Artificial Intelligence-Based Software as a Medical Device (AI-SaMD): A Systematic Review
Unlock the Future of Healthcare with AI-SaMD
This comprehensive analysis delves into the transformative impact of Artificial Intelligence-Based Software as a Medical Device (AI-SaMD) on modern healthcare. Discover how cutting-edge AI is reshaping diagnostics, treatment, and patient care, addressing critical challenges and paving the way for future advancements.
Authored by: Shouki A. Ebad, Asma Alhashmi, Marwa Amara, Achraf Ben Miled, Muhammad Saqib on 3 April 2025 in Healthcare 2025, 13, 817.
Executive Impact & Key Findings
This systematic review analyzes 62 AI-SaMD studies from 2015-2024, revealing a growing field with specific challenges and recommendations. Key findings highlight the dominance of non-practical research in specialized clinical settings (radiology, ophthalmology, oncology). Major challenges include regulatory approval, AI model transparency ('black-box' issues), algorithmic bias, performance/security, and data governance. Recommendations emphasize interdisciplinary partnerships, clinician training, seamless integration with healthcare systems (EHRs), and rigorous validation. The study underscores the need for practical, experimental research to advance real-world AI-SaMD applications and provides insights for stakeholders to address current limitations and guide future development.
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Total Studies Analyzed
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Years Covered
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Top Research Strategy (Non-Practical)
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Primary Research Focus (Radiology)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
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FDA Approved AI-SaMDs (2015-2023)
Enterprise Process Flow
Data Ingestion & Preprocessing
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AI & Machine Learning Model
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Integration with EHRs
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Secure Storage & Security
| Aspect | Traditional SaMD | AI-SaMD |
|---|---|---|
| Technology | Traditional programming | AI algorithms (machine learning and natural processing language) |
| Adaptability | Static functionality defined at deployment | Dynamic, with potential for continuous learning and improvement |
| Regulatory | Straightforward regulatory approval process | Requires more (e.g., transparency and bias mitigation). |
| Validation | Before deployment | Ongoing validation due to learning algorithms |
| Examples |
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Studies Facing Regulatory Approval Challenges
Ethical AI in Medical Imaging
Miscommunication between healthcare professionals and AI systems contributes to diminished trust in AI-SaMD, limiting user acceptance. This case study from a recent journal highlights the importance of comprehensive staff training and clear guidelines for AI-SaMD use to foster trust and proper integration. Without addressing these human-centric factors, even the most advanced AI solutions face significant barriers to real-world adoption.
Key Impacts:
- Enhanced Staff Training
- Improved Trust & Acceptance
- Clearer AI-SaMD Guidelines
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AI-SaMD Publications from Asia
Enterprise Process Flow
Interdisciplinary Collaboration
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Clinician Training Programs
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Seamless EHR Integration
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Rigorous Model Validation
| Methodology | Frequency | Implication for AI-SaMD |
|---|---|---|
| Reviews | 61.3% | Synthesize existing knowledge; identify gaps. |
| Theoretical Analyses | 11.3% | Develop conceptual frameworks; explore implications. |
| Surveys | 6.5% | Gather perceptions; identify trends. |
| Experiments/Case Studies | 21.0% | Validate real-world application; generate empirical results. |
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Total Hours Reclaimed
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Your AI-SaMD Implementation Roadmap
A structured approach is crucial for successful AI-SaMD deployment. Here's a typical roadmap:
Phase 1: Regulatory Compliance & Stakeholder Alignment
Secure necessary approvals (e.g., FDA, EMA) and establish clear guidelines. Foster interdisciplinary collaboration.
Phase 2: AI Model Development & Validation
Develop transparent, explainable AI models. Conduct rigorous testing and clinical trials to ensure safety and efficacy.
Phase 3: Integration & Training
Seamlessly integrate AI-SaMD with existing EHRs and clinical workflows. Implement comprehensive training for clinicians.
Phase 4: Continuous Learning & Monitoring
Establish robust post-market surveillance. Ensure continuous learning and adaptation while maintaining compliance.
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