Data, Ethics and Healthcare

Data, Ethics and Healthcare

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  Data, Ethics and Health Care May16th 2019
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  How will the evolutionof AI condition the means, goals, and ethics of the Life Sciences? ©2019 Business Analytics Institute • Artificial Intelligence is progressively blurring the lines between biology, technology, and society. • This interdependence challenges our notions of mortality, morbidity, and healthcare. Introduction
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  Health analytics harnessesdata science to improve decision- making. • EHR improves patient safety and outcomes • Machine learning greatly reduces misdiagnosis • Robotics improves the precision of micro-surgery • Algorithms can significantly reduce fraud NetObjex
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  AI The Road toArtificial Intelligence Machine Learning Artificial Intelligence Nature Knowledge Intelligence Vision Self-learning Algorithms Mimic human Behavior Use Scenario Learns from data Solve Complex Problem Aim Sufficient Solution Optimal Solution Metrics Accuracy Success What do we mean by Artificial Intelligence?
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  Being Human What doesit mean to be human? • Autonomy: our capacity to make informed decisions • Agency: the capacity to act independently • Empathy - the capability to understand and to relate • Ethics – shared values to differentiate right from wrong • Intelligence - our ability to acquire and apply knowledge Well-being How will health analytics condition how the medical profession defines well-being? realboxsite.tk
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  6©2017 LHST sarl •The responsibility for improving their own well-being • Little Data – the use of personal data to improve “care of oneself”. • The adoption of adoption of wearable and ambient technologies • Patients are claiming a voice at the table Little Data Should the medical profession focus solely on the patients they treat or be incentivized for the well-being of the populations they serve? Usbek & Rica
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  • AI willmove the goal posts of the medical profession • Descriptive and prescriptive analytics are growing exponentially • We now leverage AI diagnoses without having to bear the costs and time constraints • The time to study the model, the code, nor the training data • New definitions of “well-being”, “confidentiality”, “truthfulness” and “trust” 7 Game Changer To what extent does the medical profession need to understand how AI changes medical practice?
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  Implicit Bias • Algorithmslearn by processing past experience • The importance of profiling and classification • These profiles reflect several human biases • Bias in the data, in AI, in teaching AI human rules, in evaluating cases Who will be ultimately held responsible for the implicit bias of artificial intelligence? Medium
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  • AI doesn’tfuel innovation • AI learns from variables that can be empirically measured • AI can’t explore all the possible features • AI at best mimics rational intellilgence Innovation Which types of intelligence will be vital to future innovation in healthcare? CMO.com
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  • Value inimproving medical imagery, targeting treatment plans, and accelerating the development of new pharmaceuticals. • Can we bank on methodologies we don’t understand? • Does reinforced learning make us prisoners of the past? • Do we understand that the inherent logic of these platforms can be gamed? In Sum “Artificial Intelligence alone poorly illuminates the future of health analytics.”
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  Boer, R. (2014), Foucault’s Care …, Deep Learning in Healthcare Evans, R.S. (2016), Electronic Health Records: Then, Now, and in the Future Goldhill, D. (2018), Why are we living longer than ever?. Kontzer, T., (2016), Deep Learning Drops Error Rate for Breast Cancer Diagnoses by 85% Lee, J. et al. (2018), Holistic Quantified Self Framework for Augmented Human Martin, (2017), Types of Intelligence and How to Find The One You Are Best In McCaffrey, T. and Spector, L. (2012), Behind every innovative solution lies an obscure feature Mittelstadt, B. and Fioridi, L, (2016), The Ethics of Big Data, Current and foreseeable Issues in the biomedical contexts Saposnik, G. et al. (2016), Cognitive biases associated with medical decisions: a systematic review Sennaar, K., (2019), How America’s Top 4 Insurance Companies are Using Machine Learning Bibliograhy Further Reading