Machine Learning and Prediction in Medicine — Beyond the Peak of Inflated Expectations

By Jonathan H. Chen, S. Asch. 2017

920 citations recorded by the source corpus.

Big data, we have all heard, promise to transform health care. But in the “hype cycle” of emerging technologies, machine learning now rides atop the “peak of inflated expectations,” and we need to better appreciate the technology’s capabilities and limitations.

Subfields: Epidemiology, Health Informatics, Health Information Management

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Citations in the corpus

Beyond Sparsity: Tree Regularization of Deep Models for Interpretability (2017, background): “2015)), the clinical community remains skeptical of machine learning systems (Chen and Asch 2017).”
A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. (2019, methodology): “The rationale is that LR has been the standard method for clinical prediction, and more modern approaches are often discussed in relation to LR [6,7,14-17,104,107].”
Artificial intelligence in retina. (2018, background): “Although predictive algorithms are unable to provide absolute medical certainty, they may strongly improve allocations of stressed healthcare resources by helping to plan large-scale patient care, comparing the efficiency of therapeutic substances and suggesting sound treatment indications, which is”
Human–AI collaboration enables more empathic conversations in text-based peer-to-peer mental health support (2022, background): “This design reflects the high-stakes setting of mental health, where AI is probably best used to augment rather than replace human skills 46,51 .”
Current Applications and Future Impact of Machine Learning in Radiology. (2018, positive): “Machine learning approaches to the interrogation of a wide spectrum of such data (sociodemographic, imaging, clinical, laboratory, and genetic) has the potential to further personalize health care, far beyond what would be possible through imaging applications alone (89).”
Multimodal biomedical AI (2022, background): “The first requirement for the successful development of multi-modal data-enabled applications is the collection, curation and harmonization of well-phenotyped and large annotated datasets, as no amount of technical sophistication can derive information not present in the data 80 .”
Variable-Shot Adaptation for Online Meta-Learning (2020, background): “Indeed, the real world is arguably so complex that, for all practical purposes, learning is never truly finished, and any real system in open-world settings will need to improve and finetune perpetually (Chen & Asch, 2017; Zhao et al., 2019).”
External validation of prognostic models: what, why, how, when and where? (2020, background): “In recent years, prediction models based on artificial intelligence and machine learning have become a hot topic [38].”
Artificial Intelligence in Surgery: Promises and Perils (2018, background): “An understanding of AI will be key to appropriately conveying the results of complex analyses such as risk predictions, prognostications, and treatment algorithms to patients within the appropriate clinical context.(4,71) Working with patients, surgeons should develop and deliver the narrative behin”