Abstract B005: Designing ethically aligned AI to support palliative care in hematologic malignancies: Ethical considerations for equity and patient-centered care
Bibliographic record
Abstract
Abstract Patients with hematologic malignancies often face complex and unpredictable illnesses. Many undergo intense treatment near the end of life and may be referred to palliative care much later than they could have been. As artificial intelligence (AI) becomes more integrated into clinical workflows, there is growing hope that it could be utilized for the early identification of patients who might benefit from palliative care, especially in the context of disparities surrounding time of palliative care consultation. In this context—where decisions are sensitive, timing may be crucial and the intrinsic nature of the care is deeply personal—the application of AI calls for an intentional and ethical approach. The core of these concerns centers the principle of justice, especially for patients who are already underserved due to race, language, or access to care, and thus might've also been less likely to receive timely palliative care to begin with. This project proposes a framework for developing and implementing AI algorithms and tools that support human decision-making, and that are built with fairness, context, and compassion in mind. Drawing on research in oncology, ethics, and health disparities, the framework highlights several key elements: using a combination of clinical as well as structured and narrative data to better understand patient needs, correcting for known biases in data sets, and reevaluation through an iterative process. Rather than focusing on efficiency alone, the goal is to use AI to surface moments that might otherwise be missed—moments to ask, listen, and understand what matters most to each patient. Earlier, more thoughtful discussions about quality of life and goals of care can come down to a flag by an algorithm trained to predict when a patient may need it the most—especially for patients who might not always be heard. Citation Format: Wamia Siddiqui. Designing ethically aligned AI to support palliative care in hematologic malignancies: Ethical considerations for equity and patient-centered care [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr B005.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.040 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".