Marie-Christine Fritzsche

Technical University of Munich

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Alena Buyx

Technical University of Munich

Stuart McLennan

Technical University of Munich

Mónica Cano Abadía

Biobanking and Biomolecular Resources Research Infrastructure Consortium

Noussair Lazrak

Universitat de Barcelona

Wendy Rogers

Macquarie University

Antonio R. Porras

Colorado School of Public Health

Sandy Napel

Stanford University

Celia Cintas

IBM Research - Africa

Nora Hangel

Technical University of Munich

Martijn P.A. Starmans

Erasmus MC

Cooperative Institutions

Technical University of Munich

King's College London

Universitat de Barcelona

University of Cambridge

University College London

Imperial College London

Klinikum rechts der Isar

University of Oxford

University of California, San Francisco

Stanford University

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv (Cornell University) (2023)

Karim Lekadir Aasa Feragen Abdul Joseph Fofanah Alejandro F. Frangi Alena Buyx

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI.

Best practice

10.48550/arxiv.2309.12325

Cite

Citations (8)

FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare

BMJ (2025)

Karim Lekadir Alejandro F. Frangi Antonio R. Porras Ben Glocker Celia Cintas

Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. This paper describes the FUTURE-AI framework, which provides guidance for the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI Consortium was founded in 2021 and comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two year period, the FUTURE-AI guideline was established through consensus based on six guiding principles—fairness, universality, traceability, usability, robustness, and explainability. To operationalise trustworthy AI in healthcare, a set of 30 best practices were defined, addressing technical, clinical, socioethical, and legal dimensions. The recommendations cover the entire lifecycle of healthcare AI, from design, development, and validation to regulation, deployment, and monitoring.

Trustworthiness

Guideline

Consensus conference

10.1136/bmj-2024-081554

Cite

Citations (1)

Embedded Ethics in Practice: A Toolbox for Integrating the Analysis of Ethical and Social Issues into Healthcare AI Research

Science and Engineering Ethics (2024)

Theresa Willem Marie-Christine Fritzsche Bettina Zimmermann Anna Sierawska Svenja Breuer

Abstract Integrating artificial intelligence (AI) into critical domains such as healthcare holds immense promise. Nevertheless, significant challenges must be addressed to avoid harm, promote the well-being of individuals and societies, and ensure ethically sound and socially just technology development. Innovative approaches like Embedded Ethics, which refers to integrating ethics and social science into technology development based on interdisciplinary collaboration, are emerging to address issues of bias, transparency, misrepresentation, and more. This paper aims to develop this approach further to enable future projects to effectively deploy it. Based on the practical experience of using ethics and social science methodology in interdisciplinary AI-related healthcare consortia, this paper presents several methods that have proven helpful for embedding ethical and social science analysis and inquiry. They include (1) stakeholder analyses, (2) literature reviews, (3) ethnographic approaches, (4) peer-to-peer interviews, (5) focus groups, (6) interviews with affected groups and external stakeholders, (7) bias analyses, (8) workshops, and (9) interdisciplinary results dissemination. We believe that applying Embedded Ethics offers a pathway to stimulate reflexivity, proactively anticipate social and ethical concerns, and foster interdisciplinary inquiry into such concerns at every stage of technology development. This approach can help shape responsible, inclusive, and ethically aware technology innovation in healthcare and beyond.

Toolbox

Research Ethics

Reflexivity

10.1007/s11948-024-00523-y

Cite

Citations (0)

Requirements and expectations of high‐quality biomarkers for atopic dermatitis and psoriasis in 2021—a two‐round Delphi survey among international experts

Journal of the European Academy of Dermatology and Venereology (2022)

Stefanie Ziehfreund Linda Tizek Nora Hangel Marie-Christine Fritzsche S. Weidinger

Abstract Background Chronic inflammatory skin diseases such as atopic dermatitis (AD) and psoriasis (PSO) present major challenges in health care. Thus, biomarkers to identify disease trajectories and response to treatments to improve the lives of affected individuals warrant great research consideration. The requirements that these biomarkers must fulfil for use as practical clinical tools have not yet been adequately investigated. Aim To identify the core elements of high‐quality AD and PSO biomarkers to prepare recommendations for current biomarker research. Method A cross‐sectional two‐round Delphi survey was conducted from August to October 2019 and October to November 2020. All participants were members of the BIOMAP project, an EU‐funded consortium of clinicians, researchers, patient organizations and pharmaceutical industry partners. The first round consisted of three open‐ended questions. Responses were qualitatively analysed, and 26 closed statements were developed. For the second round, ‘agreement’ was assumed when the responses of ≥70% of the participants were ≥5 points on a 7‐point Likert scale for each statement. Priority classification was based on mean scores (<20th percentile = low, 20th to 60th percentile = medium, >60th percentile = high). Results Twenty‐one and twenty‐six individuals participated in rounds one and two, respectively. From 26 statements that were included in round 2, 18 achieved agreement (8 concerning the performance, 8 for the purpose and 2 on current obstacles). Seven statements were classified as high priority, e.g. those concerning reliability, clinical validity, a high positive predictive value, prediction of the therapeutic response and disease progression. Another seven statements were assigned medium priority, e.g. those about analytical validity, prediction of comorbidities and therapeutic algorithm. Low priority included four statements, like those concerning cost effectiveness and prediction of disease flares. Conclusion The core requirements that experts agreed on being essential for high‐quality AD and PSO biomarkers require rapid validation. Biomarkers can therefore be assessed based on these prioritized requirements.

Delphi Method

10.1111/jdv.18178

Cite

Citations (23)

You Can’t Have AI Both Ways: Balancing Health Data Privacy and Access Fairly

Frontiers in Genetics (2022)

Marieke Bak Vince I. Madai Marie-Christine Fritzsche Michaela Th. Mayrhofer Stuart McLennan

Artificial intelligence (AI) in healthcare promises to make healthcare safer, more accurate, and more cost-effective. Public and private actors have been investing significant amounts of resources into the field. However, to benefit from data-intensive medicine, particularly from AI technologies, one must first and foremost have access to data. It has been previously argued that the conventionally used "consent or anonymize approach" undermines data-intensive medicine, and worse, may ultimately harm patients. Yet, this is still a dominant approach in European countries and framed as an either-or choice. In this paper, we contrast the different data governance approaches in the EU and their advantages and disadvantages in the context of healthcare AI. We detail the ethical trade-offs inherent to data-intensive medicine, particularly the balancing of data privacy and data access, and the subsequent prioritization between AI and other effective health interventions. If countries wish to allocate resources to AI, they also need to make corresponding efforts to improve (secure) data access. We conclude that it is unethical to invest significant amounts of public funds into AI development whilst at the same time limiting data access through strict privacy measures, as this constitutes a waste of public resources. The "AI revolution" in healthcare can only realise its full potential if a fair, inclusive engagement process spells out the values underlying (trans) national data governance policies and their impact on AI development, and priorities are set accordingly.

Data governance

Data access

SAFER

10.3389/fgene.2022.929453

Cite

Citations (63)

Digital endpoints in clinical trials: emerging themes from a multi-stakeholder Knowledge Exchange event

Trials (2024)

Mia S. Tackney A. B. Steele Joseph Newman Marie-Christine Fritzsche Federica Lucivero

Abstract Background Digital technologies, such as wearable devices and smartphone applications (apps), can enable the decentralisation of clinical trials by measuring endpoints in people’s chosen locations rather than in traditional clinical settings. Digital endpoints can allow high-frequency and sensitive measurements of health outcomes compared to visit-based endpoints which provide an episodic snapshot of a person’s health. However, there are underexplored challenges in this emerging space that require interdisciplinary and cross-sector collaboration. A multi-stakeholder Knowledge Exchange event was organised to facilitate conversations across silos within this research ecosystem. Methods A survey was sent to an initial list of stakeholders to identify potential discussion topics. Additional stakeholders were identified through iterative discussions on perspectives that needed representation. Co-design meetings with attendees were held to discuss the scope, format and ethos of the event. The event itself featured a cross-disciplinary selection of talks, a panel discussion, small-group discussions facilitated via a rolling seating plan and audience participation via Slido. A transcript was generated from the day, which, together with the output from Slido, provided a record of the day’s discussions. Finally, meetings were held following the event to identify the key challenges for digital endpoints which emerged and reflections and recommendations for dissemination. Results Several challenges for digital endpoints were identified in the following areas: patient adherence and acceptability; algorithms and software for devices; design, analysis and conduct of clinical trials with digital endpoints; the environmental impact of digital endpoints; and the need for ongoing ethical support. Learnings taken for next generation events include the need to include additional stakeholder perspectives, such as those of funders and regulators, and the need for additional resources and facilitation to allow patient and public contributors to engage meaningfully during the event. Conclusions The event emphasised the importance of consortium building and highlighted the critical role that collaborative, multi-disciplinary, and cross-sector efforts play in driving innovation in research design and strategic partnership building moving forward. This necessitates enhanced recognition by funders to support multi-stakeholder projects with patient involvement, standardised terminology, and the utilisation of open-source software.

Digital Health

10.1186/s13063-024-08356-7

Cite

Citations (2)

Defining disease severity in atopic dermatitis and psoriasis for the application to biomarker research: an interdisciplinary perspective

British Journal of Dermatology (2024)

Ravi Ramessur Nick Dand Sinéad Langan Jake Saklatvala Marie-Christine Fritzsche

Abstract More severe atopic dermatitis and psoriasis are associated with a higher cumulative impact on quality of life, multimorbidity and healthcare costs. Proactive, early intervention in those most at risk of severe disease may reduce this cumulative burden and modify the disease trajectory to limit progression. The lack of reliable biomarkers for this at-risk group represents a barrier to such a paradigm shift in practice. To expedite discovery and validation, the BIOMarkers in Atopic Dermatitis and Psoriasis (BIOMAP) consortium (a large-scale European, interdisciplinary research initiative) has curated clinical and molecular data across diverse study designs and sources including cross-sectional and cohort studies (small-scale studies through to large multicentre registries), clinical trials, electronic health records and large-scale population-based biobanks. We map all dataset disease severity instruments and measures to three key domains (symptoms, inflammatory activity and disease course), and describe important codependencies and relationships across variables and domains. We prioritize definitions for more severe disease with reference to international consensus, reference standards and/or expert opinion. Key factors to consider when analysing datasets across these diverse study types include explicit early consideration of biomarker purpose and clinical context, candidate biomarkers associated with disease severity at a particular point in time and over time and how they are related, taking the stage of biomarker development into account when selecting disease severity measures for analyses, and validating biomarker associations with disease severity outcomes using both physician- and patient-reported measures and across domains. The outputs from this exercise will ensure coherence and focus across the BIOMAP consortium so that mechanistic insights and biomarkers are clinically relevant, patient-centric and more generalizable to current and future research efforts.

10.1093/bjd/ljae080

Cite

Citations (4)

The interrelation of scientific, ethical, and translational challenges for precision medicine with multimodal biomarkers – A qualitative expert interview study in dermatology research

Heliyon (2024)

Nora Hangel Alena Buyx Marie-Christine Fritzsche

Translational Medicine

Translational Research

10.1016/j.heliyon.2024.e31723

Cite

Citations (2)

Ethical layering in AI-driven polygenic risk scores—New complexities, new challenges

Frontiers in Genetics (2023)

Marie-Christine Fritzsche Kaya Akyüz Mónica Cano Abadía Stuart McLennan Pekka Marttinen

Researchers aim to develop polygenic risk scores as a tool to prevent and more effectively treat serious diseases, disorders and conditions such as breast cancer, type 2 diabetes mellitus and coronary heart disease. Recently, machine learning techniques, in particular deep neural networks, have been increasingly developed to create polygenic risk scores using electronic health records as well as genomic and other health data. While the use of artificial intelligence for polygenic risk scores may enable greater accuracy, performance and prediction, it also presents a range of increasingly complex ethical challenges. The ethical and social issues of many polygenic risk score applications in medicine have been widely discussed. However, in the literature and in practice, the ethical implications of their confluence with the use of artificial intelligence have not yet been sufficiently considered. Based on a comprehensive review of the existing literature, we argue that this stands in need of urgent consideration for research and subsequent translation into the clinical setting. Considering the many ethical layers involved, we will first give a brief overview of the development of artificial intelligence-driven polygenic risk scores, associated ethical and social implications, challenges in artificial intelligence ethics, and finally, explore potential complexities of polygenic risk scores driven by artificial intelligence. We point out emerging complexity regarding fairness, challenges in building trust, explaining and understanding artificial intelligence and polygenic risk scores as well as regulatory uncertainties and further challenges. We strongly advocate taking a proactive approach to embedding ethics in research and implementation processes for polygenic risk scores driven by artificial intelligence.

Polygenic risk score

10.3389/fgene.2023.1098439

Cite

Citations (12)