Framing Trust in Medical AI: Seminar EurAI ACAIJose M. Juarez
In this tutorial we will introduce the medical Artificial Intelligence field and how to handle the major concerns of doctors to adopt AI-based technologies in daily clinical practice.
The development of trustworthy AI system is multidisciplinary requiring ethical, legal, and technological measures.
This tutorial was first given in the 19th Advanced Course on AI (ACAI) is a specialized course in Artificial Intelligence sponsored by EurAI. The theme of the 2022 ACAI School is Explainable AI.
DESCRIPTION:
During this tutorial, the students will have a general vision of existing initiatives made by the European Union on the ethical and legal framework related to AI in healthcare (ethics guidelines, GDPR, AI , medical devices). The tutorial will overview most popular explainable methods on AI (e.g. LIME, SHAP, saliency maps) highlighting their advantages and drawbacks from the clinician’s perspective. To illustrate the different challenges of medical AI, this tutorial is driven by several examples obtained from the recent literature on the AI and medical fields.
WEBSITE OF THE SEMINAR
https://webs.um.es/jmjuarez/trustAImedicine/
Objectives
*To gain a better understanding of applying AI in healthcare settings.
*To identify principal interdisciplinary factors for a trustworthy AI project according to EU guidelines.
*To be aware of advantages and limitations of current eXplainable AI.
AI in Healthcare | Future of Smart Hospitals Renee Yao
In this talk, I specifically talk about how NVIDIA healthcare AI software and hardware were used to support healthcare AI startups' innovation. Three startups featured: Caption Health, Artisight, and Hyperfine. Audience: healthcare systems CXOs.
Framing Trust in Medical AI: Seminar EurAI ACAIJose M. Juarez
In this tutorial we will introduce the medical Artificial Intelligence field and how to handle the major concerns of doctors to adopt AI-based technologies in daily clinical practice.
The development of trustworthy AI system is multidisciplinary requiring ethical, legal, and technological measures.
This tutorial was first given in the 19th Advanced Course on AI (ACAI) is a specialized course in Artificial Intelligence sponsored by EurAI. The theme of the 2022 ACAI School is Explainable AI.
DESCRIPTION:
During this tutorial, the students will have a general vision of existing initiatives made by the European Union on the ethical and legal framework related to AI in healthcare (ethics guidelines, GDPR, AI , medical devices). The tutorial will overview most popular explainable methods on AI (e.g. LIME, SHAP, saliency maps) highlighting their advantages and drawbacks from the clinician’s perspective. To illustrate the different challenges of medical AI, this tutorial is driven by several examples obtained from the recent literature on the AI and medical fields.
WEBSITE OF THE SEMINAR
https://webs.um.es/jmjuarez/trustAImedicine/
Objectives
*To gain a better understanding of applying AI in healthcare settings.
*To identify principal interdisciplinary factors for a trustworthy AI project according to EU guidelines.
*To be aware of advantages and limitations of current eXplainable AI.
AI in Healthcare | Future of Smart Hospitals Renee Yao
In this talk, I specifically talk about how NVIDIA healthcare AI software and hardware were used to support healthcare AI startups' innovation. Three startups featured: Caption Health, Artisight, and Hyperfine. Audience: healthcare systems CXOs.
This tutorial extensively covers the definitions, nuances, challenges, and requirements for the design of interpretable and explainable machine learning models and systems in healthcare. We discuss many uses in which interpretable machine learning models are needed in healthcare and how they should be deployed. Additionally, we explore the landscape of recent advances to address the challenges model interpretability in healthcare and also describe how one would go about choosing the right interpretable machine learnig algorithm for a given problem in healthcare.
[Video recording available at https://www.youtube.com/playlist?list=PLewjn-vrZ7d3x0M4Uu_57oaJPRXkiS221]
Artificial Intelligence is increasingly playing an integral role in determining our day-to-day experiences. Moreover, with proliferation of AI based solutions in areas such as hiring, lending, criminal justice, healthcare, and education, the resulting personal and professional implications of AI are far-reaching. The dominant role played by AI models in these domains has led to a growing concern regarding potential bias in these models, and a demand for model transparency and interpretability. In addition, model explainability is a prerequisite for building trust and adoption of AI systems in high stakes domains requiring reliability and safety such as healthcare and automated transportation, and critical industrial applications with significant economic implications such as predictive maintenance, exploration of natural resources, and climate change modeling.
As a consequence, AI researchers and practitioners have focused their attention on explainable AI to help them better trust and understand models at scale. The challenges for the research community include (i) defining model explainability, (ii) formulating explainability tasks for understanding model behavior and developing solutions for these tasks, and finally (iii) designing measures for evaluating the performance of models in explainability tasks.
In this tutorial, we present an overview of model interpretability and explainability in AI, key regulations / laws, and techniques / tools for providing explainability as part of AI/ML systems. Then, we focus on the application of explainability techniques in industry, wherein we present practical challenges / guidelines for effectively using explainability techniques and lessons learned from deploying explainable models for several web-scale machine learning and data mining applications. We present case studies across different companies, spanning application domains such as search & recommendation systems, hiring, sales, and lending. Finally, based on our experiences in industry, we identify open problems and research directions for the data mining / machine learning community.
This talk gives an introduction about Healthcare Use cases - The AI ladder and Lifestyle AI at Scale Themes The iterative nature of the workflow and some of the important components to be aware in developing AI health care solutions were being discussed. The different types of algorithms and when machine learning might be more appropriate in deep learning or the other way will also be discussed. Use cases in terms of examples are also shared as part of this presentation .
This webinar will focus on the technical and practical aspects of creating and deploying predictive analytics. We have seen an emerging need for predictive analytics across clinical, operational, and financial domains. One pitfall we’ve seen with predictive analytics is that while many people with access to free tools can develop predictive models, many organizations fail to provide a sufficient infrastructure in which the models are deployed in a consistent, reliable way and truly embedded into the analytics environment. We will survey techniques that are used to get better predictions at scale. This webinar won’t be an intense mathematical treatment of the latest predictive algorithms, but will rather be a guide for organizations that want to embed predictive analytics into their technical and operational workflows.
Topics will include:
Reducing the time it takes to develop a model
Automating model training and retraining
Feature engineering
Deploying the model in the analytics environment
Deploying the model in the clinical environment
Spark 2019: Equifax's SVP Data & Analytics, Peter Maynard, discusses the notion (and importance) of explainable AI in the financial services sector. He looks at the work Equifax have done to crack open the black box by creating patented AI technology that helps companies make smarter, explainable decisions using AI.
Responsible AI & Cybersecurity: A tale of two technology risksLiming Zhu
With the broader adoption of digital technologies and AI, organisations face the emerging risks of AI, the unfamiliar, and the intensified risk of cybersecurity, the familiar. AI and cybersecurity are intertwined, but risk silos are often created when they are dealt with at the technology and governance levels. This talk will explore the interactions between responsible AI and cybersecurity risks via industry case studies. It will show how we can break down the risk silos and use emerging trust-enhancing technologies, architecture and end-to-end software engineering/DevOps practices to connect the two worlds and uplift the risk management posture for both.
Training #ArtificialIntelligence is an energy-intensive process. Some estimate that creating an #AI can be five times worse for the planet than a car. Learn more about #redAI versus #greenAI and latest AI use-cases to benefit the environment and what can we do to use tech and AI in a more sustainable way.
Session presented in the "AI-powered innovation in the digital era" session of the #ClujInnovationDays 2021.
Faster and Cheaper Clinical Trials: The Benefit of Synthetic Dataaccenture
Take the innovation leap: Four things pharma companies can do now for a synthetic data-driven approach to clinical trial design. https://accntu.re/3vjVjVs
Contemporary AI engenders hopes and fears – hopes of harnessing AI for productivity growth and innovation – fears of mass unemployment and conflict between humankind and an artificial super-intelligence. Before we let AI drive our hopes and fears, we need to understand what it is and what it is not. Then we need to understand how to implement AI in an ethical and responsible manner. Only then can we harness the power of AI to our benefit.
Internet of Things (IoT) and Artificial Intelligence (AI) role in Medical and...Hamidreza Bolhasani
Internet of Things (IoT) and Artificial Intelligence (AI) role in Medical and Healthcare Systems
+ History of IoT
+ Internet of Nano Things (IoNT)
+ IoT and IoNT for Medical and Healthcare Systems
+ IoT and Artificial Intelligence (AI)
+ IoT and AI for Health
+ Deep Learning Accelerator
This presentation discuss major applications of AI in Healthcare including medical diagnostics, personalized treatments and optimizing US healthcare system. This presentation also discuss some of the challenges of implementing AI in healthcare.
Presentation of Nozha Boujemaa (Dr Inria) on Trusworthy Artificial Intelligence including Responsible and Robust Artificial Intelligence - MIT Tech Review Innovation Leaders Summit "Breakthrough to Impact", Paris November 30th 2018
Give a background of Data Science and Artificial Intelligence, to better understand the current state of the art (SOTA) for Large Language Models (LLMs) and Generative AI. Then start a discussion on the direction things are going in the future.
This tutorial extensively covers the definitions, nuances, challenges, and requirements for the design of interpretable and explainable machine learning models and systems in healthcare. We discuss many uses in which interpretable machine learning models are needed in healthcare and how they should be deployed. Additionally, we explore the landscape of recent advances to address the challenges model interpretability in healthcare and also describe how one would go about choosing the right interpretable machine learnig algorithm for a given problem in healthcare.
[Video recording available at https://www.youtube.com/playlist?list=PLewjn-vrZ7d3x0M4Uu_57oaJPRXkiS221]
Artificial Intelligence is increasingly playing an integral role in determining our day-to-day experiences. Moreover, with proliferation of AI based solutions in areas such as hiring, lending, criminal justice, healthcare, and education, the resulting personal and professional implications of AI are far-reaching. The dominant role played by AI models in these domains has led to a growing concern regarding potential bias in these models, and a demand for model transparency and interpretability. In addition, model explainability is a prerequisite for building trust and adoption of AI systems in high stakes domains requiring reliability and safety such as healthcare and automated transportation, and critical industrial applications with significant economic implications such as predictive maintenance, exploration of natural resources, and climate change modeling.
As a consequence, AI researchers and practitioners have focused their attention on explainable AI to help them better trust and understand models at scale. The challenges for the research community include (i) defining model explainability, (ii) formulating explainability tasks for understanding model behavior and developing solutions for these tasks, and finally (iii) designing measures for evaluating the performance of models in explainability tasks.
In this tutorial, we present an overview of model interpretability and explainability in AI, key regulations / laws, and techniques / tools for providing explainability as part of AI/ML systems. Then, we focus on the application of explainability techniques in industry, wherein we present practical challenges / guidelines for effectively using explainability techniques and lessons learned from deploying explainable models for several web-scale machine learning and data mining applications. We present case studies across different companies, spanning application domains such as search & recommendation systems, hiring, sales, and lending. Finally, based on our experiences in industry, we identify open problems and research directions for the data mining / machine learning community.
This talk gives an introduction about Healthcare Use cases - The AI ladder and Lifestyle AI at Scale Themes The iterative nature of the workflow and some of the important components to be aware in developing AI health care solutions were being discussed. The different types of algorithms and when machine learning might be more appropriate in deep learning or the other way will also be discussed. Use cases in terms of examples are also shared as part of this presentation .
This webinar will focus on the technical and practical aspects of creating and deploying predictive analytics. We have seen an emerging need for predictive analytics across clinical, operational, and financial domains. One pitfall we’ve seen with predictive analytics is that while many people with access to free tools can develop predictive models, many organizations fail to provide a sufficient infrastructure in which the models are deployed in a consistent, reliable way and truly embedded into the analytics environment. We will survey techniques that are used to get better predictions at scale. This webinar won’t be an intense mathematical treatment of the latest predictive algorithms, but will rather be a guide for organizations that want to embed predictive analytics into their technical and operational workflows.
Topics will include:
Reducing the time it takes to develop a model
Automating model training and retraining
Feature engineering
Deploying the model in the analytics environment
Deploying the model in the clinical environment
Spark 2019: Equifax's SVP Data & Analytics, Peter Maynard, discusses the notion (and importance) of explainable AI in the financial services sector. He looks at the work Equifax have done to crack open the black box by creating patented AI technology that helps companies make smarter, explainable decisions using AI.
Responsible AI & Cybersecurity: A tale of two technology risksLiming Zhu
With the broader adoption of digital technologies and AI, organisations face the emerging risks of AI, the unfamiliar, and the intensified risk of cybersecurity, the familiar. AI and cybersecurity are intertwined, but risk silos are often created when they are dealt with at the technology and governance levels. This talk will explore the interactions between responsible AI and cybersecurity risks via industry case studies. It will show how we can break down the risk silos and use emerging trust-enhancing technologies, architecture and end-to-end software engineering/DevOps practices to connect the two worlds and uplift the risk management posture for both.
Training #ArtificialIntelligence is an energy-intensive process. Some estimate that creating an #AI can be five times worse for the planet than a car. Learn more about #redAI versus #greenAI and latest AI use-cases to benefit the environment and what can we do to use tech and AI in a more sustainable way.
Session presented in the "AI-powered innovation in the digital era" session of the #ClujInnovationDays 2021.
Faster and Cheaper Clinical Trials: The Benefit of Synthetic Dataaccenture
Take the innovation leap: Four things pharma companies can do now for a synthetic data-driven approach to clinical trial design. https://accntu.re/3vjVjVs
Contemporary AI engenders hopes and fears – hopes of harnessing AI for productivity growth and innovation – fears of mass unemployment and conflict between humankind and an artificial super-intelligence. Before we let AI drive our hopes and fears, we need to understand what it is and what it is not. Then we need to understand how to implement AI in an ethical and responsible manner. Only then can we harness the power of AI to our benefit.
Internet of Things (IoT) and Artificial Intelligence (AI) role in Medical and...Hamidreza Bolhasani
Internet of Things (IoT) and Artificial Intelligence (AI) role in Medical and Healthcare Systems
+ History of IoT
+ Internet of Nano Things (IoNT)
+ IoT and IoNT for Medical and Healthcare Systems
+ IoT and Artificial Intelligence (AI)
+ IoT and AI for Health
+ Deep Learning Accelerator
This presentation discuss major applications of AI in Healthcare including medical diagnostics, personalized treatments and optimizing US healthcare system. This presentation also discuss some of the challenges of implementing AI in healthcare.
Presentation of Nozha Boujemaa (Dr Inria) on Trusworthy Artificial Intelligence including Responsible and Robust Artificial Intelligence - MIT Tech Review Innovation Leaders Summit "Breakthrough to Impact", Paris November 30th 2018
Give a background of Data Science and Artificial Intelligence, to better understand the current state of the art (SOTA) for Large Language Models (LLMs) and Generative AI. Then start a discussion on the direction things are going in the future.
Frankie Rybicki slide set for Deep Learning in Radiology / MedicineFrank Rybicki
These are my #AI slides for medical deep learning using #radiology and medical imaging examples. Please use them & modify to teach your own group about medical AI.
Presented at the Healthcare CEO50 Certificate Program, School of Hospital Management, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand on October 4, 2021
AI in Healthcare: From Hype to Impact (updated)Mei Chen, PhD
The primary goal of this workshop is to help health professionals gain a critical understanding of the various types of AI technologies available so they can make wise decisions and invest AI for healthcare improvement.
"Challenges for AI in Healthcare" - Peter Graven Ph.DGrid Dynamics
Dynamic Talks Portland: The use of AI in many industries has revolutionized operations and efficiency. In healthcare, the progress is just beginning. Despite the promise of AI, why has the development lagged other industries? What issues are unique to healthcare that create challenges for common approaches? How can data scientists overcome these challenges and deliver on the promise of using data to reach multiple goals of improved quality, decreased cost, and greater patient satisfaction?
Cemal H. Guvercin MedicReS 5th World Congress MedicReS
Ethical Issues in Artifical Intelligence Applied to Medicine Presentation to MedicReS 5th World Congress on October 19,25,2015 in New York by Cemal H. Guvercin, MD, PhD
How AstraZeneca is Applying AI, Imaging & Data Analytics (AI-Driven Drug Deve...Nick Brown
Keynote AI Presentation given at AI-Driven Drug Development Summit Europe on 26th April 2023 in London. Overview around how AstraZeneca has been developing AI in the past 5+ years. Predominantly focused on R&D and how we are developing digital solutions & AI for right safety and right dose. AI examples include machine learning for safety assessment, augmenting digital pathology for image quantification & segmentation, understanding more about our drugs through advanced imaging modalities and first steps in applying AI for right dose - immunogenicity, adverse events and tolerability.
Validation of Clinical Artificial Intelligence: Where We Are and Where We Are...Sean Manion PhD
This is the deck from a presentation I gave to the Pittsburgh Industrial Statisticians Association (PISA) for their PISA23 event in a session on Artificial Intelligence and Machine Learning.
The deck itself is not intended to be stand alone without the accompanying verbal presentation, however many of the slides contain key elements with references, and my contact information is available at the end if anyone has questions.
Artificial intelligence, such as neural networks, deep learning and predictive analytics, has the potential to transform radiology, by enhancing the productivity of radiologists and helping them to make better diagnoses. This short report from Signify Research presents 5 reasons why artificial intelligence will increasingly be used in radiology in the coming years and concludes with a list of the barriers that will first need to be overcome before mainstream adoption will occur.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
1. Machine learning in medicine:
calm down
Ben Van Calster
Department of Development and Regeneration, KU Leuven (B)
Department of Biomedical Data Sciences, LUMC (NL)
Research Ethics Committee, University Hospitals Leuven (B)
Epi-Centre, KU Leuven (B)
Innsbruck, November 8th, 2019
4. Contents
• What are we talking about? AI, ML, and its applications
• ML instead of traditional statistics: success guaranteed?
• Typical applications of ML in medicine
• Ten concerns for model development, validation and implementation
• Beyond the hype
4
6. Artificial intelligence (AI)
6
Attributed to John McCarthy (Stanford) in 1955:
“Every aspect of learning or intelligence can in principle
be so precisely described that a machine can be made
to simulate it.”
Russell and Norvig (2010):
- Think like a human
- Act like a human
- Think rationally
- Act rationally
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
McCarthy et al. A proposal for the Dartmouth summer research project on artificial intelligence, 1955. URL http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html.
https://www.independent.co.uk/news/obituaries/john-mccarthy-computer-scientist-known-as-the-father-of-ai-6255307.html
Russell & Norvig. Artificial Intelligence: A Modern Approach (3rd ed). New Jersey: Prentice Hall, 2010.
7. Machine learning
7
Branch of AI, term attributed to Arthur Lee Samuel (IBM) in 1959.
Wikipedia (sorry):
Algorithms are developed to perform a specific task without using explicit instructions, relying
on patterns and inference instead.
Shillan et al (2019): form of AI in which a model learns from examples rather then pre-
programmed rules.
History-computer.com
Shillan et al. Crit Care 2019;23:284.
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
10. Speech recognition (e.g. Siri, Bixby)
10https://focalcode.com/blog/10-real-world-examples-of-machine-learning-and-ai-2018-2/
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
13. Non-exhaustive list
13
Gaming
Natural Language Processing (Siri etc)
Fraud detection
Shoplifting
Object recognition (e.g. for driverless cars)
Facial recognition
Traffic analysis and predictions (e.g. Waze app)
Electrical load forecasting
(Social) media and advertising (people you may know, movie suggestions, …)
Spam filtering
Search engines (e.g. Google PageRank)
Handwriting recognition
Also less positive applications, such as ‘deepfake’ adult movies
And… medicine and healthcare
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
14. Machine learning in medicine: overview
14Intellspot.com
Input and
output data
Input
data
PREDICTION PATTERNS
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
15. Popularity skyrocketing
15Search on https://www.ncbi.nlm.nih.gov/pubmed/ on (performed Oct 18, 2019)
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
16. Funding
16
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
18. Reason for popularity
18
“Typical machine learning algorithms are highly flexible
So will uncover associations we could not find before
Hence better predictions and management decisions”
→ One of the master keys, with guaranteed success!
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
20. AI in medicine vs elsewhere
• AI is everywhere
o You get ads for products you’ve just been searching (e.g. a cruise)
o You get recommendations for movies based on movies that you watched
• If 1/3 of the ads/movies are of interest to me, performance is poor but it ‘worked’
• Consequences in medicine are different: 1/3 decent predictions is unacceptable
20
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
21. Traditional Statistics vs Machine Learning
21Breiman. Stat Sci 2001;16:199-231.
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
22. A wild oversimplification
22Used visualizations from: Topol, Nat Med 2019;25:44-56; Van Calster et al, Ultrasound Obstet Gynecol 2007;29:496-504; kdnuggets.com.
X1
X2
X3
X4
X5
X6
X7
X8
Y
Classical statistical modeling
(regression)
Flexible algorithms
(machine learning)
Artificial neural networks (incl deep learning),
Support vector machines, Decision trees,
Random forests, boosted tree models, …
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
23. Traditional Statistics vs Machine Learning
23
Shmueli. Keynote talk at 2019 ISBIS conference, Kuala Lumpur; taken from slideshare.net
Bzdok. Nature Methods 2018;15:233-4.
??
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
24. Traditional Statistics vs Machine Learning
24
Traditional regression modeling can be used for prediction, is used for
prediction, and is able to do prediction very well!
Steyerberg. Clinical prediction models (2nd ed). New York: Springer, 2019.
Riley et al. Prognosis Research in healthcare. Oxford: OUP, 2019.
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
25. Machine Learning: success guaranteed?
25Christodoulou et al. J Clin Epidemiol 2019;110:12-22.
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
26. Traditional Statistics vs Machine Learning
26Christodoulou et al. J Clin Epidemiol 2019;110:12-22.
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
28. Deep learning for medical images
28Topol. Nat Med 2019;25:44-56.
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
29. Deep learning for medical images
29
Topol. Nat Med 2019;25:44-56.
Titano et al. Nat Med 2018;24:1337-41; Nam et al. Radiology 2019;290:218-28; Ehteshami Bejnordi et al. JAMA 2017;318:2199-210;
Esteva et al. Nature 2017;542:115-8; De Fauw et al. Nat Med 2018;24:1342-50; Raman et al. Eye 2019;33:97-109.
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
30. Deep learning for medical images
30Liu et al. Lancet Dig Health 2019;1:e271-97.
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
31. Machine Learning for EHR data
31
Rajkomar et al. Npj Digit Med 2018;1:18.
Rose. JAMA Netw Open 2018;1:e181404.
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
32. Machine Learning for EHR data
32Rajkomar et al. Npj Digit Med 2018;1:18.
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
33. Machine Learning for EHR data
33Goldstein et al. J Am Med Inform Assoc 2017;24:198-208.
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
34. Clustering
34Ahlqvist et al. Lancet Diabetes Endocrinol 2018;6:361-9.
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
36. Ten concerns for predictive analytics
36
1. Poor study design makes choice of algorithm irrelevant
2. Flexible algorithms are data hungry, but rich datasets have poor quality
3. There is large heterogeneity between settings and studies
4. The modeling strategy and reporting is often problematic
5. The signal-to-noise ratio in medicine is often low
6. Using a model makes it invalid (a ‘prediction paradox’)
7. Flexible algorithms are hard to interpret
8. Algorithms should be available and free from conflict of interest
9. Regulatory approval does not guarantee that it works well
10. Algorithms can be unfair for subgroups
(we do not even touch upon actual clinical utility)
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
37. 1. Study design trumps algorithm choice
37
Do you have a clear research question?
Do you have data that help you answer the question?
What is the quality of the data?
Dilbert.com
Riley. Nature 2019;275:27-9.
One simple example: EHR data were not collected for research purposes
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
38. Example
38
Uses ANN, Random Forests, Naïve Bayes, and Logistic Regression.
(Logistic regression performed best, AUC 0.92)
Hernandez-Suarez et al. JACC Cardiovasc Interv 2019;12:1328-38.
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
39. Example (contd)
39
The model also uses postoperative information.
David J Cohen, MD: “The model can’t be run properly until you know about both the presence and the absence of
those complications, but you don’t know about the absence of a complication until the patient has left the hospital.”
https://www.tctmd.com/news/machine-learning-helps-predict-hospital-mortality-post-tavr-skepticism-abounds
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
40. Example 2
40
Predictions based on whether ruler was on image. This
implied that the clinician was concerned about the lesion.
Paper mentioned that people could do it themselves
using smartphone: that is completely different
Esteva et al. Nature 2017;542:115-8.
https://towardsdatascience.com/is-the-medias-reluctance-to-admit-ai-s-weaknesses-putting-us-at-risk-c355728e9028
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
41. Example 3
41Winkler et al. JAMA Dermatol 2019; in press.
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
42. Example 4
42Khazendar et al. Facts Views Vis ObGyn 2015;7:7-15.
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
43. 2. Flexible algorithms are data hungry
43http://www.portlandsports.com/hot-dog-eating-champ-kobayashi-hits-psu/
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
44. Flexible algorithms are data hungry
44Van der Ploeg et al. BMC Med Res Methodol 2014;14:137.
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
45. Flexible algorithms are data hungry
45
https://simplystatistics.org/2017/05/31/deeplearning-vs-leekasso/
Marcus. arXiv 2018; arXiv:1801.00631
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
46. 3. Large heterogeneity
46
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
47. Large heterogeneity in healthcare
47
Riley et al. BMJ 2016;353:i3140.
Davis et al. JAMIA 2017;24:1052-61.
Steyerberg et al. Stat Med 2019;38:4290-309.
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
48. Heterogeneity of logistics and processes
48Agniel. BMJ 2018;360:k1479.
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
49. Heterogeneity between studies
49
Badgeley et al. arXiv 2018; arXiv:1811.03695v1.
Finlayson et al. Science 2019;363:1287-9.
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
50. Many possible hidden variables
50Riley. Nature 2019;572:27-9.
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
51. Example
51
Validation in the same hospital Validation in another hospital
Norgeot et al. JAMA Netw Open 2019;2:e190606.
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
52. Reliable risk estimates: the Achilles heel?
52
Shah et al. JAMA 2018;320:27-8.
Van Calster et al. BMC Med, in press.
Rose. JAMA Netw Open 2018;1:e181404.
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
53. 4. Poor modeling and unclear reporting
53
Christodoulou et al. J Clin Epidemiol 2019;110:12-22.
Collins and Moons. Lancet 2019;393:1577-9.
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
54. Poor modeling and unclear reporting
54
What was done about missing data? 45% fully unclear, 100% poor or unclear
How were continuous predictors modeled? 20% unclear, 25% categorized
How were hyperparameters tuned? 66% unclear, 19% tuned with information
How was performance validated? 68% unclear or biased approach
Was calibration of risk estimates studied? 79% not at all, HL test common
Prognosis: time horizon often ignored completely
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
Christodoulou et al. J Clin Epidemiol 2019;110:12-22.
55. Example
55
“model based on all of the 473 available variables”
Alaa et al. PLoS One 2019;14:e0213653.
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
56. No thank you
56Hutson. Science 2019;365:416-7.
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
57. 5. Signal-to-noise ratio (SNR) is often low
57
High SNR: predictors-outcome relationship is clear and sharp, little affected by unwanted
noise
There is support that flexible algorithms work best with high SNR, not with low SNR.
How can methods that look everywhere be better when you do not know where to look or what you look for?
Hand. Stat Sci 2006;21:1-14.
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
58. Algorithm flexibility and SNR
58Makridakis et al. PLoS One 2018;13:e0194889.
Ennis et al. Stat Med 1998;17:2501-8.
Goldstein et al. Stat Med 2017;36:2750-63.
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
59. Yet…
59Rajkomar et al. NEJM 2019;380:1347-58.
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
60. 6. Using a model makes it invalid
60
Peek et al. https://www.bmj.com/content/357/bmj.j2099/rr-0
Challen et al. BMJ Qual Saf 2019;28:231-7.
Coston et al. arXiv 2019; arXiv:1909.00066.
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
Peek et al (2017): “predictions influencing behaviours that in turn invalidates predictions”
61. 7. Low interpretability
61
??
Deep learning image adapted from: Topol. Nat Med 2019;25:44-56.
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
62. 8. Model (un)availability and CoI
62
Panch et al. npj Digit Med 2019;2:77.
Van Calster et al. JAMIA 2019; in press.
Boulesteix et al. Biom J 2019;61:1314-28.
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
63. Proprietary algorithms and CoI
63
Van Calster et al. JAMA Intern Med 2019;179:731. Janssens. Genes 2019;10:448.
Van Calster et al. JAMIA 2019; in press.
https://www.theguardian.com/politics/2019/oct/11/dominic-cummings-accused-of-conflict-of-interest-over-nhs-fund?CMP=Share_iOSApp_Other.
“Ethically unacceptable to have a business model
that focuses on selling an algorithm”
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
64. 9. Regulatory approval of AI (FDA, CE,…)
64
® Evolving domain, trying to keep up with reality.
® Approval not easy from academia (non-profit): should be looked at.
® Approval does not mean that it works well or is safe. This is changing (e.g. FDA).
® What with continuously updated algorithms (across time and place)?
® Balance between transparency, commercial interests, IPR, and societal benefit.
Parikh et al. Science 2019;363:810-2.
Schulz et al. Clin Chem 2019;65:10.
https://www.theguardian.com/politics/2019/oct/11/dominic-cummings-accused-of-conflict-of-interest-over-nhs-fund?CMP=Share_iOSApp_Other.
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
65. Regulation of AI (FDA, CE, NMPA)
65https://www.fda.gov/media/122535/download
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
66. Regulation of AI (FDA, CE, NMPA)
66
Topol. Nat Med 2019;25:44-56.
https://amp.theguardian.com/society/2019/oct/15/councils-using-algorithms-make-welfare-decisions-benefits?__twitter_impression=true
https://www.digitalhealth.net/2019/10/healthcare-apps-safety-standards/
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
67. Example: Babylon’s “GP at hand” app
67
https://www.thetimes.co.uk/article/gp-at-hand-app-babylon-bleeds-cash-dgtnldmqk;
https://www.thetimes.co.uk/article/its-hysteria-not-a-heart-attack-gp-app-tells-women-gm2vxbrqk; http://unitelive.org/matt-hancock-gp-at-hand/;
https://www.newstatesman.com/politics/health/2019/07/computer-will-see-you-now?amp&__twitter_impression=true;
Burki. Lancet 2019;394:457-60.
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
68. 10. Algorithms are easily unfair
68
Rajkomar et al. Ann Intern Med 2018;169:866-72.
Obermeyer et al. Science 2019;366:447-53.
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
70. The Gartner hype cycle
70https://www.gartner.com/en/research/methodologies/gartner-hype-cycle
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
71. Let’s go to the ‘plateau of productivity’
71
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
72. Algorithms are only algorithms, and dumb
72
Chen and Asch. NEJM 2017;376:2507-9.
Beam and Kohane. JAMA 2018;319:1317-8.
Maddox et al. JAMA 2019;321:31-2.
https://www.fatml.org/; http://www.equator-network.org/reporting-guidelines/tripod-statement/
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
73. Methodology is key (as always)
73Wiens et al. Nat Med 2019;25:1337-40.
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
74. Beyond the hype towards added value
74
METHODOLOGY REPORTING
GIGO DATA QUALITY DATA SIZE
PRIOR KNOWLEDGE
HETEROGENEITY INTERPRETABILITY
TRANSPARENCY AVAILABILITY
FAIRNESS
If methodology is poor, ML will not save you.
If methodology is good, ML may or may not improve predictions
My experience:
- You get funding for cool ideas, not for good methodology
- Universities do not really check whether their researchers do high quality research
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
