Ensuring Informed Consent and Fair Access to Artificial Intelligence Enhanced Healthcare Services
This article discusses a novel strategy to make AI-based healthcare informed consent clearer, simpler to obtain, and more equitable. The recommended solution uses natural language processing and user input to process permission forms faster and make them easier to understand for patients. We verify the shorter documents for clarity, user comprehension, and fairness, ensuring the agreement process is effective for numerous individuals. Dynamic feedback allows the consent process to improve depending on user behavior and group-specific issues. The strategy uses continual growth and monitoring to eliminate bias, clarify materials, and ensure compliance. Performance measuring metrics improve reading scores, user comprehension, validation accuracy, and fairness measures compared to previous techniques. The proposed method simplifies health care and makes consumers happy. These modifications ensure that informed consent in healthcare is lawful, fair, and adaptable, enabling a patient-centered strategy that adjusts to changing healthcare requirements. This study suggests that employing modern technology in informed consent might make patients more confident and participating, creating a more inclusive healthcare system.
Authors
- Rajit Nair (ORCID: https://orcid.org/0000-0002-4564-0920)
- Musaddak Maher Abdul Zahra (ORCID: https://orcid.org/0000-0002-5879-6991)
- Bibhuti Bhusan Dash (ORCID: https://orcid.org/0000-0001-8786-137X)
- Sudhansu Shekhar Patra (ORCID: https://orcid.org/0000-0001-9996-7681)
- S. Chowdhury (ORCID: https://orcid.org/0000-0001-5679-3140)
- Khyati Nema
Institutions
- Niagara University (US)
- Alsalam University College (IQ)
- Barkatullah University (IN)
- KIIT University (IN)
Publication Details
- Published
- 2025-05-23
- DOI
- https://doi.org/10.1109/incet64471.2025.11140180
- Citations
- 1
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.65