{"title":"Bioethics Artificial Intelligence Advisory (BAIA): An Agentic Artificial Intelligence (AI) Framework for Bioethical Clinical Decision Support.","authors":"Taposh P Dutta Roy","doi":"10.7759/cureus.80494","DOIUrl":null,"url":null,"abstract":"<p><p>Healthcare professionals face complex ethical dilemmas in clinical settings in cases involving end-of-life care, informed consent, and surrogate decision-making. These nuanced situations often lead to moral distress among care providers. This paper introduces the Bioethics Artificial Intelligence Advisory (BAIA) framework, a novel and innovative approach that leverages artificial intelligence (AI) to support clinical ethical decision-making. The BAIA framework integrates multiple bioethical approaches, including principlism, casuistry, and narrative ethics, with advanced AI capabilities to provide comprehensive decision support. The framework employs a structured methodology that includes data collection, paradigmatic case review, analysis through \"mattering maps,\" and scenario-based decision reasoning. A detailed analysis of two challenging cases, an end-of-life care decision and a complex conjoined twins case, demonstrates BAIA's potential to harmonize diverse ethical perspectives while reducing the moral burden on healthcare providers. The framework's agentic architecture additionally allows integration with any new and existing ethical AI systems like METHAD, Delphi, and EAIFT, enabling multiframework collaboration. This work also acknowledges limitations related to data quality, bias, and complexity of ethical decisions and proposes mitigation strategies, including standardized databases, fairness algorithms, and maintaining human oversight. Thus, this work represents a significant step toward combining technological advancement in agentic AI with established bioethical principles to improve the quality and consistency of clinical ethical decision-making, thus reducing moral distress for clinicians.</p>","PeriodicalId":93960,"journal":{"name":"Cureus","volume":"17 3","pages":"e80494"},"PeriodicalIF":1.0000,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11906199/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cureus","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.7759/cureus.80494","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/3/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
引用次数: 0
Abstract
Healthcare professionals face complex ethical dilemmas in clinical settings in cases involving end-of-life care, informed consent, and surrogate decision-making. These nuanced situations often lead to moral distress among care providers. This paper introduces the Bioethics Artificial Intelligence Advisory (BAIA) framework, a novel and innovative approach that leverages artificial intelligence (AI) to support clinical ethical decision-making. The BAIA framework integrates multiple bioethical approaches, including principlism, casuistry, and narrative ethics, with advanced AI capabilities to provide comprehensive decision support. The framework employs a structured methodology that includes data collection, paradigmatic case review, analysis through "mattering maps," and scenario-based decision reasoning. A detailed analysis of two challenging cases, an end-of-life care decision and a complex conjoined twins case, demonstrates BAIA's potential to harmonize diverse ethical perspectives while reducing the moral burden on healthcare providers. The framework's agentic architecture additionally allows integration with any new and existing ethical AI systems like METHAD, Delphi, and EAIFT, enabling multiframework collaboration. This work also acknowledges limitations related to data quality, bias, and complexity of ethical decisions and proposes mitigation strategies, including standardized databases, fairness algorithms, and maintaining human oversight. Thus, this work represents a significant step toward combining technological advancement in agentic AI with established bioethical principles to improve the quality and consistency of clinical ethical decision-making, thus reducing moral distress for clinicians.