VTT TECHNICAL RESEARCH CENTRE OF FINLAND LTD

Artificial Intelligence and Decision

Support – Where are we going?

Mark van Gils, Docent

Principal Investigator, VTT

mark.vangils@vtt.fi

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Artificial Intelligence (AI)

• By itself not a new concept

• There have been ’hype waves’ and ’winters’ over decades

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Strong vs Weak AI

▪ Strong AI (Artificial General Intelligence)

▪ Futuristic

▪ ’Machines with own intelligence taking over the world’

▪ Weak / Narrow AI

▪ More Realistic

▪ Excellent performing of specific, limited tasks –

e.g. navigation in cars; voice recognition;

image recognition; chatbots;

shopping recommendations.

▪ NO understanding of anything outside that task.

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Paradox of Artificial Intelligence

If you cannot yet do it with a computer, it is AI

BUT

Once you can do it, it is not AI (anymore)

551992

No AI. Just the PageRank algorithm, statistics,

and efficient data processing.

2017

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Why is it so promising this time around?

Access to tremendous amounts of data

Advances in computer hardware

Advances in machine learning research

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IEEE Spectrum Oct 2017

https://spectrum.ieee.org/static/ai-vs-doctors

AI in Health?

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A minority of the people have serious conditions, but they use most of the healthcare resources.

Thus, investing healthcare resources to the prevention of serious conditions can help to reduce

overall cost of care.

(after Prof Juha Teperi, Ministry of Social Affairs and Health, 2008)

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Priority areas for AI in Health and Wellness

Areas where we have strong assets

(health databases, technical competences)

1) Personalised care

2) Automated health data analytics

3) Continuous citizen-centric care

Areas where we can create big effects

4) Health and social care process development

5) Service automation in health and social care

6) Informed society public health decisions

Ilkka Korhonen et al 2017. Strategic Research Agenda (SRA) on “Finnish Innovation Hub for Artificial Intelligence for Health (AI for Health)”

http://www.vtt.fi/inf/pdf/technology/2017/T304.pdf

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Personalised care: health care treatments to

match the unique characteristics of individuals. Genomic and

other ‘omics data with other health and behavioral data

including data from sensors.

AI is needed to process huge amounts of data.

Automated health data analytics: Automated analysis of complex health data - imaging,

electronic health records, sensor data –

reliable quantification and interpretation.

AI to pre-process, and analyse data.

Continuous citizen-centric care: Improve continuous preventive

management of health of individuals by automatically monitoring and integrating

information.

Use AI to analyse, interpret changes in health status. Engage. Motivate.

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Health and social care process development

▪ Provide right actions at right time on right patients/clients.

▪ Apply AI to improve the productivity in health and social care as well

as wellness services.

▪ Forecasting of resource needs,

predictive care path planning,

predictive outcome analysis,

intelligent scheduling and

resource organisation.

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Service automation in health and social care

▪ Reduce the need for routine human intervention where possible,

freeing personnel time to focus on the most value-adding care

activities.

▪ Application of chatbots (e.g. time booking, information enquiry,

triage), AI-based user interaction, automated transformation of

information between different systems, and NLP (Natural Language

Processing) applications.

▪ An essential component here is the accurate identification of

situations where human communication is required instead of AI (e.g.

identification of life-threatening conditions).

A robot retrieving drugs in the pharmacy of a major hospital. Alibaba Health in three major Chinese Hospitals.

Photo: Agence France-Presse

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Informed society public health decisions

▪ The impact of health-related policy decisions in society are difficult to

predict and require complex modelling of the entire system (society).

▪ Improve health and social wellbeing related decision making on

individual, organizational, and societal levels via systematic and

intensive use of data.

▪ AI can help in planning the optimal population-level strategies e.g. for

disease screening and other public health campaigns.

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ELSE (Ethical, Legal, Socio-Economic) aspects

▪ AI applications have several potential ethical issues, which need to

be properly recognised and addressed.

▪ preservation of privacy

▪ an individual’s right to opt-out (e.g. decline to consent for secondary use

of his/her data)

▪ managing access rights for the data and

related conclusions

▪ …

▪ Liability issues: if AI is making decisions,

who is liable for potential adverse outcomes?

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Recommendations

▪ Healthcare professionals: acquire basic knowledge about how AI

works to understand how it might help in everyday work. Think about

where automation could help make life easier.

▪ Decision makers at healthcare institutions: measure the success

and the effectiveness of the system. This is the only way to assess

the quality of AI’s help in medical decision making.

▪ Companies: communicate even more towards the general public

about the potential advantages and risks of using AI in medicine –

and sort out data privacy issues!

▪ Non-English speaking countries: invest in natural language

processing (NLP). If the patient information is not in English, A.I.

needs to understand the content in that language.

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Where are we going?

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