Do no harm: a roadmap for responsible machine learning for health care

By Jenna Wiens, Suchi Saria, Mark P. Sendak, Marzyeh Ghassemi, V. Liu, Finale Doshi-Velez, Kenneth Jung, Katherine A. Heller, David C. Kale, Mohammed Saeed and 3 more. Nature Medicine, 2019

925 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Health Informatics, Public Health, Environmental and Occupational Health

View Paper: https://www.semanticscholar.org/paper/b0f8a829450e782fe879d9d48a188d611b6dd74d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Health Informatics

In Public Health, Environmental and Occupational Health

Citations in the corpus

Explainable, trustworthy, and ethical machine learning for healthcare: A survey (2021, background): “In this regard, a few studies suggested involving all types of stakeholders into the ML/DL method development process that may include clinicians, policymakers, data scientists, ML researchers, and hospital staff, to name a few [182], [176].”
Bias in artificial intelligence algorithms and recommendations for mitigation (2023, methodology): “clinically relevant and meaningful as well as have an impact on clinical practice [18,19].”
Interpretability of machine learning‐based prediction models in healthcare (2020, background): “Recently a broader concept of responsible ML in healthcare was presented (Wiens, et al., 2019), which proposes that an interdisciplinary team of engaged stakeholders (policy makers, health system leaders and individual researchers) systematically progress from problem formulation to widespread…”
Trustworthy artificial intelligence (2020, background): “…for beneficial AI (Future of Life Institute 2017), responsible AI (Chinese National Governance Committee for the New Generation Artificial Intelligence 2019; Université de Montréal 2017; Wiens et al. 2019) , or ethical AI (Floridi et al. 2018; UK House of Lords 2017) during the last few years.”
Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension (2020, neutral): “51,52 For example, it may become unclear whether an error case occurred due to human deviation from the instructed procedure, or if it was an error made by the AI system.”
Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022, background): “The literature advises to clearly de ﬁ ne the intended use and intended users in the preparation of both the feasibility and impact study 12,19,64,65 .”
Capabilities of GPT-4 on Medical Challenge Problems (2023, background): “Hematuria (blood in the urine) can occur in AKI, and the patient’s recent NSAID use increases the likelihood of this being the cause.”
Secure and Robust Machine Learning for Healthcare: A Survey (2020, background): “In a recent study [175], Wiens et al.”