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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.24473 |
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| _version_ | 1866913065428058112 |
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| author | Moll, Johannes Lübberstedt, Jannik Nuernbergk, Christoph Stroh, Jacob Mertens, Luisa Purcarea, Anna Zirn, Christopher Benchaaben, Zeineb Drexel, Fabian Häntze, Hartmut Narayanan, Anirudh Puttkammer, Friedrich Zhukov, Andrei Lammert, Jacqueline Ziegelmayer, Sebastian Graf, Markus Högner, Marion Makowski, Marcus Bassermann, Florian Adams, Lisa C. Pan, Jiazhen Rueckert, Daniel Braitsch, Krischan Bressem, Keno K. |
| author_facet | Moll, Johannes Lübberstedt, Jannik Nuernbergk, Christoph Stroh, Jacob Mertens, Luisa Purcarea, Anna Zirn, Christopher Benchaaben, Zeineb Drexel, Fabian Häntze, Hartmut Narayanan, Anirudh Puttkammer, Friedrich Zhukov, Andrei Lammert, Jacqueline Ziegelmayer, Sebastian Graf, Markus Högner, Marion Makowski, Marcus Bassermann, Florian Adams, Lisa C. Pan, Jiazhen Rueckert, Daniel Braitsch, Krischan Bressem, Keno K. |
| contents | Multiple myeloma is managed through sequential lines of therapy over years to decades, with each decision depending on cumulative disease history distributed across dozens to hundreds of heterogeneous clinical documents. Whether LLM-based systems can synthesise this evidence at a level approaching expert agreement has not been established. A retrospective evaluation was conducted on longitudinal clinical records of 811 myeloma patients treated at a tertiary centre (2001-2026), covering 44,962 documents and 1,334,677 laboratory values, with external validation on MIMIC-IV. An agentic reasoning system was compared against single-pass retrieval-augmented generation (RAG), iterative RAG, and full-context input on 469 patient-question pairs from 48 templates at three complexity levels. Reference labels came from double annotation by four oncologists with senior haematologist adjudication. Iterative RAG and full-context input converged on a shared ceiling (75.4% vs 75.8%, p = 1.00). The agentic system reached 79.6% concordance (95% CI 76.4-82.8), exceeding both baselines (+3.8 and +4.2 pp; p = 0.006 and 0.007). Gains rose with question complexity, reaching +9.4 pp on criteria-based synthesis (p = 0.032), and with record length, reaching +13.5 pp in the top decile (n = 10). The system error rate (12.2%) was comparable to expert disagreement (13.6%), but severity was inverted: 57.8% of system errors were clinically significant versus 18.8% of expert disagreements. Agentic reasoning was the only approach to exceed the shared ceiling, with gains concentrated on the most complex questions and longest records. The greater clinical consequence of residual system errors indicates that prospective evaluation in routine care is required before these findings translate into patient benefit. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_24473 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Agentic clinical reasoning over longitudinal myeloma records: a retrospective evaluation against expert consensus Moll, Johannes Lübberstedt, Jannik Nuernbergk, Christoph Stroh, Jacob Mertens, Luisa Purcarea, Anna Zirn, Christopher Benchaaben, Zeineb Drexel, Fabian Häntze, Hartmut Narayanan, Anirudh Puttkammer, Friedrich Zhukov, Andrei Lammert, Jacqueline Ziegelmayer, Sebastian Graf, Markus Högner, Marion Makowski, Marcus Bassermann, Florian Adams, Lisa C. Pan, Jiazhen Rueckert, Daniel Braitsch, Krischan Bressem, Keno K. Artificial Intelligence Computation and Language Multiple myeloma is managed through sequential lines of therapy over years to decades, with each decision depending on cumulative disease history distributed across dozens to hundreds of heterogeneous clinical documents. Whether LLM-based systems can synthesise this evidence at a level approaching expert agreement has not been established. A retrospective evaluation was conducted on longitudinal clinical records of 811 myeloma patients treated at a tertiary centre (2001-2026), covering 44,962 documents and 1,334,677 laboratory values, with external validation on MIMIC-IV. An agentic reasoning system was compared against single-pass retrieval-augmented generation (RAG), iterative RAG, and full-context input on 469 patient-question pairs from 48 templates at three complexity levels. Reference labels came from double annotation by four oncologists with senior haematologist adjudication. Iterative RAG and full-context input converged on a shared ceiling (75.4% vs 75.8%, p = 1.00). The agentic system reached 79.6% concordance (95% CI 76.4-82.8), exceeding both baselines (+3.8 and +4.2 pp; p = 0.006 and 0.007). Gains rose with question complexity, reaching +9.4 pp on criteria-based synthesis (p = 0.032), and with record length, reaching +13.5 pp in the top decile (n = 10). The system error rate (12.2%) was comparable to expert disagreement (13.6%), but severity was inverted: 57.8% of system errors were clinically significant versus 18.8% of expert disagreements. Agentic reasoning was the only approach to exceed the shared ceiling, with gains concentrated on the most complex questions and longest records. The greater clinical consequence of residual system errors indicates that prospective evaluation in routine care is required before these findings translate into patient benefit. |
| title | Agentic clinical reasoning over longitudinal myeloma records: a retrospective evaluation against expert consensus |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2604.24473 |