HosnyA, ParmarC, QuackenbushJ, et al.Artificial intelligence in radiology. Nat Rev Cancer, 2018; 18(8):500–510; doi: 10.1038/s41568-018-0016-5
2.
RajkomarA, DeanJ, KohaneI. Machine learning in medicine. N Engl J Med, 2019; 380(14):1347–1358; doi: 10.1056/NEJMra1814259
3.
VuE, SteinmannN, SchröderC, et al.Applications of machine learning in palliative care: A systematic review. Cancers (Basel), 2023; 15(5):1596; doi: 10.3390/cancers15051596
4.
AyersJW, PoliakA, DredzeM, et al.Comparing physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum. JAMA Intern Med, 2023; 183(6):589–596; doi: 10.1001/jamainternmed.2023.1838
5.
BurryN, NakagawaS, BlindermanCD. “You Are Not Alone”: The Allure and Limitations of Artificial Intelligence in Serious Illness Communication. J Palliat Med, 2024; 27(1):7–9; doi: 10.1089/jpm.2023.0471
6.
OmiyeJA, LesterJC, SpichakS, et al.Large language models propagate race-based medicine. NPJ Digit Med, 2023; 6(1):195; doi: 10.1038/s41746-023-00939-z
7.
WilsonPM, RamarP, PhilpotLM, et al.Effect of an artificial intelligence decision support tool on palliative care referral in hospitalized patients: A randomized clinical trial. J Pain Symptom Manage, 2023; 66(1):24–32; doi: 10.1016/j.jpainsymman.2023.02.317
8.
GalloRJ, ShiehL, SmithM, et al.Effectiveness of an artificial intelligence-enabled intervention for detecting clinical deterioration. JAMA Intern Med, 2024; 184(5):557–562; doi: 10.1001/jamainternmed.2024.0084
9.
AvatiA, JungK, HarmanS, et al.Improving palliative care with deep learning. BMC Med Inform Decis Mak, 2018; 18(Suppl 4):122; doi: 10.1186/s12911-018-0677-8
10.
ZhangH, LiY, McConnellW. Predicting potential palliative care beneficiaries for health plans: A generalized machine learning pipeline. J Biomed Inform, 2021; 123:103922; doi: 10.1016/j.jbi.2021.103922
11.
LeeRY, BrumbackLC, LoberWB, et al.Identifying goals of care conversations in the electronic health record using natural language processing and machine learning. J Pain Symptom Manage, 2021; 61(1):136–142.e2; doi: 10.1016/j.jpainsymman.2020.08.024
12.
ForsythAW, BarzilayR, HughesKS, et al.Machine learning methods to extract documentation of breast cancer symptoms from electronic health records. J Pain Symptom Manage, 2018; 55(6):1492–1499; doi: 10.1016/j.jpainsymman.2018.02.016
13.
WangJ, YangJ, ZhangH, et al.PhenoPad: Building AI enabled note-taking interfaces for patient encounters. NPJ Digit Med, 2022; 5(1):12; doi: 10.1038/s41746-021-00555-9
14.
StamerT, SteinhäuserJ, FlägelK. Artificial intelligence supporting the training of communication skills in the education of health care professions: Scoping review. J Med Internet Res, 2023; 25:e43311; doi: 10.2196/43311
15.
SteimersA, SchneiderM. Sources of risk of AI systems. Int J Environ Res Public Health, 2022; 19(6):3641; doi: 10.3390/ijerph19063641
16.
TurchinA, DenkenbergerD. Classification of global catastrophic risks connected with artificial intelligence. AI & Soc, 2020; 35(1):147–163; doi: 10.1007/s00146-018-0845-5
17.
ShahC, NachandD, WaldC, et al.Keeping patient data secure in the age of radiology artificial intelligence: Cybersecurity considerations and future directions. J Am Coll Radiol, 2023; 20(9):828–835; doi: 10.1016/j.jacr.2023.06.023
18.
SamuelsonP. Generative AI meets copyright. Science, July; 381(6654):158–161; doi: 10.1126/science.adi065