Ambient Artificial Intelligence in Health Care Documentation: A Review of Tools, Integration, and Clinical Implications
Abstract
Introduction: Ambient artificial intelligence (AAI) is increasingly being integrated into clinical workflows to address the documentation burden contributing to clinician burnout. AAI systems use automatic speech recognition and natural language processing to capture real-time clinician–patient dialogue and generate structured documentation within the electronic health record. Methods: This review examines AAI’s technical foundations, clinical applications, and implementation considerations in oncology practice. We describe how current AAI tools extract, classify, and populate relevant clinical content, such as the history of present illness, physical examination findings, and assessment and plan, while adhering to established US coding standards (e.g., International Classification of Diseases [ICD-10]). Key commercially available systems, including Nuance DAX Copilot, Ambience Healthcare, Tali AI, Athelas Scribe, and Nabla, are compared in terms of functionality, integration capabilities, and reported outcomes. Practical guidance is provided on clinician onboarding, device setup, customization, and patient consent. Results: Early data suggest AAI implementation can reduce after-hours documentation, improve note quality, and enhance coding accuracy. However, barriers such as transcription errors, hallucinations, and workflow disruptions must be proactively addressed. To ensure safe and effective integration, oncology practices adopting AAI should prioritize targeted training, documentation oversight, and user feedback. Conclusion: Although preliminary findings are promising, further prospective, multi-institutional studies are warranted to evaluate the impact of AAI on clinical efficiency, data quality, and patient outcomes in oncology. This review provides a foundational framework for researchers, clinicians, and health systems seeking to implement AAI in precision oncology documentation.
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Hundal, J., Veettil, A. A. V., Chung, C., & Hosseini, M. (2026) · American Society of Clinical Oncology Educational Book · doi:10.1200/edbk-26-517280 · Cited by 1 · see citing works
Self-citation
- Authors
- Hundal, J., Jain, M., & McCollom, J.
- Affiliations
- Cleveland Clinic (US)
- Parkview Health (US)
- Corresponding author
- McCollom, Joseph
- Venue
- AI in Precision Oncology
- Publisher
- SAGE Publications
- Published
- 2025-09-29
- Volume
- 2
- Issue
- 5
- Pages
- 158-162
- ISSN
- 2993-091X (ISSN-L)
- 2993-0928
- OpenAlex
- W4414680031
- Cited by
- 11
- References
- 20
- Citations by year
- 2026: 11
- FWCI
- 8.72
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- AI in Precision Oncology (publishedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Nursing › Issues, ethics and legal aspects › Nursing Diagnosis and Documentation
- Keywords
- clinical documentation, electronic health records, speech recognition, natural language processing, clinician burnout, documentation burden, oncology practice, ICD-10 coding, transcription errors, hallucinations, clinical workflow integration, patient consent, precision oncology
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