Developing a Medical AI Ethics Framework: Integrating Ethical Principles with Healthcare Applications Using Topic Modelling
Bibliographic record
Abstract
Addressing ethical challenges in medical AI applications and promoting responsible AI practices in healthcare necessitates a medical AI ethics framework.This thesis bridges a gap in AI ethics literature by developing such a framework, integrating ethical themes with a code of ethics.Drawing on engineering ethics principles, it employs topic modeling to synthesize findings from both medical and AI ethics literature.The resulting framework outlines a step-by-step process for tailoring a code of ethics to specific fields.The ten-step research method includes problem description, statement extraction, topic modelling, topic definition, framework development, and expert feedback.Key contributions are the medical AI ethics framework, a methodological approach for developing conceptual frameworks, an application of engineering ethics principles, topic modelling, and ChatGPT.This framework can guide practitioners, researchers, and policymakers in addressing ethical concerns in medical AI applications. Artificial intelligence (AI)Artificial intelligence is the automation of cognition.Automation is often used to describe technology applications where human input is minimized.Cognition means "to know." Artificial intelligence systemsArtificial Intelligence Systems are smart machines that simulate human intelligence and can perform tasks that will otherwise require human intelligence. AssertionAssertions are positive statements or declarations typically supported by evidence, deduction, or reasoning.Assertions are not necessarily factually correct. ChatGPTChatGPT is a large language model that uses deep learning and a massive dataset of text to complete a variety of natural language processing tasks. Conceptual frameworkConceptual framework is a broad and abstract structure or theory that provides a general understanding of a particular subject.It serves as a guide for thinking about a topic and provides a basis for making decisions and taking actions.rights, obligations, benefits to society, fairness, or moral principle (Oliveira, 2019;Lowy, 1997). GovernanceGovernance reflects the relationships between government and business actors.This relationship often requires engineering where social, political, ethical, and legal dynamics converge. Grey literatureGrey literature describes knowledge artefacts that are not the product of peer-review processes that characterize publication in academic journals (Adams et al., 2017, p. 433).Grey literature includes information that is produced outside of the formal academic or scientific publishing channels.It can include reports, policy literature, proposals, working papers, government papers and so on (Lawrence et al., 2014). MechanismsMechanism is a combination of steps that can ensure compliance to a set of principles, policies, regulations, or law. RegulationRegulation is the "promulgation of an authoritative set of rules, accompanied by some mechanism for monitoring and promoting compliance with these rules" (Koop and Lodge, 2017, p. 3)
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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 | 0.057 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".