{"doi":"10.34067/kid.0004152021","title":"Course Corrections for Clinical AI","abstract":"Introduction The translation of artificial intelligence (AI) from a promising technology to a routine clinical tool is already well underway, but its benefits to patients and their physicians are not assured, requiring additional guidance from clinicians and industry regulators. In this perspective article, we review the evidence that clinical AI benefits or harms patients and their medical providers. We argue that although clinical AI has already created positive change in medicine, stakeholders such as physicians, hospital administrators, and regulators must demand higher standards of evidence, improved auditing capabilities, and greater attention toward workers’ quality of life if we are to maximize this technology’s potential. Recently, a boom in AI in medicine has occurred, driven by modern machine learning (ML) methods, whose ability to automatically learn patterns from large amounts of data has enabled them to widely surpass earlier AI systems that required more manual programming by human experts. Although the idiosyncrasies of ML are occasionally relevant to clinicians, in this article we more broadly define AI to include both ML and other computational approaches that automate intellectual tasks typically reserved for human intelligence. In this way, we consider AI from an operational perspective, namely, in terms of its effect on health care workers and patients. For any type of medical AI system, the salient clinical questions remain the same: how does AI effect patients? How does it affect physicians? And what should we do to make it better? How Does AI Affect Patients? AI affects patients by informing diagnosis or treatment decisions through the processing of medical images, physiologic measurements, or health records; these technologies, known as “clinical decision support systems,” may decrease cost and improve patient outcomes. Dozens of AI systems have been approved by the United States Food and Drug Administration (FDA), the majority of which analyze radiologic images, whereas the next most common subset monitors cardiac function. Miscellaneous other systems analyze other medical images, monitor other physiologic signals, act as digital assistants, or provide automated therapy (1). To assess the potential benefits of these systems, we reviewed publicly available FDA application summaries and peer-reviewed publications and found a few devices show benefit in prospective clinical trials. In evaluating these devices, attention to high-quality evidence, namely, prospective data, ideally from controlled trials and concerning clinically meaningful end points, is critical, because the de facto standard for assessment of AI systems in the ML community is evaluation of predictive performance (e.g., accuracy at disease classification) on retrospective data, which may overestimate real-world performance and, more importantly, disregards patient outcomes. In a success case, a prospective clinical trial of intracranial hemorrhage detection software that triages computed tomography scans demonstrated reduced time to diagnosis of intracranial hemorrhage (2), and a number of similar software devices for triage in radiology have since come to market. Similarly, a randomized clinical trial of 68 patients in a single center demonstrated that an AI-based early warning system for hypotension events reduced the frequency and severity of hypotensive episodes during surgery (3). A small prospective trial of three nurses with 30 patients each evidenced that an AI sonography assistant can enable nurses who were not trained in sonography to acquire satisfactory ultrasound images (4), which could perhaps improve access to this imaging modality. Finally, in a randomized clinical trial of 142 patients, an ML-based sepsis prediction tool reduced hospital length of stay and mortality (5); although larger, multicenter trials will be necessary to confirm the generalizability of this result, this study provides rare evidence of meaningful imp","journal":"Kidney360","year":2021,"id":205560,"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":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.959,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":95805,"name":"Joseph D. Janizek","orcid":"0000-0003-1804-7133","position":1,"is_corresponding":false},{"id":15549,"name":"Su-In Lee","orcid":"0000-0001-5833-5215","position":2,"is_corresponding":false},{"id":54244,"name":"Alex J. DeGrave","orcid":"0000-0001-9933-6273","position":0,"is_corresponding":true}],"reference_count":40,"raw_metadata":null,"created_at":"2026-07-18T23:51:33.996102Z","pmid":"35419524","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":[]}