PhilArchive
open access
Clinical applications of machine learning algorithms: beyond the black box
Machine learning algorithms may radically improve our ability to diagnose and treat disease. For moral, legal, and scientific reasons, it is essential that doctors and patients be able to understand and explain the predictions of these models. Scalable, customisable, and ethical solutions can be achieved by working together with relevant stakeholders, including patients, data scientists, and policy makers.
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Machine learning in medicine: Addressing ethical challenges#404556
- Artificial intelligence in healthcare and medicine: clinical applications, therapeutic advances, and future perspectives#260304
- The ethical, legal and social implications of using artificial intelligence systems in breast cancer care#964464
- Epistemic virtues of harnessing rigorous machine learning systems in ethically sensitive domains#93493
- Artificial Intelligence in Clinical Decision-Making: Current Applications, Challenges, and Future Directions in Modern Healthcare.#239942
Record · ID 776460
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive