High-performance medicine: the convergence of human and artificial intelligence

By Eric J. Topol. 2019

7,777 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Health Informatics, Radiology, Nuclear Medicine and Imaging, Medical & Health AI

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Citations in the corpus

Time-series forecasting with deep learning: a survey (2020, background): “Time-series modelling has historically been a key area of academic research—forming an integral part of applications in topics such as climate modelling [1], biological sciences [2] and medicine [3], as well as commercial decision making in retail [4] and finance [5] to name a few.”
A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI (2019, background): “We will not attempt to cover all the related works many of which are already presented in the research and survey works we cite [1] [2] [9] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23].”
Improving the accuracy of medical diagnosis with causal machine learning (2020, neutral): “In Supplementary Note 3 we describe how these counterfactual probabilities are calculated.”
Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning (2019, background): “Policymakers and practitioners assert serious usability concerns that constrain adoption, notably in high-consequence domains (Topol 2019).”
The Ethical Implications of Using Artificial Intelligence in Auditing (2020, background): “Topol (2019) documents a host of AI applications used in the medical field to scan patient data rapidly and provide clinicians with accurate interpretation to facilitate diagnosis.”
The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies (2020, background): “Lack of transparency is identified as one of the key barriers to implementation [5,6].”
The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database (2020, background): “Despite its promise, the obstacles for implementation of AI/ML in daily clinical practice are numerous 10 .”
Fairness of artificial intelligence in healthcare: review and recommendations (2023, background): “Collaboration among physicians, AI researchers, and AI developers is essential to address fairness concerns in AI [108, 110].”
Quo vadis artificial intelligence? (2022, background): “The healthcare industry covers all the attributes from patients to doctors, from platform to end users, from groups to individuals [105].”
Artificial Intelligence for Clinical Oncology (2021, background): “…20 Food and Drug Administration (FDA)-approved AI applications targeted speciﬁcally for clinical oncology, and each of these performs a narrow task, utilizing a single data stream at a speciﬁc cancer care touchpoint (Benja-mens et al., 2020; Hamamoto et al., 2020; Topol, 2019) (Table 1).”
AI in Health: State of the Art, Challenges, and Future Directions (2019, background): “This may raise the potential risk of harm when these models are applied in clinical practice [122].”
Language Models are Few-shot Learners for Prognostic Prediction (2023, background): “Given the fact that most clinical data is stored in tabular form, current mainstream machine learning approaches (Topol, 2019; Rajkomar et al., 2019) to cancer prognosis (Chowell et al.”