The Patient Safety Imperative:Why AI Is Needed Now

Author

Campion Quinn
May 30, 2025

By Campion Quinn, MD, MBA, and Ajay K Gupta, CISSP, MBA

Medical errors and preventable harm remain significant challenges in modern healthcare, contributing to hundreds of thousands of avoidable deaths annually.[1] The complexity of patient care, compounded by information overload and administrative burdens, has created an urgent need for innovative solutions. Artificial intelligence (AI) holds the potential to transform patient safety and clinical outcomes by enabling real-time risk detection, optimizing workflows, and minimizing diagnostic errors. For example, AI-driven predictive analytics have been successfully implemented in sepsis detection, reducing mortality rates by approximately 20%. In radiology, AI has demonstrated high accuracy in detecting lung nodules and diabetic retinopathy, improving early diagnosis. Additionally, workflow optimization through AI-assisted documentation has reduced clinician burden and enhanced efficiency in hospital settings. Integrating AI-powered machine learning (ML) and advanced analytics can enhance clinical decision-making, reduce costs, and improve efficiency. However, to fully realize these benefits, healthcare professionals and administrators must understand both AI's transformative potential and inherent challenges.

AI-Powered Innovations: Transforming Patient Safety in Real Time

AI solutions actively reshape how clinicians detect, prevent, and respond to patient safety risks. AI profoundly impacts three key areas: diagnostic radiology, clinical decision support systems (CDSS), and predictive analytics in critical care.

AI in Diagnostic Imaging

AI-powered radiology is revolutionizing early disease detection. Consider a 55-year-old patient undergoing a routine CT scan. An AI tool can detect a small pulmonary nodule—an early indicator of lung cancer—that might be too subtle for the human eye. Early detection facilitates timely confirmatory tests and interventions such as surgical removal, radiation, or chemotherapy, significantly improving patient outcomes. A study by Ardila et al. demonstrated that deep learning models can accurately detect lung nodules on low-dose CT scans, enhancing early diagnosis and treatment planning as well as decreasing the patient’s radiation exposure. [2]

AI is also used to detect diabetic retinopathy and other pathologies more efficiently than human interpretation.[3,4] Studies have shown that AI systems can achieve diagnostic accuracy rates comparable to or exceeding those of human ophthalmologists, with some models reporting sensitivity and specificity values above 90% in detecting diabetic retinopathy.[5] However, challenges such as algorithmic bias, lack of widespread validation in real-world clinical settings, and disparities in AI performance across patient demographics remain.[6]

Clinical Decision Support Systems (CDSS)

CDSS reduces medication-related errors and enhances patient safety by analyzing real-time patient data and alerting providers to drug interactions, contraindications, and dosage errors. A systematic review found that AI-driven CDSS implementation significantly reduced medication errors.[7] AI-based alert systems, such as MedAware, have been shown to identify prescribing errors with high precision, achieving an accuracy rate of over 90% in some studies.[8] However, challenges remain, including alert fatigue, clinician resistance, and better system integration into clinical workflows. Ensuring clinician training, improving explainability, and fostering trust in AI recommendations are critical for successful adoption.[9]

Predictive Analytics in Critical Care

AI-driven predictive analytics empower clinicians in critical care settings by forecasting patient deterioration and enabling timely interventions. In the ICU, algorithms continuously monitor patient data to identify early signs of sepsis, reducing mortality rates by approximately 20%. However, [10] AI-driven sepsis detection systems require rigorous validation, as some models have performed poorly in real-world settings. For example, a widely used AI system intended to detect sepsis correctly identified only 7% of 2,552 sepsis patients in a real-world hospital setting, leading to delays in antibiotic administration.[11] Implementing these tools requires frequent monitoring, updates, and clinical oversight to ensure their efficacy and safety. Integrating AI-driven alerts within existing hospital workflows while minimizing unnecessary interruptions is a key challenge.

Implementation, Training, and Ethical Considerations

Effective AI deployment requires comprehensive integration and training. Healthcare organizations must merge AI solutions with existing IT systems and invest in interdisciplinary training for clinicians. Physician input is crucial to ensure AI tools remain clinically relevant and ethically sound. Ethical concerns include algorithmic bias, data privacy, and AI explainability. Bias in AI models has been well documented, with some algorithms disproportionately underperforming for minority populations due to biased training data.[12] Addressing these biases requires diverse data sets, continuous model evaluation, and clinician oversight.

Compliance with privacy regulations such as HIPAA is essential for maintaining patient trust and data integrity. AI developers and healthcare institutions are ensuring compliance by incorporating robust data encryption, anonymization techniques, and secure access controls. Additionally, organizations conduct regular audits, follow regulatory guidelines such as the FDA’s SaMD framework, and implement AI explainability measures to ensure transparency and accountability in patient data handling. The FDA’s AI/ML-Based Software as a Medical Device (SaMD) Action Plan provides a framework for regulating AI-enabled medical devices, focusing on safety, transparency, and iterative updates.[13] However, regulatory approaches must evolve as AI adoption grows to address AI drift, real-world validation, and governance structures for monitoring AI decision-making.

Regulatory Frameworks and Future Directions

As AI becomes more deeply embedded in healthcare, robust regulatory and ethical frameworks will be essential to realize its full potential safely. While the FDA has laid foundational guidelines, global efforts such as the European Union AI Act [14] explore risk-based frameworks that balance innovation with patient safety. Moving forward, healthcare AI regulation will likely focus on three key areas:

1. Real-World Validation & Continuous Monitoring – AI tools must undergo continuous performance assessments to ensure effectiveness in live clinical settings.

2. AI Governance & Oversight Structures – Healthcare institutions must establish governance committees to monitor AI performance, mitigate biases, and ensure ethical alignment.

3. Interoperability and Transparency in AI Algorithms—Future AI policies must address explainability, ensuring that clinicians understand AI recommendations and can override them when necessary.

Healthcare leaders must proactively navigate these challenges to establish AI as a trusted clinical ally rather than an opaque black box. Clinician involvement in AI development and ongoing staff training are vital to fostering adoption and ensuring these tools align with real-world clinical needs.

The Future of Patient Safety: AI as a Clinical Ally

AI offers a powerful means to enhance patient safety and improve clinical outcomes. Its applications in diagnostic radiology, clinical decision support, and predictive analytics empower clinicians to act decisively and preemptively. However, successful implementation demands rigorous attention to training, ethical considerations, and regulatory standards. By fostering collaboration between human expertise and machine intelligence, the future of healthcare will be safer, more efficient, and more responsive to patient needs.

References

1. Ratwani RM, Bates DW, Classen DC. Patient safety and artificial intelligence in clinical care. JAMA Health Forum. 2024;5(2):e235514. doi:10.1001/jamahealthforum.2023.5514.

2. Ardila D, Kiraly AP, Bharadwaj S, et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med. 2019;25(6):954-961. doi:10.1038/s41591-019-0571-8.

3. Lim JI, Regillo CD, Sadda SR, et al. Artificial intelligence detection of diabetic retinopathy. Ophthalmol Sci. 2022;3(1):100228. doi:10.1016/j.xops.2022.100228.

4. van Leeuwen KG, Schalekamp S, Rutten MJCM, et al. Artificial intelligence in radiology: 100 commercially available products and their scientific evidence. Eur Radiol. 2021;31(6):3797-3804. doi:10.1007/s00330-021-07892-z.

5. Bates DW, Levine D, Syrowatka A, et al. The potential of artificial intelligence to improve patient safety: a scoping review. NPJ Digit Med. 2021;4(1):54. doi:10.1038/s41746-021-00423-6.

6. Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. N Engl J Med. 2019;380(24):2477-2479. doi:10.1056/NEJMsa1908243.

7. Choudhury A, Asan O. The Role of artificial intelligence in patient safety outcomes: a systematic literature review. JMIR Med Inform. 2020; 8 (7): e18599. Doi: 10.2196/18599.

8. Schiff GD, Hickman TT, Volk LA, et al. A novel method for detecting medication errors using AI-based surveillance. JAMA Netw Open. 2020;3(6):e208600. doi:10.1001/jamanetworkopen.2020.8600.

9. Mossburg SE, Gale BM, Tighe PJ. Artificial intelligence and patient safety: promise and challenges. PSNet. 2024. https://psnet.ahrq.gov/perspective/artificial-intelligence-and-patient-safety-promise-and-challenges.

10. Desautels T, Calvert J, Hoffman J, et al. Prediction of sepsis in the ICU with minimal EHR data: a machine learning approach. JMIR Med Inform. 2016;4(3):e23. doi:10.2196/medinform.5582.

11. U.S. Food and Drug Administration. Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan. Published 2021. Accessed January 31, 2025. https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligencemachine-learning-aiml-based-software-medical-device-samd-action-plan.

13. Yeung S, Downing NL, Fei-Fei L, Milstein A. Bedside computer vision: moving artificial intelligence from driver assistance to patient safety. N Engl J Med. 2018;378(14):1271-1273. doi:10.1056/NEJMp1716891.

14. Chen MM, Golding LP, Nicola GN. Who will pay for AI?. Radiol Artif Intell. 2021;3(3):e210030. doi:10.1148/ryai.2021210030.