Neurosymbolic AI in Medicine: Challenges and Opportunities
Jiménez-Ruiz, E. 
ORCID: 0000-0002-9083-4599, Alam, M., Barile, R. , Chen, J., Cochez, M., Confalonieri, R., Diaz-Rodriguez, N., d'Amato, C., d'Avila Garcez, A., Giunchiglia, E., Gromann, D., Hertling, S., Hitzler, P., Howe, F., Kostylev, E. V., Mileo, A., Morra, L., Mutharaju, R., Paulheim, H., Pesquita, C., Sousa, R. T., Tamma, V., ten Teije, A., Tran, S. N. & Hastings, J. (2026).
Neurosymbolic AI in Medicine: Challenges and Opportunities.
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Abstract
Artificial intelligence has many applications in medicine, including drug discovery, diagnostics, clinical decision support, and the automation of clinical documentation.
Recently, large language model-based systems such as ChatGPT have made headlines for their groundbreaking performance on a wide range of tasks. However, these systems can make errors, be biased, or act harmfully in certain contexts. New approaches are needed which can complement existing approaches while addressing safety and responsibility. Neurosymbolic AI is a novel paradigm that offers methods that are able to learn from data but remain transparent and controllable. This is achieved by combining data-driven approaches with knowledge-driven approaches.
| Publication Type: | Report |
|---|---|
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Departments: | School of Science & Technology School of Science & Technology > Department of Computer Science |
| SWORD Depositor: |
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