{"doi":"10.1111/bju.16024","title":"The <scp> <i>BJUI</i> </scp> Editorial Team's view on artificial intelligence and machine learning","abstract":"Artificial intelligence (AI) aims at simulating, or approximating, human intelligence in machines with the goal of reproducing, replacing or even improving brain tasks of perception, reasoning, and learning. Modern medicine is increasingly digitalised with electronic medical record systems and offers many possibilities to test and use machine learning (ML). Attractive modern applications of these methods include, e.g., AI-driven pathology or imaging interpretation or ML applied to large qualitative interview datasets or electronic medical records fields to identify themes and patterns in text data. Often the goal of utilising ML in a clinical context is to improve the predictive capacity of a model using commonly collected, readily available variables. Boulenger de Hauteclocque et al. [1] tested different ML algorithms to predict upstaging to pathological tumour stage pT3a in patients undergoing surgery for clinical tumour stage cT1/cT2a renal cell carcinoma. The best prediction model achieved an area under the receiver-operating characteristic curve of 0.77. Khene et al. [2] on behalf of the European Association of Urology-Young Academic Urologists (EAU-YAU) Renal Cancer Working Group in their letter to the Editor raise the very relevant issues of: the problem of handling missing data and imputing approaches, adjustable hyperparameters, differentially weighting input values, methods used to evaluate the predictive accuracy of the model, and questioning the clinical relevance of such a model. AI prediction models have made an amazingly rapid introduction and widespread use into clinical management [3] with often insufficient validation, e.g., the Epic Sepsis Model (ESM) widely implemented in United States hospitals and poorly predicting the onset of sepsis [4]. In a recent review of 62 studies that used AI to diagnose COVID-19 from medical scans, Roberts et al. [5] found that none of the models were ready to be deployed clinically for use in diagnosing or predicting the prognosis of COVID-19, because of flaws such as biases in the data, methodology problems, and reproducibility failures. Among the reasons for this poor predictive capacity, they found that publicly available data sets in medicine are scarce, entrenching biases and inequities, and overlap of training and testing data sets leading to often inadvertent duplication of data. From an editorial standpoint, use of AI is becoming an ever increasing challenge and the number of journal submissions on AI has skyrocketed. Kwong et al. [6] point out the current lack of standardised reporting and system explainability when applying ML and refer to the Standardised Reporting of Machine Learning Applications in Urology (STREAM-URO) framework, a concept developed based on a review of the current literature. This initiative to standardise reporting on studies using ML in urology is most welcome; however, depending on the context in which ML is applied, other guidelines and checklists should also be considered (Table 1). For general use and reporting of AI, the European Commission has issued a checklist of relevant principles for AI research, which are Fairness, Universality, Traceability, Usability, Robustness, and Explainability (FUTURE-AI, https://future-ai.eu/). These checklists aim at defining different levels of transparency of the model applied, the training and testing data sets, and how the results are interpreted, factors also relevant for the reviewing process. As editors of the BJUI, we receive a fair amount of AI/ML manuscripts. Unless authors follow reporting guidelines, our initial enthusiasm for fancy-sounding AI/ML models may be dampened if there is an apparent lack of detailed description about the model-building procedures, presentation of the final model (intercept and regression coefficients), and calibration, i.e., the degree to which the estimated model predictions match those observed on external validation (all of which are Transparent Reporting of a multi","journal":"British Journal of Urology","year":2023,"id":380335,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":5,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9563,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1145406,"name":"Tobias Klatte","orcid":"0000-0002-4392-6861","position":1,"is_corresponding":false},{"id":913201,"name":"Nathan Papa","orcid":"0000-0002-3188-1803","position":2,"is_corresponding":false},{"id":253868,"name":"Sigrid Carlsson","orcid":"0000-0003-3553-5710","position":3,"is_corresponding":false},{"id":890720,"name":"George N. Thalmann","orcid":"0000-0002-1908-3286","position":0,"is_corresponding":true}],"reference_count":10,"raw_metadata":null,"created_at":"2026-07-19T01:17:00.789848Z","pmid":"37113110","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}