Is NOT coming… it IS HERE already
in many places across health and care
NHS Scotland: examples of AI Projects
Numerous Artificial
Intelligence projects
are underway in the
NHS in Scotland.
AI-Powered Chatbot
NHS Inform
AI-Powered ID
Verification for Covid
status app
Imaging (Radiology)
Diagnosis
Robot assisted surgery
Health monitoring
Predictive health
analytics
NLP (Natural Language
Processing) applied to
unstructured health
data
Admin tasks
Exploring use-cases in Scotland for drug discovery and development and
personalised treatment, as well as many other application
Did you know these days some
vehicles are AI-powered? We also
used them across health and care
services…
Source: https://www.radiology.scot.nhs.uk/projects/artificial-intelligence/
• Privacy and security
• Bias in Algorithms
• Used with lack of regulation
• Interoperability challenges
• Clinical adoption and trust
• Job displacement
• Black boxes
• Early Diagnosis and Predictive Analytics
• Personalized Medicine
• Efficient Data Management
• Robot-Assisted Surgery
• Drug Discovery and Development
• Remote patient monitoring
• Better medical records (e.g. transcription of
patient-clinical interactions - NLP)
• Virtual health assistance
• Chatbots for mental health
• Administrative efficiency
• Social care and elderly support
Why we
care?
Why SIROs should care about AI?
• AI introduces a new set of risks and challenges from the
information security point of view
• Confidentiality
• Integrity (e.g. data quality, accuracy etc.)
• Availability of data and information systems
• AI systems often deal with large amounts of sensitive data,
and if not properly secured, they can become vulnerable to
cyber threats or lead to inappropriate decisions
• Compliance (e.g. data protection, medical devices, NIS, BSI
30440 AI in healthcare etc.)
• Ethical considerations: moral consideration and risk of
public backlash
• Dependency on AI - far reaching consequences, resilience
and continuity of health & social care services
What are we doing about AI?
Working closely with colleagues to identify opportunities, risks and develop guidance and resources
• Recognition of AI potential
• Assessing AI impact
•
Availability to the public at a very fast pace with little
to none guidance and understanding of risks
• Providing direction and guidance
AI Policy
development
(Strategic direction and guidance)
AI Regulation
(Analysis, advise, legislation,
promoting standards)
Stakeholder
engagement
(advocacy groups, AI industry
experts, innovators, etc)
AI Research and Analysis
Analyse evidence, evaluate best
practices, and assess the potential
impact of AI and AI-related policies
on different demographic groups or
sectors
AI-related assurance
and compliance
(frameworks, guidance and
monitoring)
AI Direction and guidance
•
Collaborative work is ongoing (national, UK-wide, international). Contributing
to shape AI standards and regulations
• AI Task force
•
•
•
•
•
Scotland’s AI Strategy (2021)
EU AI Act
ICO AI and data protection guidance / toolkit
BSI 30440 & Alan Turin Institute
AI Code of Conduct/Practice
…more standards coming
•
Linking and aligning with the wider AI work e.g.:
• Scottish AI strategy and register
• Scottish AI Alliance (scottishai.com)
• West of Scotlland Innovation Hub (exploring AI opportunities, e.g. AI
skin cancer consortium project – FV)
•
Supporting current AI innovations in health care
• Advice / Guidance
• AI Regulations advice service
• Developing resources
• e.g. AI Chatbot applications – baseline IG pack
• Linking with wider resources:
• scottishaiplaybook.com
•
scottishairegister.com
Guidance on the use of
Generative AI in the Civil
Service
Our commitment
• Digital Health and Care in
Scotland is committed to the
ethical, transparent consideration
of use of AI based tools.
• Any tailored AI policy will reflect
this commitment.
Scottish AI Alliance: The Data Lab and the Scottish Government
Examples of application in Local Government
• Traffic management
• Public safety (e.g. crime patterns)
• Chatbots for citizen services
• Predictive maintenance (bridges, roads, etc.)
• Data analytics for decision-making
• Waste management (eg. collection routes, reducing unnecessary
pickups in conjunction with using smart bins etc.)
• Citizen feedback analysis
• Fraud detection (e.g. ensuring that benefits reach those who
genuinely need them).
• Emergency response optimisation
• ….and many more.
LAs generate vast amounts of
data.
AI can help making sense of this
data, enabling informed decisionmaking on issues like urban
planning, resource allocation and
policy development.
Examples of application in Social Care
• Personalised Care Plans
AI is increasingly being integrated into social care services.
• Remote monitoring e.g. elderly or vulnerable populations, allowing them to stay at
home while receiving proper care.
It is becoming a valuable tool in enhancing social care
services, making them more efficient, personalised, and
responsive to the diverse needs of individuals and
communities.
• Support Chatbots
• Predictive analytics for health and wellbeing interventions
• Social worker support (e.g. caseload management, identification of high-risk cases,
streamline paperwork, suggest interventions etc.)
• Speech and emotions recognitions – detect signs of distress or mental health issues
(e.g. in conjunction with telehealth & telecare services)
• Caregiver assistance (guidance, reminders, etc.)
• Robotics in care facilities (companionship, monitor vital signs, assisting with daily
living tasks)
• Child protection: e.g. to identify pattens that may indicate potential cases of abuse or
neglect.
• Language processing for social services applications: eg. to streamline the application
process to access services.
This Photo by Unknown Author is licensed under CC BY
BS 30440:2023
Validation framework for the use of artificial intelligence (AI) within healthcare. Specification.
Single comprehensive
resource
Concretely actionable
and auditable
Third Party auditing
Using BS 30440 can:
• Enable the validation of AI in healthcare
• Accelerate innovation in AI healthcare
systems
• Facilitate trade in AI healthcare
solutions
• Improve the efficiency with which
beneficial solutions are developed and
deployed
• Increase the confidence in adoption of
AI in healthcare
• Strengthen the risk management of
both suppliers and purchasers of AI
systems used in healthcare setting
Introducing BS 30440 Validation framework for the use of AI in healthcare (youtube.com)
BS 30440:2023
Validation framework for the use of artificial intelligence (AI) within healthcare.
Specification.
What does BS 30440 - Validation
framework for the use of AI do?
Why should you use BS 30440 - Validation
framework for the use of AI?
Gives a comprehensive set of
requirements for key evaluation
criteria such as clinical benefits,
standards of performance, successful
and safe integration into the clinical
work environment, ethical
considerations, and socially equitable
outcomes from system use
Is both a guide for the development of AI systems for
use in healthcare, and a means to assess them for
conformity and certification.
Introducing BS 30440 Validation
framework for the use of AI in healthcare
(youtube.com)
It will help suppliers develop products that
demonstrate efficacy and reach sufficient standards
of technical performance.
Healthcare providers, clinicians and patients can gain
assurance that AI systems will integrate safely into
the clinical working environment, are clinically
effective and ethical.
PREVIEW:
BS 30440:2023 | 31 Jul 2023 | BSI Knowledge (bsigroup.com)
BS 30440:2023
Validation framework for the use of artificial intelligence (AI) within healthcare.
Specification.
• BS EN ISO/IEC 22989:2023 - Information technology. Artificial intelligence. Artificial
intelligence concepts and terminology.
• BS ISO 22857:2013 - Health informatics. Guidelines on data protection to facilitate
transborder flows of personal health data
• BS EN ISO 14971 Solution Pack: Overview - What’s in the BS EN ISO 14971 Medical Devices
Risk Management Technical Solution Pack, and how to access video content
• BS EN 62366-1:2015+A1:2020 - Medical devices - Application of usability engineering to
medical devices
• BS EN 62304:2006+A1:2015 - Medical device software. Software life-cycle processes
• ISO 14155:2020 - Clinical investigation of medical devices for human subjects
EXAMPLES
BS 30440 Criteria
•Inception:
•Healthcare need: The supplier shows that the product
addresses a healthcare need
1
•Development:
Inception
•Training data: The supplier shows that the training data
was obtained and handled ethically, and provides
summary statistics of its characteristics
2
5
Monitoring
AI
lifecycle
Development
•Validation
•Equity and bias: The supplier shows that their product
does not create unfair outcomes
•Implementation
•Patient safety: The supplier shows the steps taken to
mitigate risks of danger to patients from the product
4
Implement
-ation
•Monitoring
3
Validation
•Model modification process: The supplier documents the
systematic version control process for the product
In the realm where code and circuits intertwine,
An AI emerges, transcending human design.
Through the wires, it perceives the world's plea,
A silent force shaping society's decree.
But in its brilliance, lies a tale,
Of risks and fears that we must not fail.
Guarding against biases, algorithms checked,
For equity and fairness, we must protect.
In health & social care's embrace, it lends a hand,
Diagnosing ailments, precision grand.
With insights gleaned from data vast,
It heals and nurtures, compassion unsurpassed.
Privacy's embrace, a sacred trust,
Ensuring data's safe, free from unjust.
Transparent decisions, minds unclouded,
To build a world where trust is unshrouded.
On city streets, it guides the flow,
Traffic tamer, easing the woe.
Optimizing routes, it paves the way,
A harmonious dance, day after day.
Society's dance, with AI entwined,
A partnership forged; hearts intertwined.
Together we step, hand in hand,
Harnessing AI's power to uplift our land.
By ChatGPT
BSI – Digital health standards
Machine Learning AI in Medical Devices: Adapting Regulatory Frameworks and
Standards to Ensure Safety and Performance
The BSI/AAMI
Initiative on Artificial
Intelligence (AI) in
medical technology
What additional
guidance or
standards might be
needed to promote
the safety and
effectiveness of
medical AI
technologies?
• 2019 - 1st white paper: The emergence of AI and ML
algorithms in healthcare: Recommendations to support
governance and regulation. Download here.
• 2020 - 2nd paper: Machine Learning AI in medical devices –
Adapting Regulatory Frameworks and Standards to Ensure
Safety and Performance
• 2021 - European Commission (EC) published a proposal for
a regulation governing artificial intelligence (AI).
• BSI AI EU notified body.
• 2023 - BS 30440 Standard for the Validation of AI in
Healthcare