The role of patient-reported outcome measures in trials of artificial intelligence health technologies: a systematic evaluation of ClinicalTrials.gov records (1997–2022)

Zhang, 2022 He, 2019, The practical implementation of artificial intelligence technologies in medicine, Nat Med, 25, 30, 10.1038/s41591-018-0307-0 Topol, 2019, High-performance medicine: the convergence of human and artificial intelligence, Nat Med, 25, 44, 10.1038/s41591-018-0300-7 Leddy, 2019, Improving proteinuria screening with mailed smartphone urinalysis testing in previously unscreened patients with hypertension: a randomized controlled trial, BMC Nephrol, 20, 132, 10.1186/s12882-019-1324-z Black, 2013, Patient reported outcome measures could help transform healthcare, BMJ, 346, f167, 10.1136/bmj.f167 Patrick, 1989, Generic and disease-specific measures in assessing health status and quality of life, Med Care, 27, S217, 10.1097/00005650-198903001-00018 Kaasa, 1995, The EORTC core quality of life questionnaire (QLQ-C30): validity and reliability when analysed with patients treated with palliative radiotherapy, Eur J Cancer, 31A, 2260, 10.1016/0959-8049(95)00296-0 Juniper, 1996, Measuring quality of life in children with asthma, Qual Life Res, 5, 35, 10.1007/BF00435967 Feng, 2021, Psychometric properties of the EQ-5D-5L: a systematic review of the literature, Qual Life Res, 30, 647, 10.1007/s11136-020-02688-y Brazier, 1992, Validating the SF-36 health survey questionnaire: new outcome measure for primary care, BMJ, 305, 160, 10.1136/bmj.305.6846.160 Till, 1994, Research on health-related quality of life: dissemination into practical applications, Qual Life Res, 3, 279, 10.1007/BF00434902 Mitchell, 2014 Sanders, 1998, Reporting on quality of life in randomised controlled trials: bibliographic study, BMJ, 317, 1191, 10.1136/bmj.317.7167.1191 Coon, 2016, The use of patient-reported outcomes in demonstrating safety and efficacy in oncology, Clin Ther, 38, 756, 10.1016/j.clinthera.2016.03.015 Vodicka, 2015, Inclusion of patient-reported outcome measures in registered clinical trials: evidence from ClinicalTrials.gov (2007–2013), Contemp Clin Trials, 43, 1, 10.1016/j.cct.2015.04.004 Young, 2021, Patient and general public attitudes towards clinical artificial intelligence: a mixed methods systematic review, Lancet Digit Health, 3, e599, 10.1016/S2589-7500(21)00132-1 Cruz Rivera, 2023, Embedding patient-reported outcomes at the heart of artificial intelligence health-care technologies, Lancet Digit Health, 5, e168, 10.1016/S2589-7500(22)00252-7 Kyte, 2019, Systematic evaluation of patient-reported outcome protocol content and reporting in cancer trials, J Natl Cancer Inst, 111, 1170, 10.1093/jnci/djz038 Maruszczyk, 2022, Implementation of patient-reported outcome measures in real-world evidence studies: analysis of ClinicalTrials.gov records (1999–2021), Contemp Clin Trials, 120, 10.1016/j.cct.2022.106882 Zwolan, 2021, Investigation of an outcomes-driven, computer-assisted approach to CI fitting in newly implanted patients, Ear Hear, 42, 558, 10.1097/AUD.0000000000000957 Zhu, 2022, Enhancing self-management in type 1 diabetes with wearables and deep learning, NPJ Digit Med, 5, 78, 10.1038/s41746-022-00626-5 Piette, 2022, Patient-centered pain care using artificial intelligence and mobile health tools: a randomized comparative effectiveness trial, JAMA Intern Med, 182, 975, 10.1001/jamainternmed.2022.3178 Mohr, 2017, IntelliCare: an eclectic, skills-based app suite for the treatment of depression and anxiety, J Med Internet Res, 19, e10, 10.2196/jmir.6645 Mohr, 2019, Comparison of the effects of coaching and receipt of app recommendations on depression, anxiety, and engagement in the IntelliCare platform: factorial randomized controlled trial, J Med Internet Res, 21, 10.2196/13609 Livovsky, 2021, Detection of elusive polyps using a large-scale artificial intelligence system (with videos), Gastrointest Endosc, 94, 1099, 10.1016/j.gie.2021.06.021 Jaroszewski, 2019, Randomized controlled trial of an online machine learning-driven risk assessment and intervention platform for increasing the use of crisis services, J Consult Clin Psychol, 87, 370, 10.1037/ccp0000389 Eng, 2021, Artificial intelligence algorithm improves radiologist performance in skeletal age assessment: a prospective multicenter randomized controlled trial, Radiology, 301, 692, 10.1148/radiol.2021204021 Buegler, 2020, Digital biomarker-based individualized prognosis for people at risk of dementia, Alzheimers Dement (Amst), 12 Kroenke, 2001, The PHQ-9: validity of a brief depression severity measure, J Gen Intern Med, 16, 606, 10.1046/j.1525-1497.2001.016009606.x Spitzer, 2006, A brief measure for assessing generalized anxiety disorder: the GAD-7, Arch Intern Med, 166, 1092, 10.1001/archinte.166.10.1092 Snaith, 2003, The Hospital Anxiety and Depression Scale, Health Qual Life Outcomes, 1, 29, 10.1186/1477-7525-1-29 Zhou, 2022, Application of patient-reported outcome measurements in clinical trials in China, JAMA Netw Open, 5, 10.1001/jamanetworkopen.2022.11644 Cruz Rivera, 2020, Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension, Nat Med, 26, 1351, 10.1038/s41591-020-1037-7 Calvert, 2018, Guidelines for inclusion of patient-reported outcomes in clinical trial protocols: the SPIRIT-PRO extension, JAMA, 319, 483, 10.1001/jama.2017.21903