SMARTER SCANS, BIGGER QUESTIONS

Ultrasound imaging has taken an important role in healthcare since the late 20th century, allowing doctors to see inside the body without surgery. Ultrasound, which became widely used in clinical practice in the 1970s and 1980s, uses sound waves to create images of organs and tissues. Today, it is commonly used during pregnancy and to examine the heart, liver, kidneys, and breast. In the last decade (2010s–2020s), artificial intelligence (AI) has begun transforming how these images are analyzed, raising important questions about how data-driven technology is reshaping medical decision-making.

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Researchers and engineers, including contributors like Adam E. M. Eltorai and Katherine P. Andriole, argue that AI can significantly improve diagnostic accuracy by identifying patterns that the human eye might miss. AI systems are now being developed and tested to detect conditions such as tumors, heart defects, and internal bleeding more quickly. Companies and research teams in the early 2020s, including those working on AI-assisted fetal imaging (Benson 2025), have advocated for AI as a tool to improve efficiency and reduce human error. In this sense, AI is not replacing doctors but supporting them in making faster and more informed decisions.

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However, critics including medical researchers like Burak Koçak have pointed serious concerns about bias in AI systems. Because these systems are trained on large datasets, they can reflect inequalities already present in healthcare. If training data is drawn primarily from certain populations, AI may be less accurate for underrepresented groups. This issue has become more widely discussed recently (mid-2020s), as AI tools have moved closer to real clinical use. These concerns show that AI is not neutral but shaped by the data and decisions behind it.

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Privacy and consent have also become major issues as AI adoption has increased. Since the passage of the Health Insurance Portability and Accountability Act (HIPAA) in 1996, patient data in the United States has been legally protected, but the rise of AI has complicated how that data is used. Scholars such as David Resühr and Colleen Garnett argue that even “de-identified” medical data carries risks, especially as healthcare systems become more digital and interconnected. Cybersecurity experts have also warned throughout the 2020s that hospitals are increasingly vulnerable to data breaches and ransomware attacks, putting sensitive patient information at risk.

At the same time, engineers and clinicians continue to advocate for the expansion of AI in ultrasound imaging. Researchers working on ultrasound-guided radiotherapy, such as Saskia Camps and Maria Antico emphasize AI’s potential to automate image analysis and improve treatment precision. More recently, scholars like Emma Harris and Frank Verhaegen have suggested that AI, combined with robotics and advanced imaging technologies, could eventually make ultrasound systems more efficient and widely accessible in clinical settings.

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AI has the potential to improve healthcare by increasing accuracy and making imaging more accessible. However, the concerns raised by researchers, policymakers, and clinicians throughout the 2010s and 2020s show that these systems are not purely beneficial. Instead, AI in ultrasound imaging reflects a broader tension between innovation and ethics, raising important questions about bias, patient privacy, and control over medical data. As AI continues to develop, it will be necessary to ensure that technological progress does not come at the expense of fairness, security, and patient trust.

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Sources:

Benson, Martin, Sacha Walton, Tom Hartley, Simon Meagher, Suresh Seshadri, Nicholas Sleep, and Aris T. Papageorghiou. “Fetal Gestational Age Estimation Using Artificial Intelligence on Non-Targeted Ultrasound Images and Video.” npj Digital Medicine 8 (2025): 700. https://doi.org/10.1038/s41746-025-02024-z

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Eltorai, Adam E. M., James M. Hillis, Rajat Chand, Sudhen B. Desai, and Katherine P. Andriole, eds. The Radiology AI Handbook. Philadelphia, PA: Elsevier, 2026.

Harris, Emma, Fontanarosa, Davide, Verhaegen, Frank, and Camps, Saskia, eds. Modern Applications of 3D/4D Ultrasound Imaging in Radiotherapy. Bristol: Institute of Physics Publishing, 2021.

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Koçak, Burak, Andrea Ponsiglione, Arnaldo Stanzione, Christian Bluethgen, João Santinha, Lorenzo Ugga, Merel Huisman, Michail E. Klontzas, Roberto Cannella, and Renato Cuocolo. “Bias in Artificial Intelligence for Medical Imaging: Fundamentals, Detection, Avoidance, Mitigation, Challenges, Ethics, and Prospects.” Diagnostic and Interventional Radiology 31, no. 2 (2025): 75–88. https://doi.org/10.4274/dir.2024.242854

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Kollmann, Christian. “AI in Ultrasound: Can I Trust It?” HealthManagement.org The Journal 23, no. 3 (2023): 193–195.

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Resühr, David, and Colleen Garnett. “The Good, the Bad and the Ugly of AI in Medical Imaging.” European Medical Journal (EMJ) Radiology. 6, no. 1 (2025): 53–55. https://doi.org/10.33590/emjradiol/JTPO7801

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RIKEN. “AI Used to Detect Fetal Heart Problems.” September 18, 2018. https://www.riken.jp/en/news_pubs/research_news/pr/2018/20180918_3/

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Sakai, Akira, et al. “Medical Professional Enhancement Using Explainable Artificial Intelligence in Fetal Cardiac Ultrasound Screening.” Biomedicines 10, no. 3 (2022): 551. https://doi.org/10.3390/biomedicines10030551

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Taksoee-Vester, Caroline A., Kamil Mikolaj, Zahra Bashir, Anders N. Christensen, Olav B. Petersen, Karin Sundberg, Aasa Feragen, Morten B. S. Svendsen, Mads Nielsen, and Martin G. Tolsgaard. “AI Supported Fetal Echocardiography with Quality Assessment.” Scientific Reports 14 (2024): 5809. https://doi.org/10.1038/s41598-024-56476-6