{"doi":"10.1002/jhm.13364","title":"Artificial intelligence, ethics, and hospital medicine: Addressing challenges to ethical norms and patient‐centered care","abstract":"Recent advances in artificial intelligence (AI) will transform how we care for hospitalized patients.1-3 Although AI has been present in healthcare for many years,4, 5 advances in the underlying models and improved computational power will enable AI to be seamlessly integrated into the routine practice of hospital medicine.4, 6, 7 Hospitalists are likely to use AI in a number of ways, including triaging patients, automating progress notes, diagnosing patients, assessing risk, and identifying treatment plans.8, 9 Since AI models are trained on vast amounts of patient data, they will be able to complete these tasks with increasing speed, accuracy, and reliability—compared to humans or current systems.2, 10 As with any new disruptive technology with the potential to affect patients' care and their health outcomes, hospitalists should be attentive to the ethical concerns AI may create. As front-line providers, hospitalists' attentiveness to ethical concerns will help ensure that the use of AI in hospital medicine practice is ethically sound. Powerful AI models are challenging established ethical norms of patient data ownership, privacy, and security,4, 11 since the sheer scope of data needed for AI applications is changing how healthcare data is acquired, used, and maintained.11-13 As a result, hospitalists will need to understand and be able to explain the ethical implications of these changes to patients and their families in service of transparency and patient trust. Moreover, AI applications in hospital medicine will change clinical practice and how hospitalists pursue high-quality patient-centered care. Hospitalists will need to be active agents in monitoring whether changes in clinical practice also challenge established ethical norms of providing care that benefits patients, minimizes harm, and respects their values and preferences. Recognizing the ethical implications of AI will help hospitalists responsibly use AI to promote patients' interests, and this editorial discusses a few of the current ethical challenges of AI and the hospitalists' role in addressing them. One of the major promises of AI is its ability to identify valuable patterns in routinely generated healthcare data (i.e., prescribing, demographic, clinical, biospecimen, communication, etc.). Based on those patterns, AI can generate new data to answer medical questions and/or improve clinical tasks. For decades, the ethical use of patient data has been legitimated by the principle of informed consent, which requires patients to receive relevant information and be provided a meaningful choice about the use of their data. Informed consent makes it ethically permissible to use a patient's data because a patient's right and ability to decide if and how their data is used allows for self-determination—a central tenet of the ethical principle of patient autonomy.3, 13, 14 Before AI, this ethical norm was being challenged by the social value of and impracticability of obtaining informed consent for large amounts of healthcare data. Due to its requirement for vast amounts of healthcare data, AI will almost certainly accelerate the challenge to informed consent and erode current norms that underpin the ethical use of patient data.15 At times, hospitalists may be responsible for articulating to patients who may not be fully aware of this shift in practice, how their health data is being used, and what safeguards are in place to protect it. These conversations will be necessary to maintain trust and mitigate losses in patient autonomy, especially given there are larger social debates occurring about trust in healthcare. This raises another important question: As AI is increasingly integrated into and relied on to deliver patient care, will hospitalists be required or able to obtain informed consent for its use in directing patient care? It is already the case that patient consent is not required when health systems deploy clinical decision support (CDS) tools embedded w","journal":"Journal of Hospital Medicine","year":2024,"id":485138,"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":6,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9619,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":743315,"name":"David Alfandre","orcid":"0000-0001-9778-7580","position":1,"is_corresponding":false},{"id":514734,"name":"Micah T. Prochaska","orcid":"0000-0002-5629-0137","position":0,"is_corresponding":true}],"reference_count":19,"raw_metadata":null,"created_at":"2026-07-19T02:07:47.633574Z","pmid":"38650109","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":[]}