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Maria Giovanna Trovato
Business Lead for Health and Life Sciences at the Institute for Experiential AI, Northeastern University
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Today’s
Welcome &
Agenda
About Us
ADVANCING AI THROUGH APPLIED WORK
We believe the biggest breakthroughs in the art and science of AI have come—and will come—through applied projects with real-world data that generate tangible results.
WHAT IS EXPERIENTIAL AI?
The Institute for Experiential AI at Northeastern University researches and develops human-centric AI solutions that leverage machine technology to extend human intelligence.
Successful AI is not ”set it and forget it.” To create real impact, AI must prioritize the human experience and utilize a human in the loop to consistently evaluate and improve its outcomes.
POSITIONED FOR OUR PARTNERS’ SUCCESS
The Institute for Experiential AI exists at
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the
AI has incredible potential to transform human health.
Sensing technology will be critical to reaching that potential.
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‘Sensing’ includes technologies like wearables, sleep tracking mats, and motion sensors.
Sensing technologies present incredible potential. And significant risk.
Responsible AI is crucial.
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Gene Tunik Director for AI + Health, Institute for Experiential AI Associate Dean of Research and Innovation at Northeastern University
Wearable Technology
Wearable technology, also known as "wearables," is a category of electronic devices that can be worn as accessories, embedded in clothing, or implanted in the user's body. These devices enable direct interaction between sensors and the end user. They are powered by microprocessors and equipped with the capability to send and receive data via the Internet.
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Role of AI in Developing Sensing Devices?
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Current Projects
Revolutionizing patient care, diagnostics, and research
Remote Sensing for Health and Healthcare
Wearables and IoT
AI in Diagnostic Medicine
Drug Discovery and Development
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Ethics Lead, Institute for Experiential AI Research Associate Professor in Philosophy
Co-Chair, AI Ethics Advisory Board
Director of Research, Institute for Experiential AI
Co-Chair, AI Ethics Advisory Board
Cansu Canca, Ph.D.
Ricardo Baeza-Yates, Ph.D.
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Our RAI services are based on the Puzzle-Solving in Ethics (PiE) Model , developed by the AI Ethics Lab.
The Global Nexus
The PiE Model lays out four core components of AI ethics implementation: Roadmap, Strategy, Analysis, and Training.
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Wearables, Sensors & the Role of AI in Healthcare Applications
Gene Tunik Director for AI + Health, Institute for Experiential AI Associate Dean of Research and Innovation at Northeastern University
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AI + Health
Accelerating Health and Healthcare Innovation with Human-Centric AI
Global AI + Health practice that spans our 14 campuses spread across 3 countries and 2 continents.
Wearable Sensors Market Size
| https://www.grandviewresearch.com/
Sensors Market Size
Wearable
15 Smart Material s AI-Assisted Data Processing
Sensors Early Detection Accurate Diagnosis
Wearable
Today’s Healthcare Model
Clinical Decision Support System (CDSS)
The Challenge:
• Sparse data collection and in the confines of medical visits
The Solution:
• Continuous monitoring offers real-world data about individuals
• Explainable AI models needed to process and interpret large and complex data
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| https://oliverottitsch.com/ Clinician 17 Patient Clinician Triangle of Trust AI
Non-Explainable vs. Explainable AI
Today: Non-Explainable AI
Training
Training Data
Model Training
Fractured Hand (accuracy=.89)
Model Output
• Why does it think there’s a fracture?
• Am I missing something?
• Can I correct the error in the AI algorithm?
The Future: Explainable AI
Fractured Hand
• Fx with displacement
• Ligament disruption
• Mechanism of injury
• Other symptoms
Training Data Explainable Model Model Explanation
● I understand why the AI prediction
● Decision was based on known input data
● I’d like to tweak the model based on learned features
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Performance vs. Explainability Model
| Goa l XGB Rulebased models Linear model s Decisi on tree Statistic al Models Graphica l models Ensembl es Deep Learning Models Random forest Markov Bayesian CRF / HBN MLN/SLR ADG SVM GAN CNN RNN
Explainability M o d e l P e r f o r m a n c e Modified from Yang et al., 2021 Hig h Low High 19
Model
Re-Imagining the Clinical Care Model
• Note generation from clinicianpatient interactions
• Real-time visualization of salient data
• Visualization of Real World Evidence / Data
• Ecologically-based data labeling
• Smart sampling
• Data compression
• Auto alert to patient / clinician of adverse events
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AI / ML
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Sensor Technology and AI Research at Northeastern University
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Case Study
Raimond Winslow Director of Life Sciences and Medical Research
at the Roux Institute. Professor, Northeastern University College of Engineering
The
HEART Project: Healthcare Enabled
by AI in Real-Time22
Sensing and AI Ethics
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Data sPO2 ECG/HR BP RR Temp ICP EEG Lab Tests sPO2 ECG/HR BP RR Movements sPO2 ECG/HR BP RR Movements Temp Weight Images Edema 23
Acute Care Home/Life Care Continuum of Care
The HEART Project: Healthcare Enabled by AI in Real-Time
Data (about patients) Models (of patients) Recommendations (for patients)
Real-Time Predictive Analytics
In the Cardio-Thoracic ICU (Kramer/ Sawyer/ Winslow)
Monitor Waveforms
Vital Signs
MMC Epic
Advanced prediction of negative outcomes
Feed-back to caregivers
RealTime Comput
Platform
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se
t
Data Warehou
HIPAA Complian
App s
e
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From Features to Risk Models to Predictions
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What We Can Learn From This
Septic Shock/sepsis
(Median EWT 8 hours)
Liu, R., Greenstein, J. L., Fackler, J. C., Bembea, M.M., Sarma, S. V., Winslow, R. L. (2020). Spectral Clustering of Risk Score Trajectories
Stratifies Sepsis Patients by Clinical Outcome and Interventions
Received. eLife, 2020;9:e58142 DOI: 10.7554/eLife.58142
Pediatric Multiple Organ Dysfunction Syndrome
(Median EWT 37 hours)
PPV 97%
Time Relative to Threshold Crossing (hours)
SN Bose, JL Greenstein, JC Fackler, SV Sarma, RL Winslow, MM
Bembea (2021). Early prediction of multiple organ dysfunction in the pediatric care unit. Frontiers Pediatr. doi: 10.3389/fped.2021.711104
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The Promise of Wearables for AI + Health: Using Theory and Evidence to Guide
Responsible Design
Karen Quigley Professor of Psychology at Northeastern University Core Faculty, Institute for Experiential AI
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The Promise of Wearables for AI + Health: Using Theory and Evidence to Guide Responsible Design
Karen S. Quigley. Ph.D.§ & Lisa Feldman Barrett, Ph.D. §
† *
Northeastern University §
Massachusetts General Hospital †
Harvard Medical School *
INTERDISCIPLINARY AFFECTIVE SCIENCE LABORATORY
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The Promise of Wearables for AI + Health is optimized when:
• Data is high-quality, reliable, and multimodal
• Predictive models at the person level, not the group level
• Data is accessible and comprehensible to the user
• Data is secure and protects user privacy
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The Promise of Wearables for AI + Health is optimized when:
• Data is high-quality, reliable, and multimodal
• Predictive models at the person level, not the group level
• Data is accessible and comprehensible to the user
• Data is secure and protects user privacy
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High-quality, reliable
Biologically-Triggered Experience Sampling
• Ambulatory physiological monitoring (8 hours/day for 14 days)
• Physiologically-triggered sampling moments (6-8/day)
N = 52 (56% female; M = 22.48 years, SD = 4.38 years)
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& multimodal
Biologically-Triggered Experience Sampling Excited A lot High-quality, reliable & multimodal 33
High-quality, Reliable & Multimodal
Clusters of physiological features = patterns of change
44 patterns were observed in at least 2 subjects (66%)
1/3 of patterns were unique
2 0 1 5 1 0 5 N u m b e r o f P a r t i c i p a n t s 1 0 5 1 0 1 5 2 0 2 5 3 0 3 5 4 0 4 4 Hoemann et al. (2020)
PATTERNS
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High-quality, Reliable & Multimodal
Number of physiological patterns per participant
N u m b e r o f P a r t i c i p a n t s 1 2 3 4 5 6 7 2 0 8 9 1 0 4 6 8 1 0 1 2 1 4 1 6 Hoemann et al. (2020) PATTERNS
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High-quality, Reliable & Multimodal
Many-to-Many Mapping
1 2 3 4 5 6 7 8 9 1 0 Hoemann et al. (2020)
PATTERNS PATTERNS
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Guided by Theory
Unsolved Problem: Lack of Correspondence
Subjective report
Physiological measurement
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The Promise of Wearables for AI + Health is optimized when:
• Data is high-quality, reliable, and multimodal
• Predictive models at the person level, not the group level
• Data is accessible and comprehensible to the user
• Data is secure and protects user privacy
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User Accessible & Comprehensible
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User Accessible & Comprehensible
“Gave myself that piece of mind knowing that I don’t have sleep apnea. So, it’s nothing that I should have a concern about being, the numbers being so low. That I’m not even close to like maybe being dangerous. It’s like no, you’re really healthy.”
“It was reassuring that, though I perceive my sleep as being abnormally short, the scientific data says I’m sleeping longer than I think.”
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Secure User Data for Privacy
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The Promise of Wearables for AI + Health is optimized when:
• Data is high-quality, reliable, and multimodal
• Measures and Algorithms/Models guided by Theory
• Data is accessible and comprehensible to the user
• Data is secure and protects user privacy
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Cansu Canca
Ethics Lead at the Institute for Experiential
AI Research Associate Professor in Philosophy
Northeastern University
AI Ethics in Sensing and Wearables: Critical Considerations
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Clinician & Researcher
What is the right technology ?
What is the right thing to do?
Hospital & Healthcare system
What is the right health policy?
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Patient
AI
Clinician & Researcher
AI
What is the right technology ?
What is the right thing to do?
Hospital & Healthcare system
What is the right health policy?
Medical ethics
Public health ethics
Biomedical ethics
Research ethics
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Patient
Clinician & Researcher
What is the right technology and the right use of it?
Hospital & Healthcare system
Medical ethics
Public health ethics
Biomedical ethics
Research ethics
AI / tech ethics
What is the right thing to do?
What is the right health policy?
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Patient
AI
Responsible AI
Content / product: Developing & designing ethical technologies (AI for Good & Good
AI)
Process: Ensuring ethical processes for tech development
Implementation: Using and implementing tech ethically
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Responsible AI
Research — Development — Design — Deployment — Update — Monitoring + Procurement (full innovation cycle)
data algorithm tool/UI
practice (HCI)
Content / product: Developing & designing ethical technologies (AI for Good & Good
AI)
Process: Ensuring ethical processes for tech development
Implementation: Using and implementing tech ethically
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● data ● user ● user’s environment
● others in relation transparency
● what ● how ● when ● choice? equal outcome
● fairness
● equity accuracy
● define harm
● impact
agency
● HCI
● collaborative decisionmaking
● risk allocation well-being
● impact of information
● individual (user & others)
● societal
● environmental contestability
● expert input
● user input
| 50 privacy
● data ● user ● user’s environment
● others in relation transparency
● what
● how ● when ● choice? equal outcome
● fairness
● equity accuracy
● define harm
● impact
agency
● HCI
● collaborative decisionmaking
● risk allocation
well-being
● impact of information
● individual (user & others)
● societal
● environmental contestability
● expert input
● user input
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agency
● data ● user ● user’s environment
● others in relation transparency
● what ● how ● when ● choice?
● fairness
● equity accuracy
● define harm
● impact
● HCI
● collaborative decisionmaking
● risk allocation well-being
● impact of information
● individual (user & others)
● societal
● environmental contestability
● expert input
● user input
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equal outcome
| 53 The Box by Version 1: prototype https://aiethicslab.com/the-box/
| 54 The Box by Version 1: prototype https://aiethicslab.com/the-box/
https://aiethicslab.com/big-picture/
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Dynamic of AI Principles by
Toolbox:
AI HSR: Guidance
The Belmont Report: respect for persons, beneficence, justice
informed consent
● online setting
● secondary use of data
● third party models
● withdraw from research
risk & benefit assessment
● minimal risk
● black box
● dual use
● externalities
private information
● re-identification
● group privacy
● data ownership
discrimination / bias
● subject selection
● unrepresentative data
● data with social bias
● proxies
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AI HSR: Guidance
The Belmont Report: respect for persons, beneficence, justice
informed consent
● online setting
● secondary use of data
● third party models
● withdraw from research
risk & benefit assessment
● minimal risk
● black box
● dual use
● externalities
private information
● re-identification
● group privacy
● data ownership
discrimination / bias
● subject selection
● unrepresentative data
● data with social bias
● proxies
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● Canca & Eto, “AI and Human Subjects
Protection”, CITI Training webinar
● Canca, with Eto & Leong, “AI and Ethics in Human Subjects Research”, CITI Training module
● Eto, with Canca & Leong, “Regulatory
Approaches to AI in Human Subjects
Research”, CITI Training module
● Canca, “Operationalizing AI Principles”, ACM
Communications
● AI Ethics Lab, Dynamics of AI Principles
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RESPONSIBLE AI
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RESPONSIBLE AI
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Conclusion
How can we Achieve the Highest Standard of Care?
This slide is an editable slide with all your needs.
Optimize clinical workflow
Enhance accuracy
This slide is an editable slide with all your needs.
Human Centric Approac h
Reduce administrative burden
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A New Healthcare Model
• Innovation
• Research
• Collaboration
• Ethics
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How can we Achieve the Highest Standard of Care?
Sensor technology developers
Human Centric Approach
This slide is an editable slide with all your needs.
• Innovation
• Research
• Collaboration
• Ethics
AI algorithm designers
Healthcare Professionals
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Real solutions to Real Problems
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“If you can dream it, we can help you build it!”
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Coming Soon
Questions from today’s talk?
Contact Maria Giovanna at m.trovato@northeastern. edu
Or scan the QR code
Thank You!
