Recommendations for Artificial Intelligence projects in Healthcare
Patients groups; Academics; HealthCare Professionals; hospitals; R&D, HC Industry (Pharma, Medical Device, BioTech, CROs, …), HC Insurers (private/public), Assisters, Authorities (Health Agencies, evaluators, governments…), Start-up, IT/digital operators
#Artificial Intelligence #Big Data #Machine Learning #Deep Learning #Block Chain #e-Health #Bioinformatics #Biostatistics #XAI #Algorithms #Neural Network #Analytics #Privacy by design #Anonymization #re-identification #Medico-economy #10Vs’ data #Open Data #Open Auto-ML #IoT #Wearable #Medical Device #Drug Delivery #Monitoring #Compliance #Bias #Hacking #User Experience #Education #Acceptance #Intellectual Property #Agile Methodology #IT Architecture #FDA #HMA #EMA #GDPR #EU
2. Healthcare context
2.1 Patient objectives 1st for an acceptance of AI
2.2 At the origin of the AI: the medical needs for the patient benefit
3.1 Artificial intelligence, Machine learning, Deep learning, Blockchain, Big data
3.2 The 10Vs’ of data and Open data
4. The data road map & checklist
4.1 Data management in 16 steps
4.2 Roles and Responsibilities of 6 expertise
4.3 Collecting and protecting End Value in 5 categories
4.4 IT and tools
4.4.1 Machine learning with 6 methods,
4.4.2 ML process,
4.4.3 Statistics and Bias limitation,
4.4.4 IT infrastructure: the 7 trends
4.4.5 Auto-Machine-learning: 8 auto-ML tools
4.5 Explainable Artificial Intelligence (XAI) concept
5.1 The 7 Recommendations for data Standardization, interoperability & analysis
5.2 The 5 Recommendations for data quality, validation, regulatory acceptability
5.3 The 8 Recommendations for Privacy Impact Assessment and confidence in a patient benefit balance
5.4 Algorithm and Regulation (use cases)
Artificial Intelligence (AI) is a science of making tools do things that would require human intelligence and only do what it is designed to do. It’s a promising transformation for the world and a little bit scary, only due to the fact Human decide the objective, helping people, creating new ways to service, prevent, cure…. or not!
Healthcare couldn’t escape regarding this new Eldorado promise trillion productivity or business value (Aug.2019 Gartner report), but last summer we are witnessing an outcry against major health intelligence initiatives because of the unreliability of their results. European Commission should set a world-standard to protect individuals about “sensitive data, human and ethics implications of AI “ said Ursula von der Leyen, incoming President of the Commission.
The objective of this white paper is to clarify capabilities after definition and starting recommendations for the future usage of healthcare data, compiled in the word “Artificial Intelligence”.
The conclusion of this work is to describe the best road-map, team and methodology for real deliverables and added value
ABOUT THE AUTHOR: A PIONEER IN DIGITAL MEDICINE
Vincent VARLET earned his medical degree in Paris. He received also his MBA degree from P&M Curie Paris University in 1992 and a Global Certification at INSEAD in 2015.
Dr. Varlet is the president of the Digital Healthcare think-tank «LeLab eSanté» who regroup patient groups, HealthCare professional, pharma industries, Institutional, start-ups, IOT, developers and influencers.
He spent more than 20 years in Big pharma companies, first on Marketing & Sales, then, at the end of the 90’s, he developed new skills, using a Digital business oriented in communication. He managed diversified teams as executive, seeking resources and structures optimization, business transformation, integrating innovation in concrete valuable operations.
Mentor & Coach for EIT-Health (body of EU), Dr. Varlet is now focused on business agility and flexibility, managing start-up with the experience of processes and business orientation, helping as well Big Healthcare Companies to revamp their digital mutation.
More information on www.varlet.paris
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