FDA Approved AI Devices Analysis

Health informatics students strengthen their grasp of clinical innovation when they review Table 3.1 in the Carroll text and perform an in-depth exploration of one of the AI powered medical devices approved by the FDA between 2017 and 2018.

AI Powered Medical Devices

• Review Table 3.1 in the Carroll text and perform an in-depth exploration of one of the AI powered medical devices. Faculty encourage selection of a device that connects directly to areas of personal interest in diagnostics or patient monitoring.

• Explore what type of data is gathered in the use of this device. Many early devices relied on imaging files or physiological signals captured in routine care settings to train and operate the underlying algorithms.

• Assess how predictive data analysis is incorporated in the use of this device. Contemporary FDA authorizations continue to expand these capabilities, with recent analyses showing more than 1,000 cleared devices by mid-2025 that incorporate machine-learning models for risk scoring and outcome forecasting.

Submission Instructions • The paper is to be clear and concise and students will lose points for improper grammar, punctuation and misspelling. • The paper should be formatted per current APA and 3-4 pages in length, excluding the title, abstract and references page. Incorporate a minimum of 5 current (published within last five years) scholarly journal articles or primary legal sources (statutes, court opinions) within your work. Instructors note that the five-source minimum reflects the rapid pace of regulatory updates and helps ground the discussion in the most relevant evidence available today.

Sample Student Response Example The IDx-DR system appears in Table 3.1 as an example of AI integration for diabetic retinopathy diagnosis. Retinal images captured during standard eye exams form the primary data input for this device. Predictive data analysis relies on deep learning algorithms trained to identify specific patterns indicative of disease progression. According to Abràmoff et al. (2018; https://doi.org/10.1038/s41746-018-0040-6), the autonomous system demonstrated sensitivity of 87.2% and specificity of 90.7% in primary care environments. Such incorporation allows for efficient screening without immediate specialist involvement. Recent updates in similar technologies continue to refine accuracy through larger and more inclusive training sets. Overall, the approach supports broader access to timely diagnostics in underserved areas.

Follow-up Considerations for Deeper Analysis Evaluations of FDA-cleared devices suggest that predictive elements play a central role in shifting from reactive to proactive healthcare models. Data from systematic reviews indicate that radiology-focused applications dominate approvals due to the availability of annotated imaging datasets. Practitioners may observe improved outcomes when these tools integrate seamlessly with existing electronic health records. Continued monitoring of post-market performance remains essential to address any emerging limitations in diverse patient groups.

References Abràmoff, M.D. et al. (2018) ‘Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices’, npj Digital Medicine, 1(1), p.9. Available at: https://doi.org/10.1038/s41746-018-0040-6.

Benjamens, S., Dhunnoo, P. and Meskó, B. (2020) ‘The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database’, npj Digital Medicine, 3(1), p.118. Available at: https://doi.org/10.1038/s41746-020-00324-0.

Joshi, G. et al. (2024) ‘FDA-approved artificial intelligence and machine learning (AI/ML)-enabled medical devices: an updated landscape’, Electronics, 13(3), 498. Available at: https://doi.org/10.3390/electronics13030498.

Singh, R. et al. (2025) ‘How AI is used in FDA-authorized medical devices’, npj Digital Medicine. Available at: https://doi.org/10.1038/s41746-025-01800-1.

Windecker, D. et al. (2025) ‘Generalizability of FDA-approved AI-enabled medical devices for clinical use’, JAMA Network Open, 8(4), e258052. Available at: https://doi.org/10.1001/jamanetworkopen.2025.8052.

• AI powered medical devices table 3.1 Carroll text assignment help
• Prepare a 3-4 page APA formatted paper on one AI-powered medical device listed in Table 3.1 of the Carroll text, including analysis of data types and predictive analysis methods. (pages length)
• Compose a 900-word essay exploring FDA approved AI medical devices with focus on data gathering and predictive features for your health informatics course. (word count)
• Submit a clear APA paper that examines data collection and predictive analytics in a chosen AI device from the provided table. (short form/summary)

 Assignment (Module 4 – Week 5 Discussion Post) Ethical and Regulatory Challenges in AI Medical Device Implementation In the upcoming module, learners will examine ethical considerations surrounding the deployment of AI-powered devices in healthcare settings. Students prepare a 500-word discussion post analyzing potential biases in data used for predictive analytics and referencing at least two post-2020 scholarly sources. The post must also address FDA guidance on post-market surveillance to demonstrate awareness of ongoing regulatory expectations.