Support for the keynote "Data, Ethics and Health Care,”, Keynote, Creating Value in Health Care through Innovation Management, May 16,2019, Deusto, San Sebastien

1. Data, Ethics
and
Health Care
May 16th 2019

2. How will the
evolution of 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

3. Health analytics harnesses data
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

4. AI
The Road to Artificial 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?

5. Being Human
What does it 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

6. 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

7. • AI will move 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?

8. Implicit Bias
• Algorithms learn 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

9. • AI doesn’t fuel 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

10. • Value in improving 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.”

11. 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