Balancing Ethics and Innovation: Can Artificial Intelligence Safely Transform Emergency Surgery? A Narrative Perspective
Abstract
:1. Introduction
2. Methods
3. Results
4. Ethical Considerations in AI-Assisted Emergency Surgery: Summary of Evidence and Discussion of Critical Issues for Implementation
4.1. Accountability and Transparency
4.2. Bias and Equity in AI Algorithms
4.3. Data Protection and Privacy
- Storage Capacity and Infrastructure:
- High Data Volume: Surgical procedures, especially those recorded in high-definition formats, generate substantial amounts of data. Continuous recording of all surgeries can quickly exceed existing storage capacities, necessitating significant investments in scalable storage solutions.
- Cost Implications: Maintaining and upgrading storage infrastructure to accommodate the growing volume of video data can be financially burdensome for healthcare institutions, particularly those with limited resources.
- Data Management and Accessibility:
- Efficient Retrieval: As the volume of stored video data increases, implementing effective data management systems becomes essential to ensure that relevant videos can be easily retrieved for analysis and review.
- Standardization Issues: Variations in video formats, annotations, and metadata can complicate data integration and analysis, underscoring the need for standardized protocols in video recording and storage.
- Legal and Ethical Considerations:
- Patient Privacy: Surgical videos often contain sensitive patient information. Ensuring compliance with data protection regulations, such as GDPR and HIPAA, is crucial to safeguard patient privacy and maintain trust.
- Consent and Data Ownership: Clarifying issues related to informed consent for recording and using surgical videos, as well as determining data ownership, is essential to address ethical and legal concerns.
4.4. Surgical Data Quality
4.5. Real-Time Data and Workflow in the Operating Room
4.6. Informed Consent in Emergency AI-Assisted Surgery
4.7. Regulatory and Legal Frameworks
- The issue of explainability is increasingly central to regulatory efforts. EU legislation, including the GDPR and amended Directive 2011/83 on Consumer Rights, outlines obligations for explainable AI in automated decision-making (e.g., GDPR Articles 13.2(f) and 14.2(g)) [55]. In practice, explainability entails providing clinicians with access to key information such as the following:
- The main features driving the model’s decision;
- All contributing data points;
- How features interact in the model’s logic;
- And, in some cases, the architecture of the model itself [58].
4.8. Liability in AI-Assisted Complications: Who Is Responsible?
- Supportive AI, aimed to support clinical decision-making;
- AI-assisted decision-making, which provides semi-autonomous guidance;
- Autonomous AI in predefined tasks, which automates specific surgical steps.
5. Call to Action and Clinical Recommendations for Ethical AI Implementation in Emergency Surgery
- Enhancing AI Transparency—Prioritizing the development of explainable AI (XAI) models to improve interpretability, ensuring that healthcare providers can critically assess and validate AI-generated recommendations.
- Developing Clear Communication Protocols—Standardizing the disclosure of AI involvement in patient care to maintain trust and uphold patient autonomy.
- Mitigating Bias in AI Training Data—Ensuring that AI training datasets are diverse and representative of all patient populations to prevent the exacerbation of health disparities.
- Aligning AI with Patient-Centered Care—Designing AI systems that integrate ethical considerations and patient values into their decision-making frameworks.
- Strengthening Regulatory Oversight—Establishing comprehensive legal frameworks to define AI accountability, enhance data protection, and uphold ethical standards in emergency surgical applications.
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
AI | Artificial Intelligence |
ML | Machine Learning |
DL | Deep Learning |
CV | Computer Vision |
CNNs | Convolutional Neural Networks |
R-CNN | Region-based Convolutional Neural Network |
FDA | U.S. Food and Drug Administration |
GDPR | General Data Protection Regulation |
EU | European Union |
MHRA | Medicines and Healthcare products Regulatory Agency |
NMPA | National Medical Products Administration |
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Authors (Year) | Study Focus | Setting | Key Ethical or Clinical Implications |
---|---|---|---|
Panossian et al. (2025) [1] | Validation of AI-based risk calculator in emergency laparotomy | Emergency Surgery | Accountability, clinical reliability |
Gebran et al. (2022) [2] | AI tool to predict ICU need after emergency surgery | Emergency Surgery | Predictive validity, clinical utility |
Elhaddad and Hamam (2024) [3] | Review on AI-driven clinical decision-support systems | General Healthcare | Potential vs. limitations of AI in decision-making |
Capelli et al. (2023) [4] | White paper on ethics and trust in AI-assisted surgery | Clinical Surgery | Trustworthiness, transparency |
Cobianchi et al. (2022) [5] | Ethical dilemmas of AI in surgery | General Surgery | Bias, autonomy, data governance |
Hashimoto et al. (2018) [7] | Promises and perils of AI in surgery | General Surgery | Technological optimism, black box concerns |
Mascagni et al. (2022) [8] | AI for surgical safety: critical view assessment | Laparoscopic Surgery | AI-driven safety enhancement |
Mascagni et al. (2021) [9] | Computer vision for detecting surgical events | Laparoscopic Surgery | Video annotation and decision support |
Shinozuka et al. (2022) [10] | AI for surgical phase recognition | Laparoscopic Surgery | Workflow optimization, data use |
Madani et al. (2022) [11] | Semantic segmentation for intraoperative guidance | Laparoscopic Surgery | Surgical anatomy identification |
De Simone et al. (2022) [12] | Global survey about AI-assisted implementation in emergency surgical practices | Emergency Surgery | Knowledge, attitudes, perspectives, and barriers perceived by emergency surgeons to AI-driven tool implementation in the emergency setting |
Regulatory Body | Region | Key AI Guidelines | Major Challenges |
---|---|---|---|
FDA (U.S.) | United States | AI/ML-based surgical tools must undergo premarket approval (PMA) or 510(k) clearance. | Lack of standardized AI-specific rules; most AI tools classified as decision-support rather than autonomous systems. |
GDPR (EU) | European Union | Focuses on data privacy, informed consent, and algorithmic transparency. | No clear AI-specific medical regulations; AI explainability requirements are still evolving. |
EU AI Act | European Union | First global AI regulatory framework, categorizing AI applications based on risk levels (minimal, high, unacceptable). | Surgical AI could be classified as “high risk”, requiring stringent validation and real-time monitoring. |
WHO AI Ethics Framework | Global | Calls for ethical AI integration with principles of trustworthiness, fairness, and transparency. | Non-binding recommendations; lacks enforcement mechanisms for individual countries. |
MHRA (UK) | United Kingdom | Requires AI as a Medical Device (AIaMD) to meet CE marking for safety and performance. | Post-market AI monitoring is weak, making it difficult to detect AI-related adverse events in real time. |
China NMPA AI Regulations | China | Encourages AI in healthcare but requires strict cybersecurity measures and data localization. | AI models must be trained on Chinese patient data, limiting generalizability across global populations. |
AI Role | Description | Case Example in Emergency Surgery | Ethical and Practical Considerations |
---|---|---|---|
AI as a Decision-Support Tool (Assistive AI) | Provides real-time data analysis, risk assessment, and decision-making support without making autonomous choices. | POTTER Calculator predicts postoperative mortality and complications in emergency general surgery, improving triage and resource allocation. | Surgeons retain full control; AI acts as an augmentation tool to reduce cognitive load. Trust and interpretability (XAI) are key. |
AI-Assisted Decision-Making (Semi-Autonomous AI) | AI suggests interventions based on real-time surgical video, patient data, or intraoperative findings. The final decision remains with the surgeon. | Computer vision evaluates intestinal perfusion via ICG fluorescence imaging, aiding in anastomotic leak prevention. | AI requires explainability and surgeon oversight. If AI misinterprets perfusion, who is responsible? There is a lack of clear medico-legal guidelines. |
AI-Enabled Automation (Task-Specific AI) | AI executes predefined actions autonomously within a controlled scope. | AI-powered automatic staplers (e.g., Medtronic Signia™) adjust stapling depth and compression based on tissue thickness sensors, reducing human error in colorectal anastomosis. | This event requires human intervention in case of AI failure. If a staple misfires, is it a surgeon’s or manufacturer’s responsibility? There are not clear regulations on it. |
Fully Autonomous AI Surgery (Future Concept) | AI performs entire surgical procedures autonomously without direct human input. | STAR Robot (Smart Tissue Autonomous Robot) successfully performed soft-tissue anastomosis in a porcine model with greater precision than human surgeons. | Not yet ethically or legally acceptable in humans. Requires new regulatory frameworks to define accountability and patient consent. |
Scenario | Example | Liability Considerations |
---|---|---|
AI as a Decision-Support Tool (Assistive AI) | POTTER calculator underestimates a patient’s risk, leading to an anastomosis that fails instead of a safer Hartmann’s procedure. | The surgeon retains final decision-making authority, so primary liability falls on the clinician. However, if AI models were trained on biased datasets, legal responsibility may extend to developers and institutions. |
AI-Assisted Surgical Decision-Making | AI misinterprets ICG fluorescence imaging and fails to detect ischemia before an anastomosis, leading to a leak. | If the surgeon over-relied on AI despite conflicting clinical findings, they may share liability. However, if AI misdiagnosis results from algorithmic failure, the AI vendor could be held accountable under product liability laws. |
AI-Enabled Automation (Task-Specific AI) | AI-powered stapler malfunctions and misfires, causing anastomotic dehiscence. | Product liability law applies—manufacturer is responsible for device failure unless the surgeon misused the device against recommendations. AI safety validation is crucial. |
Ethical Issue | Clinical Recommendation |
---|---|
Informed Consent in Emergency Settings | Implement standardized protocols to inform patients or their proxies about AI involvement during emergency procedures. |
Explainability and Transparency | Adopt explainable AI tools to ensure clinicians can interpret and validate system outputs in real time. |
Bias and Equity in AI Models | Ensure diverse and representative datasets are used to train AI, reducing bias across populations. |
Liability in AI-Assisted Complications | Develop shared responsibility models between surgeons, institutions, and AI developers for adverse outcomes. |
Surgical Video Data Governance | Follow ethical and legal frameworks (e.g., GDPR, HIPAA) for storage, use, and consent of surgical video data. |
Workflow Integration in Emergency Surgery | Use AI tools to support—rather than replace—surgeon decision-making; maintain human oversight during intraoperative use. |
Data Quality and Annotation | Promote the prospective collection of high-quality clinical data and intraoperative images through standardized acquisition protocols, expert-validated annotation, and interoperability between centers. |
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De Simone, B.; Deeken, G.; Catena, F. Balancing Ethics and Innovation: Can Artificial Intelligence Safely Transform Emergency Surgery? A Narrative Perspective. J. Clin. Med. 2025, 14, 3111. https://doi.org/10.3390/jcm14093111
De Simone B, Deeken G, Catena F. Balancing Ethics and Innovation: Can Artificial Intelligence Safely Transform Emergency Surgery? A Narrative Perspective. Journal of Clinical Medicine. 2025; 14(9):3111. https://doi.org/10.3390/jcm14093111
Chicago/Turabian StyleDe Simone, Belinda, Genevieve Deeken, and Fausto Catena. 2025. "Balancing Ethics and Innovation: Can Artificial Intelligence Safely Transform Emergency Surgery? A Narrative Perspective" Journal of Clinical Medicine 14, no. 9: 3111. https://doi.org/10.3390/jcm14093111
APA StyleDe Simone, B., Deeken, G., & Catena, F. (2025). Balancing Ethics and Innovation: Can Artificial Intelligence Safely Transform Emergency Surgery? A Narrative Perspective. Journal of Clinical Medicine, 14(9), 3111. https://doi.org/10.3390/jcm14093111