ArticleEBioMedicine2025
Why implementing machine learning algorithms in the clinic is not a plug-and-play solution: a simulation study of a machine learning algorithm for acute leukaemia subtype diagnosis.
Article in EBioMedicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Incorporating artificial intelligence into morphological diagnosis of acute leukemias: Current landscape, challenges and prospects (Review).Oncology reports · 2026Review
- [Recent advances in individualized treatment for pediatric high-risk B-cell acute lymphoblastic leukemia].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) and machine learning (ML) algorithms have shown great promise in clinical medicine. Despite the increasing number of published algorithms, most remain unvalidated in real-world clinical settings. This study aims to simulate the practical implementation challenges of a recently developed ML algorithm, AI-PAL, designed for the diagnosis of acute leukaemia and report on its performance.
methodsWe conducted a detailed simulation of the AI-PAL algorithm's implementation at the University Hospital Essen. Cohort building was performed using our Fast Healthcare Interoperability Resources (FHIR) database, identifying all initially diagnosed patients with acute leukaemia and selected differential diagnoses. The algorithm's performance was assessed by reproducing the original study's results.
findingsThe AI-PAL algorithm demonstrated significantly lower performance in our simulated clinical implementation compared to prior published results. The area under the receiver operating characteristic curve for acute lymphoblastic leukaemia dropped to 0.67 (95% CI: 0.61-0.73) and for acute myeloid leukaemia to 0.71 (95% CI: 0.65-0.76). The recalibration of probability cutoffs determining confident diagnoses increased the number of confident positive diagnosis for acute leukaemia from 98 to 160, highlighting the necessity of local validation and adjustments.
interpretationThe findings underscore the challenges of implementing ML algorithms in clinical practice. Despite robust development and validation in research settings, ML models like AI-PAL may require significant adjustments and recalibration to maintain performance in different clinical settings. Our results suggest that clinical decision support algorithms should undergo local performance validation before integration into routine care to ensure reliability and safety.
fundingThis study was supported by the DFG-cofounded UMEA Clinician Scientist Program and the Ministry of Culture and Science of the State of North Rhine-Westphalia.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.