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Towards a framework for interoperability and reproducibility of predictive models
5 references·0 citing·22 second hop
Towards a framework for interoperability and reproducibility of predictive models (2023)Towards a framework for interoperability …Obligations to assess: Recent trends in AI accountability regulations (2022)Obligations to assess: Recent…Reporting quality of studies using machine learning models for medical diagnosis: a systematic review (2020)Reporting quality of studies …Artificial intelligence faces reproducibility crisis (2018)Artificial intelligence faces…Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement (2015)Transparent reporting of a mu…Replicability is not Reproducibility:Nor is it Good Science (2009)Replicability is not Reproduc…Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement (2009)Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement (2009)Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement. (2009)Mobile health solutions for the aging population: A systematic narrative analysis (2016)Reducing the stigma of mental illness (2016)A validated predictive algorithm of post-traumatic stress course following emergency department admission after a traumatic stressor (2020)Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement (2009)Completeness of reporting of clinical prediction models developed using supervised machine learning: A systematic review (2021)Review of study reporting guidelines for clinical studies using artificial intelligence in healthcare (2021)Quality of reporting of randomised controlled trials of artificial intelligence in healthcare: a systematic review (2022)Big Data's Disparate Impact (2014)Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (2021)Towards a standard for identifying and managing bias in artificial intelligence (2022)Taking Algorithms to Courts: A Relational Approach to Algorithmic Accountability (2023)Comparative technical analysis of legal and ethical frameworks in AI-enhanced procurement processes (2024)Algorithmic accountabilities and health systems: A review and sociomaterial approach (2025)Journal of Computational Neuroscience (2013)On the scientific superiority of conceptual replications for scientific progress (2016)Scientific workflows for computational reproducibility in the life sciences: Status, challenges and opportunities (2017)Insights From “The Machine Stops ” to Better Understand Rational Assumptions in Algorithmic Decision Making and Its Implications for Organizations (2019)Artificial intelligence and the conduct of literature reviews (2021)The AI gambit: leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations (2021)
this work references cited by second hop

References

  • 2015Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement1,920 cited
  • 2018Artificial intelligence faces reproducibility crisis567 cited
  • 2009Replicability is not Reproducibility:Nor is it Good Science231 cited
  • 2020Reporting quality of studies using machine learning models for medical diagnosis: a systematic review102 cited
  • 2022Obligations to assess: Recent trends in AI accountability regulations32 cited

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