The ongoing innovation in technology has affected every field and industry in today's world. While these innovations have led to helpful and improved practices or processes, they have also created a demand for even further technological improvements. In healthcare, machinery used for medical imaging such as MRIs, CT scans, Ultrasounds, X-rays, etc. now incorporates the use of artificial intelligence. Artificial intelligence in this case is used to scan and find anomalies, determine patient urgency based off of imaging, screen for early signs of diseases, create personal treatment plans based on imaging and patient health history, and more (Keller, 2025) (Resuhr, Garnett, 2025). Machine imagery is a vital tool used in today's healthcare field; the incorporation of artificial intelligence brings both benefits and potential risks to patients, healthcare professionals, hospitals, medical imaging device manufacturers, and the government. These systems, while they may help improve efficiency and accuracy within imaging and diagnosing, also come with the risks and demands of improved data governance. The article, "Securing Data Flow in Clinical Machine Vision: Lessons from EDC (Electronic Data Capture) Systems" discusses this current ongoing event describing the need for increased data governance, the importance of electronic data capture systems, and possible solutions and next steps to the ethical and technological dilemmas found in these systems. Analyzing this event provides an understanding of the importance of ethically integrating technology within healthcare, relating information policy, data privacy and consent, healthcare, and responsible uses of AI and technology.

Overview of Todays System

In today's healthcare, medical imaging is an important part of treating patients. With the incorporation of artificial intelligence in this process it has brought the industry into a new era of patient diagnosis and treatment. However, in this new era it is now vital to create improved policies and data governance to ensure that these data captures are ethically handled. As stated by Thornbury, "With the rise of AI-driven diagnostics, automated image analysis, and real-time patient monitoring, vast quantities of sensitive data flow through interconnected systems. Ensuring that this flow remains secure, transparent, and compliant is essential" (2025). One of the first points of concern stated by Thornbury was the volume and pace of data that are generated by electronic data captures. Over 528 million scans are completed yearly combining MRI, CT, PET, and ultrasound scans (Yardi, 2025). This load of data creates a dilemma of how it can be properly and securely stored. With data this sensitive it is crucial to ensure that it adheres to HIPPAs privacy and security standards. Without proper storage, citizens' health data runs the risk of being unprotected and breached. Another dilemma pointed out by Thornbury is artificial intelligence bias and transparency. As stated by Thornbury, "Models trained on poorly governed datasets risk embedding bias into clinical decisions" (2025). Artificial intelligence and machine learning models can have built in prejudices due to the data they are built with. This prejudice can have a harmful impact on marginalized communities, further fueling the bias that currently occurs within healthcare. According to the National Library of Medicine, "Bias in healthcare is pervasive worldwide, despite increasing awareness and an explicit commitment to its elimination… For example, because of the legacy of chattel slavery in the United States, bias towards race remains prominent" (2024). Using these tools without policy or laws from the government to ensure ethical use harms patients and puts their health data at risk.

Government Action

Laws such as the Health Insurance Portability and Accountability Act (HIPPA) and The Health Information Technology for Economic and Clinical Health Act (HITECH) aid in creating standards regarding the use of healthcare data such as electronic data captures. HIPPA comprises five components regarding patient health information; privacy rules which set the standards for protecting patient health information, security rules which set the standards for managing patient health information, transaction rules which help ensure the safety, accuracy, and security of medical records, enforcement rules which address the penalties for violations, and unique identifier rules (Kelvas, n.d.). The HITECH Act as described by congress.gov, "intended to promote the widespread adoption of health information technology to support the electronic sharing of clinical data…includes a series of privacy and security provisions that expand the current requirements under (HIPPA)" (2025). These two acts work together to regulate and create standards for management and protection of patient data, such as electron data captures. HIPPA as described above sets standards for how patient data must be stored, shared, and accessed. The HITECH act further strengthens these standards by expanding and adding additional ones such as stronger data security requirements, expanding patient rights, and creating standard practices for the adoption and use of technology. These acts are significant as they identify the key stakeholders within this event by setting standard practices and guidelines for hospitals, manufactures, and insurance companies to adhere to regarding patient health data. And outlines the rights of citizens, to ensure that they maintain autonomy and protection over their health.

Ethical Dilemmas

Looking further, this event presents multiple ethical issues such as the protection of patient data, accuracy of artificial intelligence diagnosis, and preventing bias models and output. The rule utilitarian theory follows the belief that a body or people, or society, should follow the rules of a governing power that brings the greatest amount of good to all. The social contract theory follows the belief that for in exchange for certain protections a society must follow the rules of that governing body. These two frameworks help to provide a structure in creating solutions that can be formed by the government, manufacturers, and health care facilities to solve the dilemmas created by this event. Looking at these dilemmas, the protection of patient data and prevention of bias models or outputs, these protections would fall under the responsibility of the government. People trust the government to ensure that they and their possessions are protected that there are systems set up to ensure that in the event of a violation of their possessions, such as their data, someone is held accountable in this injustice. Acts such as HIPPA and HITECH have set up these standards and guidelines to keep organizations accountable in the event that they fail to protect a person's health information. Through continuous monitoring and revision of policy these acts create an ethical system to ensure the protection of people's data. In addition, to ensure that artificial intelligence scans are both accurate and unbiased, there must be an exchange of some health information and data to build and train these models to create the most accurate and unbiased outputs, resulting in the greatest good for all parties. These frameworks work together to create solutions to these problems. Ensuring that people's information is fairly protected allows for its use in building accurate and unbiased data. In turn, people are able to receive improved healthcare and treatment, and healthcare facilities are able to innovate and create more efficient systems and services.

Conclusion

Through technological innovation today's healthcare now has the opportunity to provide improved care to patients through diagnostic image scanning using artificial intelligence. But with this innovation has come the need and demand of improving not only the security of these systems but also issues regarding its accuracy, bias, and more. Through policy creation using ethical principles, healthcare can provide transparent, unprejudiced, and improved care its patients.

References

Keller, D. (2025, November 17). Precision medicine: How ai is quietly redefining radiology. Forbes. https://www.forbes.com/councils/forbestechcouncil/2025/11/17/precision-medicine-how-ai-is-quietly-redefining-radiology/

Kelvas, D. K. (n.d.). Understanding the 5 main HIPAA rules. HIPAA Exams. https://www.hipaaexams.com/blog/understanding-5-main-hipaa-rules#purpose

Lee, A., & Hastie, M. (2024, July). Recognising and managing bias and prejudice in healthcare. BJA education. https://pmc.ncbi.nlm.nih.gov/articles/PMC11184476/

Resühr, D., & Garnett, C. (2025, April 10). The good, the bad, and the ugly of AI in Medical Imaging. European Medical Journal. https://www.emjreviews.com/en-us/amj/radiology/article/the-good-the-bad-and-the-ugly-of-ai-in-medical-imaging-j140125/

The Health Information Technology for Economic and Clinical Health (HITECH) Act. (2025,

November 21). https://www.congress.gov/crs-product/R40161

Thornbury, M. (2025, October 30). Securing data flow in Clinical Machine Vision. IEEE Computer Society. https://www.computer.org/publications/tech-news/trends/data-flow-clinical-machine-vision

Yardi, S. (2025, January 14). Medical Imaging Statistics and facts (2025). Market.us Media. https://media.market.us/medical-imaging-statistics/