As artificial intelligence (AI) tools are increasingly integrated into healthcare settings, their application to ethically complex care decisions for patients who lack decision-making capacity and identifiable surrogates remains largely unexplored. This study explored ethics consultants’ perspectives on the use of artificial intelligence (AI) in ethical and clinical decision-making for patients who are incapacitated with no evident advance directives or surrogates (INEADS). We conducted a qualitative study using in-depth semi-structured interviews with 19 ethics consultants across nine U.S. states, analyzed using thematic analysis. Three themes were generated. Theme 1, Human Accountability as a Non-Negotiable Boundary, captured ethical concerns including clinician accountability, AI’s inability to capture individual context, and algorithmic bias risk. Theme 2, Designing AI That Reflects the Complexity of INEADS Care, identified requirements for high-quality training data, interdisciplinary development teams, and explainability. Theme 3, A Conditional and Constructive Vision for AI as Supportive Tool, described applications encompassing surrogate identification, goals-of-care facilitation, longitudinal decisional pattern review, and individualized prognostication. Participants framed AI as a resource to support rather than replace human moral judgment, raised concerns about algorithmic bias and institutional variation, and articulated a constructive vision grounded in prior natural language processing work identifying INEADS patients across care settings. Ethics consultants expressed conditional acceptance of AI in INEADS care when used to support, not replace, accountable human judgment. Findings highlight the need for equity-centered design, transparent communication of data limitations, interdisciplinary development, and ethics consultant involvement in validation before clinical implementation.
Aviv Y. Landau, Marsha Williamson, Jiyoun Song et al.· AI and Ethics· 0 citations
Objective Substance use disorders (SUDs) are a major public health challenge, and stigma remains a key barrier to care. Unprofessional or stigmatizing language can shape clinician perceptions and affect decision-making. Traditional natural language processing (NLP) often misses context-dependent bias, while large language models (LLMs) pose reliability concerns. This study aimed to (1) develop an LLM-enhanced, human-validated NLP model to detect unprofessional language, (2) quantify unprofessional language and behavioral health referrals, and (3) examine their association among patients with SUD. Methods In this retrospective cohort study, we analyzed the MIMIC-IV, a large deidentified electronic health record database from a tertiary academic medical center in USA, for adult (≥18 years) with SUD admitted to the emergency department or intensive care unit between 2008 and 2019. A rule-based NLP algorithm detected unprofessional language. Three LLMs (GPT-4, Claude 3, Llama-3) expanded the vocabulary, and expert panel reviewed all generated terms for relevance and clinical realism before integration. Multivariable logistic regression examined the association between unprofessional language and behavioral health referrals. Results Of 260,347 patients, 31.5% had SUD. Unprofessional language was more frequent in SUD notes (72% vs. 52%). The LLM-enhanced model improved over baseline (F-score 0.89 to 0.91). Within the SUD cohort, unprofessional language was significantly associated with higher odds of referral (adjusted odds ratio = 1.54) (all p < .001). Conclusion Unprofessional language was common in SUD documentation and associated with behavioral health referrals. This human-validated, LLM-enhanced approach highlights how documentation-based stigma may influence care pathways and underscores the need for bias-aware, equitable communication strategies.
Jiyoun Song, Sue Hyon Kim, Yoon-Jae Lee et al.· Digital Health· 0 citations
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