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A reimbursement framework for artificial intelligence in healthcare.
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Health Technology Assessment for Cardiovascular Digital Health Technologies and Artificial Intelligence: Why Is It Different?
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Stakeholders' Requirements for Artificial Intelligence for Healthcare in Korea.
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Artificial intelligence framework for simulating clinical decision-making: a Markov decision process approach.
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Artificial Intelligence: Threat or Boon to Radiologists?
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A framework for examining patient attitudes regarding applications of artificial intelligence in healthcare.
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Exploring healthcare professionals' perceptions of artificial intelligence: Piloting the Shinners Artificial Intelligence Perception tool.
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Explainable Artificial Intelligence for Predictive Modeling in Healthcare.
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Registries, Databases and Repositories for Developing Artificial Intelligence in Cancer Care.
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Artificial intelligence for cardiac imaging is ready for widespread clinical use: Pro Con debate AI for cardiac imaging.
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Commercialization of medical artificial intelligence technologies: challenges and opportunities.
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An early pipeline framework for assessing vendor AI solutions to support return on investment.
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Primary Care Provider Preferences Regarding Artificial Intelligence in Point-of-Care Cancer Screening.
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Artificial intelligence optimizes the standardized diagnosis and treatment of chronic sinusitis.
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Generalist medical AI reimbursement challenges and opportunities.
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Cost-effectiveness of AI for pediatric diabetic eye exams from a health system perspective.
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1
Patient apprehensions about the use of artificial intelligence in healthcare.
NPJ Digit Med. 2021 Sep 21;4(1):140. doi: 10.1038/s41746-021-00509-1.
2
Foundational Considerations for Artificial Intelligence Using Ophthalmic Images.
Ophthalmology. 2022 Feb;129(2):e14-e32. doi: 10.1016/j.ophtha.2021.08.023. Epub 2021 Aug 31.
3
Who Will Pay for AI?
Radiol Artif Intell. 2021 Mar 3;3(3):e210030. doi: 10.1148/ryai.2021210030. eCollection 2021 May.
4
Racial/Ethnic Disparities and Barriers to Diabetic Retinopathy Screening in Youths.
JAMA Ophthalmol. 2021 Jul 1;139(7):791-795. doi: 10.1001/jamaophthalmol.2021.1551.
7
Approaching autonomy in medical artificial intelligence.
Lancet Digit Health. 2020 Sep;2(9):e447-e449. doi: 10.1016/S2589-7500(20)30187-4.
8
Identifying Ethical Considerations for Machine Learning Healthcare Applications.
Am J Bioeth. 2020 Nov;20(11):7-17. doi: 10.1080/15265161.2020.1819469.
9
Cost-effectiveness of Autonomous Point-of-Care Diabetic Retinopathy Screening for Pediatric Patients With Diabetes.
JAMA Ophthalmol. 2020 Oct 1;138(10):1063-1069. doi: 10.1001/jamaophthalmol.2020.3190.
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Implications for Telehealth in a Postpandemic Future: Regulatory and Privacy Issues.
JAMA. 2020 Jun 16;323(23):2375-2376. doi: 10.1001/jama.2020.7943.

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