Implementing Machine Learning in Health Care — Addressing Ethical Challenges

Char, Danton; Shah, Nigam H.; Magnus, David

doi:10.1056/nejmp1714229

Cited by 929 publications

(671 citation statements)

References 4 publications

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“…In addition, a safeguard against “learned helplessness” must be used as a means to curb high reliance on automation and the ultimate abandonment of common sense. Finally, automated systems also might challenge the dynamics of responsibility within the doctor‐patient relationship, as well as the expectation of confidentiality …”

Section: Challenges and Future Directionsmentioning

confidence: 99%

Artificial intelligence in cancer imaging: Clinical challenges and applications

Hosny

Schabath

et al. 2019

CA A Cancer J Clinicians

1,143

820

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Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care.

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Section: Challenges and Future Directionsmentioning

confidence: 99%

Artificial intelligence in cancer imaging: Clinical challenges and applications

Hosny

Schabath

et al. 2019

CA A Cancer J Clinicians

1,143

820

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“…8 As a result, the algorithms may not offer benefit to people whose data are missing from the data set. 9 …”

Section: Missing Data and Patients Not Identified By Algorithmsmentioning

confidence: 99%

“…Automation is important, but overreliance on automation is not desirable. 8,20 Computer scientists and bioinformaticians, together with practitioners, biostatisticians, and epidemiologists, should outline the “intent behind the design,” 9(p982) including choosing appropriate questions and settings for machine learning use, interpreting findings, and conducting follow-up studies. Such measures would increase the likelihood that the results of the models are meaningful and ethical and that clinical decision support tools based on these algorithms have beneficial effects.…”

Section: Recommendationsmentioning

confidence: 99%

Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data

et al. 2018

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A promise of machine learning in health care is the avoidance of biases in diagnosis and treatment; a computer algorithm could objectively synthesize and interpret the data in the medical record. Integration of machine learning with clinical decision support tools, such as computerized alerts or diagnostic support, may offer physicians and others who provide health care targeted and timely information that can improve clinical decisions. Machine learning algorithms, however, may also be subject to biases. The biases include those related to missing data and patients not identified by algorithms, sample size and underestimation, and misclassification and measurement error. There is concern that biases and deficiencies in the data used by machine learning algorithms may contribute to socioeconomic disparities in health care. This Special Communication outlines the potential biases that may be introduced into machine learning–based clinical decision support tools that use electronic health record data and proposes potential solutions to the problems of overreliance on automation, algorithms based on biased data, and algorithms that do not provide information that is clinically meaningful. Existing health care disparities should not be amplified by thoughtless or excessive reliance on machines.

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“…Clinical data sets tend to be small, and models trained on limited observations of a certain type of data (eg, recorded in a silent room, Caucasian speakers, adults) may not even extrapolate to data that seems to be similar. Furthermore, algorithms are susceptible to learning biases inherent in the data used to train them (eg, incorrectly assigning lower disorder severity to African Americans because less of them have the disorder in the training set) . Critically, many high‐performing algorithms (eg, deep neural networks, proprietary models) are “black boxes,” since it is currently not understood how these models combine features to output the severity of a disorder.…”

Section: Introductionmentioning

confidence: 99%

Automated assessment of psychiatric disorders using speech: A systematic review

Low

Bentley

Ghosh

2020

Laryngoscope Investig Oto

289

268

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Objective There are many barriers to accessing mental health assessments including cost and stigma. Even when individuals receive professional care, assessments are intermittent and may be limited partly due to the episodic nature of psychiatric symptoms. Therefore, machine‐learning technology using speech samples obtained in the clinic or remotely could one day be a biomarker to improve diagnosis and treatment. To date, reviews have only focused on using acoustic features from speech to detect depression and schizophrenia. Here, we present the first systematic review of studies using speech for automated assessments across a broader range of psychiatric disorders. Methods We followed the Preferred Reporting Items for Systematic Reviews and Meta‐Analysis (PRISMA) guidelines. We included studies from the last 10 years using speech to identify the presence or severity of disorders within the Diagnostic and Statistical Manual of Mental Disorders (DSM‐5). For each study, we describe sample size, clinical evaluation method, speech‐eliciting tasks, machine learning methodology, performance, and other relevant findings. Results 1395 studies were screened of which 127 studies met the inclusion criteria. The majority of studies were on depression, schizophrenia, and bipolar disorder, and the remaining on post‐traumatic stress disorder, anxiety disorders, and eating disorders. 63% of studies built machine learning predictive models, and the remaining 37% performed null‐hypothesis testing only. We provide an online database with our search results and synthesize how acoustic features appear in each disorder. Conclusion Speech processing technology could aid mental health assessments, but there are many obstacles to overcome, especially the need for comprehensive transdiagnostic and longitudinal studies. Given the diverse types of data sets, feature extraction, computational methodologies, and evaluation criteria, we provide guidelines for both acquiring data and building machine learning models with a focus on testing hypotheses, open science, reproducibility, and generalizability. Level of Evidence 3a

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Implementing Machine Learning in Health Care — Addressing Ethical Challenges

Cited by 929 publications

References 4 publications

Artificial intelligence in cancer imaging: Clinical challenges and applications

Artificial intelligence in cancer imaging: Clinical challenges and applications

Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data

Automated assessment of psychiatric disorders using speech: A systematic review

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