale A., Ruggieri S., Turini F.: Meaningful Explanations of Black Box AI Decision Systems, July 17, 2029, doi: https://doi.org/10.1609/aaai.v33i01.33019780

[4], [5], [6] Herington J., McCradden D. M., Kathleen Creel, Ronald Boellaard, Elizabeth C. Jones, Abhinav K. Jha, Arman Rahmim, Peter J.H. Scott, John J. Sunderland, Richard L. Wahl, Sven Zuehlsdorff and Babak Saboury: Ethical Considerations for Artificial Intelligence in Medical Imaging: Deployment and Governance, Journal of Nuclear Medicine, October 2023, https://jnm.snmjournals.org/content/64/10/1509