Publication2026
RADO: Trustworthy Radiology Impression Generation Using Safety and Faithfulness-Based Preference Optimization
The record
- Type
- Publication
- Date
- 16 May 2026
- Level
- National
- Journal
- ACM Transactions on Computing for Healthcare
- Volume
- 7
- Issue
- 3
- Published
- 16 May 2026
- DOI
- 10.1145/3805803
- Scopus ID
- 105043949901
- ISSN
- 26911957
- Authors
- 6
- Open access
- Yes
- Citations
- 0
- International co-authorship
- No
- Co-author countries
- IN
Classification
Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.
UN Sustainable Development Goals
3.4 Reduce premature mortality from non-communicable diseases and promote mental health and well-being
RADO framework for radiology impression generation using large language models with safety and faithfulness constraints improves diagnostic accuracy and clinical relevance in medical imaging
- High confidence
- The subject matter is a strong and direct inference from the record type, the funding or organising body, or the programme context, even where the title alone is thin. Example: an ICMR-funded project against SDG 3, on the strength of the agency.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-1.2 Health and well-being
Healthcare AI system development advances diagnostic imaging interpretation supporting radiologist decision-making and improving patient health outcomes in low-resource settings
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Each label above carries the instrument and the version that produced it. How each classification is reached.
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- Source rows resolved into this record
- 1
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Citing this record
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KIIT Deemed to be University. “RADO: Trustworthy Radiology Impression Generation Using Safety and Faithfulness-Based Preference Optimization”. Kortex.