MPNE patient consensus on data and AI 2.0

Melanoma Patient Network Europe

mpne

WHITE PAPER Patient Consensus

Data, AI and data- dependent models for business and research.

Version 2.0

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Patient Consensus v2.0

impressum Melanoma Patient Network Europe Legal entity MPNEsupport Fjällbo Selknä 152, 74177 Uppsala, Sweden Org 168024921069 https://www.mpneurope.org/

disclaimer MPNEconsensus 2024 was conducted as part of MPNE's deliverables for the iToBoS project under WP11 'patient engagement and education, Task 11.1. iToBoS is a project funded under the European Horizon 2020 research and innovation program under grant agreement 965221.

The information and views provided in this document are solely the authors' and do not constitue endorsement by the European Comission.

www.mpneurope.org

Patient Consensus v2.0

A patient consensus on Data, AI and data-dependent models for businesses and research.

B. Ryll11, G. Spurrier-Bernard¹,2, A. Evans1, R. White1, V. Astratinei1,3,4, F. Östman1,5, Tamás Benkó1,6, H. Boetel1,7, K. Curtin1,8, I. James1, M. Pajunen1,9, A. Skoutari1,10, S. Szókovács-Vajda1,6, O. Valciņa1,11, A. Wispler1,7, L. Zielinska1,12 affiliations

1 Melanoma Patient Network Europe, www.mpneurope.org 2 Melanome France, France 3 Melanom Romania, Romania 4 Stichting Melanoom, Netherlands 5 Melanomföreningen, Sweden 6 Magyar Melanoma Alapítvány, Hungary 7 Hautkrebs-Netzwerk Deutschland e.V., Germany 8 Melanoma Support Ireland, Ireland 9 Cancer Patients Finland - Suomen Syöpäpotilaat, Finland 10 PASYKAF CY, Cyprus 11 Melanoma Patient Network of Latvia “Step Ahead of Melanoma”, Latvia 12 Polish Sarcoma and Melanoma Patients Association /Stowarzyszenie Pomocy Chorym na Mięsaki i Czerniaki Sarcoma/, Poland

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Patient Consensus v2.0

executive summary MPNEconsensus 2024, a patient consensus process on data, AI and data-dependent business models was conducted by MPNE, the Melanoma Patient Network Europe, in early 2024.

The objective was to develop an independently generated patient position as part of the patient engagement of the iToBoS- intelligent Total Body Scanner- project as part of MPNE's responsibilities. iToBoS has received project funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 965221 und which the consensus meeting was funded. The event was generously hosted by the iToBoS partner Fraunhofer Institute for Telecommunications, Heinrich-Hertz-Institut, HHI, Berlin.

This White Paper summarises the consensus statements formulated based on the discussions at MPNEconsensus 2024 and constitutes of 10 core consensus statements capturing over-arching principles and consensus statements on 8 sub-sections: Data governance and future-proof ‘zero-trust’ systems; Privacy-preserving AI in healthcare; Ethical frameworks for data use and AI; Trustworthy and reliable AI beyond explainability; Regulatory frameworks, independent process oversight, effective enforcement and punitive sanctions for misconduct; Mitigation, proactivity and agility; New models for data-dependent business and research and Patient-led independent spaces for learning and debate to advance complex issues.

The White Paper is intended as a 'Living document' and aims to contribute an independently formulate patient perspective to the general data discussion, e.g. on the European Health data Space, the AI Act, the European Cancer Patient Data Centre and other data-related initiatives and projects.

The objective of an independent patient consensus process is to provide the community of those, who both stand to considerably benefit but who also carry the ultimate risk, with a self-owned process to build community consensus and to arrive at substantiated positions free from the habitual framing imposed by other stakeholder groups. MPNEconsensus 2024 is MPNE's second consensus process, a third one is in preparation and will be published in due course, together with the overall methodology.

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Patient Consensus v2.0

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The consensus process has recently been recognised by the EU Innovation Radar as Innovation under the title: Socio-Cultural and Ethical Guidelines for Future Implementation of AI in the Medical Context1.

This work would have not been possible without the contribution of those willing to make their technical expertise accessible to a non-specialist audience and to engage in discourse over extended periods of time.

We therefore thank all internal and external speakers and in particular, our iToBoS colleagues from the Fraunhofer Institute for Telecommunication Berlin, IBM Research in Haifa and the Hannover Centre for Optical Technologies, for their effort, time but above all: enthusiasm! to make their technical fields of work accessible to the MPNE community.

Bettina Ryll

1 Innovation radar > Innovation > Socio-Cultural and Ethical Guidelines for Future Implementation of AI in the Medical Context. https://innovation-radar.ec.europa.eu/ innovation/57594.

Patients first.

MPNE principle No 1

MPNE founder

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Patient Consensus v2.0

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Patient Consensus v2.0

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contents

impressum

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executive summary 3 table of contents

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MPNEconsensus 2024

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Consensus phases 9 Problem statements

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MPNE core consensus statements

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MPNE specific consensus statements

19- 40

Data governance and future-proof ‘zero-trust’ systems 19 Privacy-preserving AI in healthcare 23 Ethical frameworks for data use and AI 25 Trustworthy and reliable AI beyond explainability 27 Regulatory frameworks, independent process oversight, effective enforcement and punitive sanctions for misconduct 33 New models for data-dependent business and research 37 Patient-led independent spaces for learning and debate 39 conclusions 41 next steps

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annex 44 consensus meeting program 45 about 47

Patients first.

MPNE principle No 1

Mitigation, proactivity and agility 35

Patient Consensus v2.0

The MPNEconsensus 2024 The MPNEconsensus2024 took place at the Frounhofer Institute for Telecommunications, Heinrich-Hertz-Institut, Berlin 31st January- 2nd February 2024.

Motivation Today, research and innovation projects

Patients are the stakeholder group to

in health nearly invariably rely on

ultimately benefit most from successful

some form of health data acquisition

researchbut also to carry most of the

and analysis. Discussions about the

unintend consequences. Striking a

harmonisation of data acquisition, data

successful balance that both enables

sharing and access rights between

research while mitigating and addressing

countries and legislations, privacy-

risks is therefore a core patient interest.

preserving methods and strategies, data analytics and validation of methods and models, including AI models are therefore common and shared between different research projects.

The objective of the MPNEconsensus meeting was to generate independent patient consensus on data-related topics identified as problematic by the patient community.

Consensus process Consensus building is an iterative

impact on patients again (1). The process

process starting with observation and

is protocolled to trace the development

understanding of patient need (1),

of the consensus to improve the process

followed by a phase of learning with

itself.

consulting of expertise and debate (2) and a phase for formulating decisions and actions (3), to then observe the

2024

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This is MPNE's second Consensus process.

Recognised by the EU Innovation Radar The consensus meeting was recognised in 2024 as innovation by the EU innovation radar under the title: Socio-Cultural and Ethical Guidelines for Future Implementation of AI in the Medical Context2.

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Patient Consensus v2.0

Objectives A process to help patient

deliberation and a consensus-building

communities develop valid and

process involving a sufficiently large

substantiated positions on complex

proportion of the advocacy community

topics. A valid position on complex

and methodology open for analysis and

topics requires background research,

continuous improvement.

reflection and debate and is therefore not suitable for ad hoc single positions and majority votes; it rather requires

Making patient concerns accessible

with access to expertise, interests and

for other stakeholders. The interaction

constraints of the former. ‘Pre-decision’

with diverse stakeholders in the

safe spaces allow to formulate positions

consensus building process creates a

that can then be tested and re-worked

bi-directional information exchange,

in subsequent exchanges, continuously

providing outsiders with access to the

improving and refining them.

insights, reflections and the working of a patient community and the latter

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Patient Consensus v2.0

Consensus phases

1st Phase

2nd Phase

3rd Phase

November 23- January 24

January - February

March- April

Preparation

MPNEconsensus

Documentation

During the preparation phase of

MPNEconsensus 2024

Based on the meeting notes

the meeting, MPNE advocates

took place in Berlin 31st

and the consensus statements

formulated statements

January- 2nd February 2024.

formulated at the MPNE

capturing the concerns of

Consensus statements were

consensus meeting, Version 1.0

the patient community; these

formulated after each of the

of this White Paper was drafted.

were presented in form of an

specific sections and reviewed in

Oxford Debate at the consensus

total during the final day of the

meeting.

conference.

Motion of the Oxford debate ‘This house believes that patients should be compelled to give up their data for whatever purpose the state sees fit; if they wish to benefit from solidarity-based funded healthcare'

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Patient Consensus v2.0

5th Phase

6th Phase

May- August

September - October

next

Internal consultation

Version 2.0

External consulation

Version 1.0 was circulated

The consensus sections on

The White Paper v 2.0 will be

internallly to different audiences

specific topics were expanded,

circulated to internal and external

and adapted based on the feed-

adding background and

communities for discussion.

back received. A first version of

references, the document was

Previously involved and new

the core consensus statements

re-circulated internally for feed-

experts will be contacted for

was published on the MPNE

back, resulting in the present

further discussion/ elaboration/

website.

version of the Consensus White

action on respective sections for

Paper.

a future version 3.0

MPNE is a volunteer-based European network of Melanoma patients and family members.

The network operates across language and healthcare systems barriers and works principle-based, ensuring patient-centricity, focus on solutions, evidence-basis, proactivity and constructiveness.

If you don’t do itno one will.

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MPNE principle No 4

4th Phase

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Patient Consensus v2.0

Problem statements Starting points for the MPNEconsensus 2024 meeting were broad, datarelated problems the MPNE community had identified in their work; these were brought forward in the Oxford Debate.

1.0 Problematic access to data Problematic access to personal data

even if they contain clinically relevant

and publicly financed data sources

or actionable information such as

casting doubt on the motives and

pathological germline variants or

credibility of the parties involved.

prognostic factors.

Patients struggle to access their own

Publicly funded data sets are

medical records, including pathology,

not inaccessible even for other

laboratory and imaging files and

established, publicly funded research

reports. Patients are thereby often

consortia, leading to avoidable

left in the dark about their personal

duplication and undermining rapid

rights and GDPR is falsely invoked to

progress in research.

withhold data.

This casts serious doubts on the

Research data and results are

intentions and credibility of the parties

routinely withheld from patients,

involved.

The MPNEconsensus 2024 meeting was conducted within the iToBoS project.

Problem statements were however deliberately broadened to ensure relevance beyond the well-defined data-related scope of iToBoS; statements are based on the MPNE community’s long-standing involvement in diverse data-related activities.

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Patient Consensus v2.0

2.0 Altruism gaslighting Patients’ desperation and

Dependency on care team. Craving

dependency is unduly exploited to

a good relationship with their clinical

force consent under the cover of

team on which they ultimately

‘altruism’.

depend for their care makes patients

By the time you know enough, it’s too late. Patients, dealing with disease and treatment uncertainty in health landscapes that they don’t fully understand, can hand over control of their lives and information, simply

vulnerable to decisions that please their team and to consent for fear of negative repercussions. Decisions under these conditions of dependency cannot be considered independent nor free.

because they do not feel equipped to

Unjustified moral pressure. Despite

take responsibility or even capable

the fact that they themselves, as

of questioning, as they often would

European patients, are contributing to

in other choice situations such as

solidarity-based healthcare systems,

lifestyle, financial or commercial

especially patients in tax-financed

“transactions”.

healthcare systems are then made to

Especially at first diagnosis, cancer patients frequently have to make far-reaching decisions without having had the time to sufficiently educate themselves. This leads to the tendency

feel as petitioners for charity provided by others rather than as the entitled users they are, further increasing moral pressure on patients to comply with the wishes of others.

to heavily rely and trust those who

This is often exacerbated by the way

speak authoritatively around them,

health systems view patients who

whether or not that is merited.

question, as“difficult”, “time-wasters”

With time and the initial shock of a cancer diagnosis receding, patients’ understanding of the disease and familiarity with the health system increases, making them more critical and discerning. However at that point in time, the ‘horse has bolted’ and

or “antisocial”: there is frequent gaslighting of patients by healthcare professionals, the health system and even their own communities to ‘behave altruistically’ and to e.g. share their time, data and tissue without concern for themselves.

patients have already made decisions before being aware of potential consequences to themselves or their families.

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continued

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Patient Consensus v2.0

The combination of information

stakeholder groups and against

asymmetrie, dependency on care

their personal best interest; almost

providers and moral pressure can

uniquely in human experience, in

make patients easy targets for

social healthcare, it continues to be

exploitation for the interests of other

encouraged by societal leaders.

3.0 Unilateral value extraction Data needs to generate true value

collaboration- ‘Everyone wants our

for all parties involved, not just

data but no one wants to share theirs’.

unllaterally.

Financial models were recognised

The MPNE community is acutely aware

as problematic as data platforms

that improving patient outcomes

were mainly driven by the interest to

relies on impactful research and swift

monetise data while offering patients

and effective translation of research

services of limited interest in return.

findings into processes, products and services that can ensure research.

In contrast, business models that a good for patients because they are

At the same time, the community is

not reliant on selling data struggle to

frustrated about current approaches

find investment and don’t survive, e.g.

that mainly result in siloes

ImputeMe1.

‘everyone their own data platform or registry’, access control and lack of 1

Folkersen, L. et al. Impute.me: An Open-Source, Non-profit Tool for Using Data From Direct-to-

Consumer Genetic Testing to Calculate and Interpret Polygenic Risk Scores. Front. Genet. 11, 578 (2020).

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Patient Consensus v2.0

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4.0 Lack of transparency and accountability Appeals for ‘trust’ are no

such as ‘scientific progress’, ‘better

replacement for transparency and

patient outcomes’ and the reality of

accountability.

the community e.g. looking for second

In the current data debate, the MPNE community missed concrete value propositions to patients and saw a

opinions, access to clinical trials and treatment and specific expertise such as radiotherapy or palliative care.

In addition to the disregard of the

The community felt strongly that

valid expectation of patients to see

trustworthiness therefore had to

concrete benefit from sharing their

be enshrined into the system itself

data, the equally valid concerns

in a trust-by-design approach,

and reservation regarding risks and

ensuring auditability, transparency of

negative consequences to themselves

decision-making and accountability,

but more often, their families, are

and needed to be above the reach of

brushed aside. Instead, patients are

individuals or organisations that might

encouraged to place their ‘trust’ in

be willing to forgo citizens’ rights for

organisations that in the past have

personal, economic, political or other

rather demonstrated their inability to

interests.

place citizen rights and interest first.

Data, not opinions. www.mpneurope.org

MPNE principle No 3

clear gap between vague promises

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Patient Consensus v2.0

5.0 Constructive contribution Constructive contribution to

regulatory and HTA level and closely

discussions and policies on data and

followed European data-related

AI is time-consuming and requires

initiatives, e.g. TEHDAS1 and 22, GA4GH3

learning, access to expertise and

or TEF-Health4 and the development of

debate.

the ECPDC5.

In the past years, the MPNE community

We believe that civil society needs to be

has been contacted with increasing

an integral part of decision-making in

frequency to comment on policy drafts,

democracies and therefore highly value

white papers or other statements

these opportunities.

concerning data and AI. We are

However, it also became clear

regularly invited to conferences and

that meaningful and constructive

workshops for a patient position on

contribution critically relies on iterative

data, AI and related topics.

processes of a) accessing diverse

MPNE has actively participated in initiatives such as GetReal1, multiple initiatives on the use of Real World

expert knowledge and b) broad discussion within the community, requiring considerable effort and time.

Evidence in decision-making at

1

GETREAL | IHI Innovative Health Initiative. https://www.ihi.europa.eu/projects-results/project-

factsheets/getreal.

2

Second Joint Action Towards the European Health Data Space – TEHDAS2 - Tehdas. https://tehdas.

eu/.

3

GA4GH. https://www.ga4gh.org/.

4

TEF-Health - Testing and Experimentation Facility for Health and Robotics. https://tefhealth.eu/

home.

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An operational concept for a European Cancer Patient Digital Centre - Publications Office of the EU.

https://op.europa.eu/en/publication-detail/-/publication/495c3a52-2161-11ef-a251-01aa75ed71a1/languageen.

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MPNE principle No 2

Patient Consensus v2.0

Solutions, not problems.

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Patient Consensus v2.0

MPNE core consensus statements 1- Data use is an ethical imperative as people are dying and we as society have data that could save them.

2- Data should be used but not abused. Individuals and societies need to be effectively protected against abuse; this will require new ethical frameworks for the use of data and AI, diverse risk mitigation strategies, effective enforcement and proactivity and agility in the approach.

3- The approach to data use should be risk-appropriate, similar to the benefit/ risk trade-offs seen with drugs. Data is not a one-size-fits all category: differentiation of different data sets and the risks associated with them is critically needed.

4- Health data is common goods - it is not acceptable to extract value for a few and settle patients and society with the risks; this will require new models for business and research beyond platformization.

5- Altruism gaslighting is unacceptable: people have valid concerns and reservations, e.g. with regards to their privacy, protection against misuse and onesided value extraction.

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Patient Consensus v2.0

6- Citizens need ‘zero-trust’ data environments beyond the reach of single parties, institutions or governments, in particular, for sentitive data such as genomic data, to protect the rights of citizens.

7- Governance needs to ensure that those most affected by the risks have a voice.

8- It is evident that tech is not able to self-regulate, control therefore has to occur at the level of laws and regulations. Citizens and patients need hard guardrails of what is permissible when it comes to data-use; realtime monitoring and effective enforcement, e.g. existential fines to ensure compliance with laws, regulations and ethical standards..

9- Society and patients need future-proof approaches, taking into account ‘the unknown unknowns’ of future technologies. Society, regulators and decisionmakers need to consider the risks for today’s but also for future generations.

10- We as a patient community need independent spaces for learning, exchange and debate to develop understanding and positions on complex topics that affect patients, such as data and AI.

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Patient Consensus v2.0

Data governance and future-proof ‘zero-trust’ systems

Consensus statements •

Human governance needs translation into the digital space, with layered,

distributed and democratic governance models instead of strongly centralised, techno-autocratic models.

•

The approach to data use should be risk-appropriate, similar to the

benefit/ risk trade-offs we see with drugs.

•

“Zero-trust” environments enable trust-by-design and be beyond the

control of single individuals, authorities or institutions, and governments to ensure that the rights of the individual are protected •

Approaches should be future-proof, taking into account ‘the unknown

unknowns’, and not only consider risk to today’s but also to future generations.

•

Governance needs to ensure that those most affected by the risks have a

voice.

•

Broad ethical debate is now needed to keep pace with technological

progress •

Proactivity is critical, TestBeds and regulatory Sandboxes can help to

systematically evaluate new technologies and increase the likelihood to also uncover unexpected consequences while providing all stakeholders involved with valuable learning opportunities.

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Patient Consensus v2.0

The community agreed that there was

patient but also against family members

a moral imperative to use data while

and descendents, e.g. in the form of

patients and societies stood to benefit

discrimination in an insurance setting.

from it. The value of the data thereby did not reside in the data itself but rather the insights generated from it, rendering data access, data flow as well as quality and quantity important value components. It was pointed out that data was not a homogenous category but rather that different types of data came with different levels of sensitivity and risk to the individual or groups of inviduals, such as genomic data. This should warrant a risk-based approach

It was pointed out that negative consequences could arise despite original good intent; systems therefore needed to monitor not only where and how data was collected, used, transformed and re-used but also how and for what purpose the resulting information and insights were used, with the capability and mandate to intervene, within an overarching legal and governance framework.

to data-use, not unlike in the regulation

Data governance thereby referred

of medicines where benefit/ risk trade-

to both data management and the

offs are strongly context-dependent: a

overarching regulation. Participants

treatment for an untreatable cancer will

highlighted that, given the current

be met with different expectations than

speed of technological development,

a vaccine intended for use in healthy

such a governance model needed to be

individuals.

flexible and agile enough to anticipate

It was further noted that, while there might be a moral imperative for use, the consequences of accidental or malicious misuse were unevenly born and go beyond the original core population in question, so while a situation of high unmet need might justify the acceptance of a high level of risk, for example by broadly sharing genomic and clinical information, inheritable germline information derived from that setting could then be used not only for the

and respond to future developments to avoid becoming obsolete too soon.

The core idea was that governance models and ethics needed to co-evolve with technological advances to ensure data regulation was ‘future-proof’.

Governance frameworks should thereby be able to address not only expected but also unforeseen risks, ‘the unknown unknowns’ and not only consider the present risks but also that to future generations.

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Patient Consensus v2.0

The community agreed that in order

regulators, policymakers and other

to be trustworthy, such a governance

relevant actors, across all levels was

model could not depend on trust

seen as important to ensure legitimacy

in single individuals, institutions,

of the system and as essential for

authorities or governments but needed

ensuring that the needs and concerns

to be enshrined in the structure of the

of the respective communities were

system itself, ensuring ‘trust-by-design’.

appropriately .

Centralisation was seen as a major risk

In light of the far-reaching implications

because of the undue concentration

of data use and in particular, AI, broad

of power and as a barrier to equitable

ethical debate involving society at

development. In contrast, the community

large was considered important, with

saw a layered governance model that

particular attention to ensuring that

distributed decision power and authority

those who carry the largest risk had a

across multiple entities or individuals

voice.

in a ‘checks-and-balances’ type of fashion as considerably more capable of promoting transparency, accountability, and collaboration, ultimately resulting in trust across the entire (healthcare) ecosystem.

Overall it was felt that proactivity, rather than reactivity, was critical and that more research, systematic testing and learning, e.g. in the form of Testbeds or regulatory Sand Boxes was needed to develop effective governance

Broadest possible multi-stakeholder

mechanisms that safeguard patients

engagement, involving patients,

and society while enabling innovation in

healthcare providers, researchers,

healthcare delivery and research.

innovators, the commercial sector,

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Patient Consensus v2.0

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Patient Consensus v2.0

Privacy-preserving AI in healthcare

Consensus statements •

Diverse mitigation: we need more research and concepts to address the

handling of data and in particular, AI, in a way that is privacy-preserving and protects the individual and societies. The approach should be systematic and forward-looking, with clear assurance levels and leveraging diverse mitigation strategies as no single solution will be able to address everything.

•

Anticipation: Monitoring and anticipation of future privacy breaches

through new technologies or methods is needed to ‘stay ahead of the curve’.

•

Accountability: Incentives and legal frameworks are critical to hold

entities accountable ‘it’s all about security, not about privacy’.

•

Data minimisation: payment could be an effective incentive for data

minimisation, increasing privacy and reducing environmental impact.

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Patient Consensus v2.0

The European Artificial Intelligence

in the lifecycle of a machine learning

Act provides comprehensive rules for

model and require a set of different

trustworthy AI, including a risk-based

measures to ensure that a model is

approach, the obligation that high-

privacy-preserving. For example, a

impact General Purpose AI models

combination of data masking, model-

with systemic risk must conduct

guided model anonymisation and model

model evaluations, assess and mitigate

data minimisation is used for iToBoS.

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systemic risks and must conduct adversarial testing and requires that data used to train such models are subject to appropriate state-of-the-art security and privacy measures.

Privacy-preservation is a research topic in itself that needs to keep pace with the development of new machine learning models and needs to anticipate future privacy attacks. Incentives and

Anonymous data is GDPR-exempt,

legal frameworks are thereby critical to

making it attractive for developers and

hold entities accountable for privacy

theoretically, also patients.

breaches. Data minimisation is part of

However, privacy attacks on Machine Learning Models have been able to reveal private information from training data, making privacy-preservation an important topic for AI in healthcare due to the particular sensitivity of the underlying data.

the privacy-preserving toolkit, financial incentives (pay for the data ‘unit’ used) could provide an effective stimulus for the development of machine learning models relying on as little data as possible, simultaneously reducing the environmental impact of AI.

Privacy thereby finds itself in a trade-off with accuracy and performance: these need to be explored at different stages 1 EU AI Act: first regulation on artificial intelligence | Topics | European Parliament. https://www.europarl.europa.eu/topics/en/article/20230601STO93804/eu-ai-act-firstregulation-on-artificial-intelligence.

2 Artificial intelligence (AI) act: Council gives final green light to the first worldwide rules on AI - Consilium. https://www.consilium.europa.eu/en/press/pressreleases/2024/05/21/artificial-intelligence-ai-act-council-gives-final-green-light-tothe-first-worldwide-rules-on-ai/.

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Patient Consensus v2.0

Ethical frameworks for the use of data and AI

Consensus statements •

Data use for societal good is an ethical imperative; it must however be

balanced with cautionary measures to protect from undesired consequences and abuse.

•

Health data is common goods; it is not acceptable that value and

benefit is extracted for a few while society is settled with the risks and negative consequences.

•

Inherent tensions between individual and societal interests need to be

recognised and addressed fairly, according to ethical principles based on broad societal consensus.

•

Bias in medical datasets is of particular concern as it exposes

subpopulations of patients to increased risk and can increase inequity.

•

Independent ethical frameworks for data use and AI are needed; these

frameworks need to be based on broad societal consent.

•

We need citizen ethics and independent citizen ethics boards to ensure

citizens have an effective voice.

•

We need effective, ethical frameworks for AI and data use that keep pace

with the current technical development.

•

Ethical and legal frameworks need to be integrated to ensure ethical

principles are implemented.

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Patient Consensus v2.0

The community agreed that it was

community. Considering melanoma care,

an ethical imperative to use data and

bias in datasets could result in missed

technology for the good of society and

diagnoses, inappropriate treatment, or

patients as a particularly vulnerable

overlooked treatment options, potentially

group. At the same time, effective

jeopardising patient outcomes and

measures needed to be in place to

resulting in inequity as novel algorithms

protect individuals, groups of individuals

e.g. worked better for some patient

and societies from undesired unintended

populations than others.

consequences and from deliberate misuse.

Ethical frameworks for data use and AI should therefore provide guardrails

The community considered health

ensuring that technologies are

data as a common good and that the

designed, implemented, and utilised in

equitable distribution of benefits and

alignment with consensus-based ethical

risks across all members of society

considerations to guarantee fairness and

needs to be assured1.

accountability.

The group acknowledged that the

While multiple ethical frameworks

interests and needs of the individual and

are being developed, e.g. frameworks

those of the group could be at tension

for data use in the European Health

or at odds with each other and that

Data Space EHDS1,2, the Data Ethics

processes needed to be in place in order

Framework for the US Federal

to resolve these situations to prevent

data strategy3 and the UNESCO AI

‘stalemate’ situations harming everyone.

stewardship rules4 , the community felt

Further, the ethical implications of bias, e.g. ethnic, gender, geography, socioeconomic, in datasets was discussed and of particular concern to the

1

that, considering the profound effect of data and AI on humanity, broader societal involvement and a new form of Citizen Ethics was needed.

Staunton, C., Shabani, M., Mascalzoni, D., Mežinska, S. & Slokenberga, S. Ethical and social reflections on

the proposed European Health Data Space. Eur. J. Hum. Genet. 32, 498–505 (2024).

2

Dataetisk Tænkehandletank. https://dataethics.eu/.

3

fds-data-ethics-framework.pdf. https://resources.data.gov/assets/documents/fds-data-ethics-

framework.pdf.

4

Ethics of Artificial Intelligence | UNESCO. https://www.unesco.org/en/artificial-intelligence/

recommendation-ethics.

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Patient Consensus v2.0

Trustworthy and reliable AI beyond explainability

Consensus statements •

In medical care, errors in AI risk to cause direct patient harm. Validation

strategies of medical AI algorithms are therefore extremely important, including model interpretability, explainability and reliability.

•

Explainability depends on the audience and AI models are increasingly

becoming incomprehensible for humans. Society needs assurance and security in AI that go beyond explainability.

•

Medical data is sensitive by nature and AI models trained on it can

inadvertently reveal privacy-endangering information. Strong guidelines and methods for the development of AI training data need to ensure that the privacy of the people whose data is used is ensured, by e.g. ensuring that personallyidentifiable information (PII) is excluded and/or adequately anonymized.

•

Bias in data sets can place patients at risk. AI models would benefit from

greater peer review of training strategies, including system prompts, reinforcement learning parameters, and fine tuning, to catch the potential for bias.

•

Patients should be made aware when and where AI has been used in their

care pathways.

•

Over-reliance on algorithms represents a danger in itself as it creates

dependencies on the availability of those algorithms, e.g. also sufficient energy supply, and as the unfettered amplification of hidden biases in datasets can lead to grave errors and harm.

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Patient Consensus v2.0

In light of recent developments, Artificial Intelligence occupied a large part of the discussion at the consensus meeting.

‘Artificial intelligence systems have become increasingly prevalent in everyday life and enterprise settings, and they’re now often being used to support human decision-making. These systems have grown increasingly complex and efficient, and AI holds the promise of uncovering valuable insights across a wide range of applications. But broad adoption of AI systems will require humans to trust their output.

When people understand how technology works, and we can assess that it’s safe and reliable, we’re far more inclined to trust it. Many AI systems to date have been black boxes, where data is fed in and results come out. To trust a decision made by an algorithm, we need to know that it is fair, that it’s reliable and can be accounted for, and that it will cause no harm. We need assurances that AI cannot be tampered with and that the system itself is secure. We need to be able to look inside AI systems, to understand the rationale behind the algorithmic outcome, and even ask it questions as to how it came to its decision’ Source https://research.ibm.com/topics/trustworthy-ai

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Patient Consensus v2.0

According to the Ethics Guidelines for

should be explained in a manner adapted to

Trustworthy Artificial Intelligence of the

the stakeholder concerned. Humans need to

High-Level Expert Group on AI set up by the

be aware that they are interacting with an AI

European Commission, trustworthy AI is

system, and must be informed of the system’s

lawful, ethical and robust1.

capabilities and limitations.

The Guidelines put forward a set of 7 key

Diversity, non-discrimination and fairness:

requirements that AI systems should meet in

Unfair bias must be avoided, as it could could

order to be deemed trustworthy:

have multiple negative implications, from the

Human agency and oversight: AI systems should empower human beings, allowing them to make informed decisions and fostering their fundamental rights. At the same time, proper oversight mechanisms need to be ensured, which can be achieved through

marginalization of vulnerable groups, to the exacerbation of prejudice and discrimination.

Fostering diversity, AI systems should be accessible to all, regardless of any disability, and involve relevant stakeholders throughout their entire life circle.

human-in-the-loop, human-on-the-loop, and

Societal and environmental well-being: AI

human-in-command approaches

systems should benefit all human beings,

Technical Robustness and safety: AI systems need to be resilient and secure. They need to be safe, ensuring a fall back plan in case something goes wrong, as well as being accurate, reliable and reproducible. That is the only way to ensure that also unintentional harm can be minimized and prevented.

Privacy and data governance: besides ensuring full respect for privacy and data protection, adequate data governance mechanisms must also be ensured, taking into account the quality and integrity of the data, and ensuring legitimised access to data.

Transparency: the data, system and AI

including future generations. It must hence be ensured that they are sustainable and environmentally friendly. Moreover, they should take into account the environment, including other living beings, and their social and societal impact should be carefully considered.

Accountability: Mechanisms should be put in place to ensure responsibility and accountability for AI systems and their outcomes. Auditability, which enables the assessment of algorithms, data and design processes plays a key role therein, especially in critical applications. Moreover, adequate and accessible redress should be ensured.

business models should be transparent.

Traceability mechanisms can help achieving this. Moreover, AI systems and their decisions 1 EU COMMISSION, ETHICS GUIDELINES FOR TRUSTWORTHY AI . https://digital-strategy. ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai.

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Patient Consensus v2.0

Trustworthiness includes

trustworthiness in AI. However, closed-

independent validation and

source model code and weights make

verification

it impossible to independently verify

Essential aspects of trustworthy AI

algorithms.

were reviewed, such as AI systems

Explainability- analysing based on

that demonstrate dependability,

which factors an algorithm makes

transparency, precision, coherence, and

decisions- was discussed as another

ethical conduct throughout their entire

way to increase the precision and

lifecycle, spanning from conception and

trustworthiness of AI. It was however

design to deployment and operation.

noted that the approach was slow and

Several strategies to achieve

30

relied on open models.

trustworthiness in AI were discussed,

It was also pointed out that as model

such as the concept of explainable

sophistication scales up by orders of

AI, establishing clearer guidelines and

magnitude in the coming years, models

consensus regarding training data

will reach a point where human model

sets, independent validation of training

comprehensibility will no longer be

strategies, implementing privacy

practical nor even possible1,2, increasing

safeguards through the utilisation of AI

the importance of tools that allow to

models with anonymised datasets, and

evaluate AI algorithms in ways that go

adopting practices to mitigate the risks

beyond explainability.

associated with data bias.

Independent validation of AI models

1

Introduction - SITUATIONAL AWARENESS: The Decade Ahead. https://situational-awareness.ai/.

2

OpenAI Believes Superhuman AI Could Emerge Within the Decade and is Giving $10M in Grants to Solve

the Alignment Problem. https://www.maginative.com/article/openai-launches-10-million-in-research-grants-foraligning-superintelligent-ai/.

It's a feature, not a bug. www.mpneurope.org

MPNE principle No 5

was discussed as a measure to increase

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Patient Consensus v2.0

Ethical AI

race to ever more powerful AI models,

The uncritical use of AI has been reported to lead to discrimination in

downsized rather than increased their safety and alignment teams5,6.

e.g. hiring processes1 or insurance

A further topic of discussion was the

decisions , , caused by biases in

danger of over-reliance on AI to answer

the training data sets and lack of

in particular difficult questions where the

human oversight, raising concerns for

underlying dataset might not provide

‘algorithmic discrimination in health

sufficient information and models

care’ . In medicine, the uncritical use of

started producing hallucinations that

AI can lead to direct patient harm and

could lead to serious human harm if left

therefore requires particular attention

unchecked.

2 3

4

to accuracy, reliability and sensitivity to biases.

Model alignment ensures that AI models are consistent with ethical principles but remains a very difficult area of AI research, with considerable resources required to ensure model safety and

The community felt it important for patients to be aware when and where AI had been used during their care and that the use could be both positive and negative.

transparency.

The community was therefore concerned about recent developments in the commercial AI world that, in the heated

1

Chen, Z. Ethics and discrimination in artificial intelligence-enabled recruitment practices. Humanit. Soc.

Sci. Commun. 10, 567 (2023).

2

Obermeyer, Z., Powers, B., Vogeli, C. & Mullainathan, S. Dissecting racial bias in an algorithm used to

manage the health of populations. Science 366, 447–453 (2019).

3

Celi, L. A. et al. Sources of bias in artificial intelligence that perpetuate healthcare disparities—A global

review. PLOS Digit. Heal. 1, e0000022 (2022).

4

Wójcik, M. A. Algorithmic Discrimination in Health Care: An EU Law Perspective. Heal. Hum. rights 24,

93–103 (2022).

5

OpenAI’s Long-Term AI Risk Team Has Disbanded | WIRED. https://www.wired.com/story/openai-

superalignment-team-disbanded/.

6

Microsoft lays off AI ethics and society team - The Verge. https://www.theverge.

com/2023/3/13/23638823/microsoft-ethics-society-team-responsible-ai-layoffs.

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Patient Consensus v2.0

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Patient Consensus v2.0

Regulatory frameworks, independent process oversight, effective enforcement and punitive sanctions for misconduct

Consensus statements

•

Data should be used but not abused. Individuals and societies need to be

effectively protected against abuse; this will require new ethical frameworks for the use of data and AI, diverse risk mitigation strategies, effective enforcement and proactivity and agility in the approach.

•

It is evident that tech is not able to self-regulate, control therefore has

to occur at the level of laws and regulations. Citizens and patients need hard guardrails of what is permissible when it comes to data-use, real-time monitoring and effective enforcement, e.g. existential fines to ensure compliance. datasets can lead to grave errors and harm.

www.mpneurope.org

Patient Consensus v2.0

It has become evident that Big-Tech is

to default reactions, either systematic

unable to self-govern, e.g. in the recent

opt-out with the risk of insufficient

case of 23andMe . The community

contribution of data or systematic opt-

agreed that as ‘the horse had bolted’

in, exposing patients to risk. Further,

and the technology was already widely

consequences, in particular, long-

available, the only way forward was

term consequences, can be difficult to

appropriate regulation, independent

oversee and to judge as a lay person,

expert supervision of processes and

warranting independent expert oversight

effective enforcement of sufficiently

safe-guarding in particular, vulnerable

punitive up to existential sanctions to

populations such as patients.

1

the offending organisation to ensure compliance.

The community discussed that this independent oversight of processes to

Protections therefore have to be

ensure that data use, e.g. for AI, does not

enshrined in law and would ideally be

harm citizens needed to be ongoing as

based on global agreements and treaties

current- day ethics boards only provide

to ensure encompassing enforcement.

a single point assessment and are often

While patients and citizens will have the final say when it comes to sharing their

mainly concerned with mitigating risks to organisations.

data, the community felt that additional,

The community agreed that oversight

independent expert oversight was

needed to be balanced, fair and

needed to protect patients and citizens.

appropriate not to unduly deter

The community is concerned that the

progress- ‘Just saying NO all the time is

additional burden of consenting placed

no option’.

on patients who are already preoccupied with their personal disease might lead

1 As 23andMe Begins Its Death Spiral, Your Genetic Data is Up for Grabs | American Council on Science and Health. https://www.acsh.org/news/2024/10/10/23andmebegins-death-spiral-your-genetic-data-grabs-49043.

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Patient Consensus v2.0

Mitigation, proactivity and agility

Consensus statements •

Data use is an ethical imperative as people are dying and we as society

have data that could save them.

•

Data should be used but not abused. Individuals and societies need to be

effectively protected against abuse; this will require new ethical frameworks for the use of data and AI, diverse risk mitigation strategies and proactivity and agility in the approach.

•

We need proactive and inventive, not just defensive approaches to AI.

•

Regulatory and ethical frameworks are not keeping pace with current

technological development, we need more agile processes involving society at large.

•

Protected experimental environments and regulatory sandboxes, e.g. TEF

Health1, are one important tool to monitor for unintended consequences of AI technologies.

•

The combination of genomic/ molecular data and AI is impactful and a

topic of major importance in the near future; it brings the specific issues and tensions with regards to data ownership, risk, privacy and right of the individual and value extraction and business models to the point.

www.mpneurope.org

Patient Consensus v2.0

The community felt very strongly that in

needed to be anticipated with

the face of suffering, the use of data for

sufficient monitoring and e.g. protected

societal good was a moral imperative.

experimental environments and

At the same time, the community was

regulatory sandboxes.

concerned about the risks to patients themselves and in particular, their families. It also noted that the ‘horse had bolted’ and that society and regulators now needed to be faster, more innovative and agile to take a proactive, rather than reactive stance on, in particular, AI.

The community was particularly concerned about the combination of genomic data and AI as it brings the specific issues and tensions with regards to data ownership, risk, privacy and right of the individual, and value extraction and business models to the point.

The community felt that regulatory and ethical frameworks were lagging behind current developments and that a broader societal consensus was needed to make sure that new technologies aligned with societies’ needs and interests as well as personal rights.

The community agreed that even with best intent, unintended consequences of AI should be expected and therefore

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Patient Consensus v2.0

New models for data-dependent businesses and research

Consensus statements •

Current business and research models based on platforms holding

patient data, in particular genomic data, are problematic as they pose a security risk by design and undermine research as they incentivise proprietary behaviour, effectively locking away information that is valuable to patients and society and creating dispersed, non-interoperable data sets.

•

We need new business and research models that do not rely on the

selling of data or lock-in into non-compatible data platforms or systems; other industries and initiatives could provide interesting models for that.

•

Publicly-funded research should not only stipulate rules with regards to

data (e.g. FAIR principles) but also provide the necessary support and incentives for researchers, clinicians and institutions; contribution of high-quality datasets should be valued and recognised, e.g. in the form of ear-marked research funding and dedicated support.

The consensus meeting underscored the

to the current trend of ‘platformization’1

need for new models for business that

due to its negative impact on patients

relied on data as part of their business

and society in general.

model as participants strongly objected

1 Introduction to the special issue on Locating and theorising platform power. https://policyreview.info/pdf/policyreview-2024-2-1781.pdf.

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Patient Consensus v2.0

It was noted that a lot of research

the completion of the human genome

shared similar issues with regards to

project in 2003 that thanks to its open

data-dependency; the consensus scope

availability dramatically accelerated

was therefore extended to ‘new models

research across diverse fields.

for data-dependent businesses and research’.

This (platform-based approach) therefore not only slows but also biases scientific progress as data use and collection will be driven by commercial,

Problematic models

rather than societal interest and

Business and research models based on

the desire to slow down unwanted

platforms were considered problematic

competition. At the same time, the

due to 1. Their inherent security risk

provision of high-quality data including

due to data aggregation, in particular,

collection and curation needs to be

of genomic data 2. Their skewed

supported and appropriately rewarded

model for value extraction based on

and other industries, e.g. the royalty

platform ownership and access control

system of the music industry, could

rather than excellence 3. Their implicit

provide interesting models for that.

incentives for siloed, non-compatible datasets and lock-ins undermining progress and 4. The resulting risk of bias,

Wanted: viable, alternative business

further amplified by the use of AI, in

models

research findings, products and services.

At the same time, businesses intending not to sell e.g. genomic or other data

‘Competition on brain, not on access’

currently struggle to develop viable business models and to attract

Platform-based approaches effectively

investment, as e.g. also previously

lock valuable datasets away and limit

seen with the open-source platform

it to the capabilities, resources and

IMPUTE.ME1. There is therefore a great

personal interests of the commercial

need to come up with viable, alternative

or academic platform owner, instead

business models that do not rely on the

of leading to an open competitive

commercial exploitation of data itself.

scientific process as e.g. witnessed after

1 Folkersen, L. et al. Impute.me: An Open-Source, Non-profit Tool for Using Data From Direct-to-Consumer Genetic Testing to Calculate and Interpret Polygenic Risk Scores. Front. Genet. 11, 578 (2020). www.mpneurope.org

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Patient Consensus v2.0

Patient-led independent spaces for learning and debate to advance complex issues

Consensus statements •

We as patient community need self-directed settings for learning and

exchange with experts to analyse and discuss relevant complex topics to develop informed, independent positions in the sense of boundary integration.

Patients frequently observe that

That misconception is perpetuated

a diagnosis renders them 'lay per

by the fact that, despite the known

definition' in the eyes of other

heterogeneity of patient communities

stakeholders, irrespective of their

with regards to background, patient

personal professional background,

representatives are solely selected

experience and expertise.

based on their disease experience,

This creates two false dichotomies: a patient is not a lay person per definitiona cancer diagnosis does not remove a

not whether they possess the relevant technical expertise to meaningfully contribute to a given topic.

professional background. At the same time, any expert will only be an expert in their usually narrow subject of expertiseand a lay person for anything else concerned.

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Patient Consensus v2.0

In reality, we frequently witness a

The MPNE community believes that

process of boundary integration

creating these patient-led spaces for

between a person's disease experience

learning and debate are critical for

and professional background, similar to

patient advocates to integrate between

the phenomenon described between

their patient advocacy expertise and

work and private life .

diverse technical areas.

According to MPNE principle No

Meaningful and constructive positions

5 'it's a feature, not a bug', MPNE

need to be able to demonstrate how a

considers the heterogeneity of its

certain factor- policy, technical design,

network an opportunity and seeks

business model- affects patients and in

to actively leverage the diverse

return, translate patient prefences into

professional backgrounds, including for

appropriate and feasable solutions, e.g.

MPNEconsensus 2024.

trial designs.

The patient consensus on Data, AI and

The MPNE community does not only

data-dependent models for business and

see its patient-led spaces important

research was MPNE's second consensus

for its own community but also as an

meeting and the first time leveraged

opportunity for other stakeholders to

internal as well as external expertise.

experience a patient-defined framing of

1

It demonstrated yet again the value of

40

a given topic.

having a patient-led convening platform for the patient advocacy community as well as relevant experts around items itself.

1

Cobb, H. R., Murphy, L. D., Thomas, C. L., Katz, I. M. & Rudolph, C. W. Measuring boundaries and borders: A

taxonomy of work-nonwork boundary management scales. J. Vocat. Behav. 137, 103760 (2022).

It's a feature, not a bug. www.mpneurope.org

MPNE principle No 5

identified as relevant by the community

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Patient Consensus v2.0

conclusions Providing effective feed-back for

optimisation of treatment and care is

current European and national data

still needed to improve the Quality of

projects has been challenging for our

Life in those who survive.

communities. While initiatives tended to clearly articulate the political, economic and strategic interests of data sharing and in particular, the European Health Data space (EHDS), they failed to provide an equally compelling and concrete value proposition for individual patients and citizens. At the same time, altruism

Our community sees the EHDS as a critical enabler to generate better, faster and more equitable solutions for patient communities like ours. Tangibly improved patient outcomes would present one of the most concrete examples possible for how the EHDS created value for our societies.

was considered as sufficient motivator

As the realisation of that value

to gain individuals’ support, with

critically relies on individuals’ consent,

appeals to trust rather than effective

benefit/ risk assessments have to be

mitigation strategies against harm

positive at the level of the individual.

being in place.

This means that benefit needs to be

That omission appears rather careless, as thanks to the foresight of those who drafted the General Data Protection Regulation (GDPR), patients and citizens hold the final say on the use of their data, making consent granted at the level of the individual the critical ‘lynchpin’ to the success of the entire EHDS. For us as patient community, this comes as a major concern.

While survival in Melanoma has dramatically improved in the last decade, we still lose half of our patients with advanced cutaneous Melanoma and an even higher proportion of those suffering from rare Melanomas. At the same time,

www.mpneurope.org

tangible for the individual who also needs to feel protected from potential risk.

Patient Consensus v2.0

42

next steps The MPNE patient consensus version

2 options: to work through concrete,

2.0 will be published on MPNE’s issuu

existing data proposals and projects

channel and shared with the MPNE

to better understand the pros and

community and interested parties.

cons from a patient perspective

It is planned to to further build on and expand the separate sections of the MPNEconsensus 2024 for a future version 3.0, alternating between

and to further explore user-driven prototyping/ design of data-based applications, e.g. GILLYWEED 'Sync not Sink our data'1.

internal and external rounds of consultation and deliberation.

To move from abstract position to concrete solutions, we are exploring

Patients first.

MPNE principle No 1

1 Sync not Sink our data – we need more cancer patient agency in health data use. https://www.mpneurope.org/post/sync-not-sink-our-data-we-need-morecancer-patient-agency-in-health-data-use.

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Patient Consensus v2.0

www.mpneurope.org

Patient Consensus v2.0

annex

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Patient Consensus v2.0

MPNEconsensus 2024 program Wednesday, 31st January 2024 18:00

A Pint of Science with Andrew Evans on Large Language Models

Thursday, 1st February 9:00

Welcome and Introduction Bettina Ryll MD, PhD, Founder MPNE

9:30- 10:30

Oxford debate on “This house believes that patients should be compelled to give up their data for whatever purpose the state sees fit; if they wish to benefit from solidarity-based universal healthcare” Moderated by Robert White, MPNE

11.00-11.45

Data security and privacy.

Ariel Farkash, IBM and IToBos project partner

13.30-14.00

Dark Side of AI: Tricky poems and why patient advocates should care about Large Language Models Andrew Evans, MPNE

14:00 - 15:30 Overview and progress on TEF-Health (Testing and Experimentation Facility for Health AI and Robotics) Johanna Furuhjelm, TEF-Health 15:30 - 16:30 Sharing data is hard, sharing genomic data is….maybe not even a good idea?

Marie-Laure Yaspo, Max Planck Institute for Molecular Genetics and Alacris Theranostics 17:00 - 18:30 What is the ethical compass when we talk about health data in the pursuit of EthicalAI? Robin Renwick and Sarah Murray, Trilateral Research, IToBoS Project Partner

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Patient Consensus v2.0

Friday, 2nd February 9:00 - 10:30

Artificial Intelligence We Can Trust Sebastian Roland Lapuschkin, Fraunhofer Institute, iToBoS partner

11:00 - 12:30

Trust by Design in No-Trust Environments Philippe Page, The Human Colossus Foundation

13:30 - 15:00 MELCAYA: Healthcare System Implementation of New Technologies and AI Lukas Heinlein and David Krauss-Roskamm, German Cancer Research Center DKFZ and MELCAYA 15:30 - 17:00 Practical Session - Shaping AI for Melanoma Patients Facilitated by Philippe Page, The Human Colossus Foundation

Saturday, 3rd February 9:00 - 12:30

Review of the consensus statements Consolidation

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Patient Consensus v2.0

about Melanoma Patient Network Europe, MPNE, is a community of Melanoma patients, carers, and patient advocates focused on collaborative learning and sharing knowledge related to Melanoma. Their mission is to systematically address problems faced by the European Melanoma community in a constructive, result-oriented and evidence-based manner. MPNE collaborates to (1) effectively share knowledge relevant to Melanoma such as novel therapies, clinical trials, access schemes, research results, updates about regulatory and Health Technology Assessment decisions, (2) build capacity by sharing successful advocacy projects, knowledge and resources and support others to get started in Melanoma advocacy and (3) create a platform to interact as a group with other stakeholders and to serve as a neutral convenor. https://www.mpneurope.org/

Intelligent Total Body Scanner for Early Detection of Melanoma (iToBoS) is an AI-driven project funded under the EU Horizon 2020 research and innovation programme. Composed of a consortium of 20 partners, the project aims to develop a diagnostic platform for early detection of melanoma. It involves creating a total body scanner and a Computer Aided Diagnostics (CAD) tool that integrates various data sources like medical records and genomics data. This approach aims to provide highly personalised, early diagnoses by empowering healthcare practitioners with risk assessments for every mole. https://itobos.eu/

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Melanoma Patient Network Europe www.mpneurope.org