{"id":2686,"date":"2025-06-05T08:11:57","date_gmt":"2025-06-05T08:11:57","guid":{"rendered":"https:\/\/digital-health-space.ch\/?page_id=2686"},"modified":"2025-08-19T14:06:55","modified_gmt":"2025-08-19T14:06:55","slug":"use-case-digital-health-interventions-for-cancer-patients","status":"publish","type":"page","link":"https:\/\/digital-health-space.ch\/en\/use-case-digital-health-interventions-for-cancer-patients","title":{"rendered":"Use Case: Digital Health Interventions for Cancer Patients"},"content":{"rendered":"
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AI to Expedite Sex\/Gender Analysis in Evidence Synthesis (AI-SGES)<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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A use case of digital health interventions for cancer patients<\/font><\/div><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Why Sex and Gender-based Analysis in Evidence Synthesis Matters<\/span><\/b><\/span><\/p>\t\t\t\t<\/span>\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Sex and gender are often overlooked in evidence synthesis. Our general aim is to accelerate the integration of sex\/gender analysis within evidence synthesis by using AI.<\/p>

Evidence synthesis is the systematic process of identifying, appraising, and integrating findings from research to answer a specific question. It supports decision-making by summarizing large bodies of evidence. Common methods include systematic reviews and clinical practice guidelines.<\/p>

Sex and gender can influence health risks and treatment responses. Their systematic consideration in evidence synthesis can reveal outcome differences, reduce bias, and help address health inequities that might otherwise be overlooked. This improves the accuracy, relevance, and applicability of the evidence, ultimately supporting better healthcare decisions for diverse populations.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Using AI in Evidence Synthesis: Opportunities and Risks<\/span><\/b><\/span><\/p>\t\t\t\t<\/span>\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Despite its importance, sex\/gender analysis is still limited in systematic reviews and clinical guidelines. Artificial intelligence (AI) can automate labour-intensive tasks like selecting studies and extracting data, which can speed up evidence synthesis. However, if not carefully implemented and evaluated, AI may reinforce existing sex\/gender biases and lead to inequitable conclusions.<\/p>

There is a need to evaluate the performance of AI for accelerating the consideration of sex\/gender in evidence synthesis.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Project Aims<\/span><\/b><\/span>
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This project aims to accelerate the integration of sex\/gender analysis within evidence synthesis by using AI. We will tackle the following specific objectives.<\/p>

  1. To map existing AI tools used to speed up systematic reviews and outline how they can support sex\/gender analysis.<\/li>
  2. To evaluate how sex, gender, and equity issues are handled in clinical guidelines for digital health tools for cancer.<\/li>
  3. To evaluate AI tools and large language models like ChatGPT to see how well they help systematic reviewers identify and assess sex\/gender information in research.<\/li>
  4. To develop tools, including an AI framework and a custom GPT, to help integrate sex and gender analysis into evidence synthesis efficiently, transparently, and without amplifying bias.<\/li><\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    Methodological Approach<\/a><\/span><\/b><\/span><\/p>\t\t\t\t<\/span>\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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    Three year mixed-methods project using scoping reviews, diagnostic accuracy studies, agreement studies, surveys, and consensus methods. The engagement of the key stakeholders, including healthcare professionals and patients, will be integral to this project from inception to conclusion.<\/p>

    \u00a0<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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    Funding<\/b><\/font><\/span><\/span><\/p>\t\t\t\t<\/span>\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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    Project funded by the Swiss National Science Foundation (SNSF) under the \u00a0National Research Programme (NRP) 83, Gender Medicine and Health<\/a>.<\/p>

    Project protocols at Open Science Framework: https:\/\/osf.io\/qp2er\/<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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    Project Team<\/b><\/font><\/span><\/span><\/p>\t\t\t\t<\/span>\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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    Project Applicants<\/strong><\/p>

    Claudia M. Witt \u2013 University Hospital Zurich, and University of Zurich<\/p>

    Janna Hastings \u2013 University of Zurich<\/p>

    Jes\u00fas L\u00f3pez-Alcalde \u2013 University Hospital Zurich, and University of Zurich<\/p>

    Project Partners <\/strong><\/p>

    Javier Zamora \u2013 Hospital Universitario Ram\u00f3n y Cajal, IRYCIS, CIBERESP (Spain)<\/p>

    Gabriel Rada \u2013Epistemonikos Foundation (Chile)<\/p>

    Jennifer Petkovic \u2013 Campbell and Cochrane Health Equity Thematic Group, the MuSE Consortium, Bruyere Research Institute, University of Ottawa (Canada)<\/p>

    Gemma Villanueva \u2013 Cochrane Response (UK)<\/p>

    Project Information<\/strong>: https:\/\/data.snf.ch\/grants\/grant\/227036<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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    Use Case: Multiple Sclerosis<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/a>\n\t\t\n\t\t\t\t
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    Evidence Based
    Medicine & AI <\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"

    AI to Expedite Sex\/Gender Analysis in Evidence Synthesis (AI-SGES) A use case of digital health interventions for cancer patients Why Sex and Gender-based Analysis in Evidence Synthesis Matters Sex and gender are often overlooked in evidence synthesis. Our general aim is to accelerate the integration of sex\/gender analysis within evidence synthesis by using AI. Evidence […]<\/p>","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"class_list":["post-2686","page","type-page","status-publish","hentry"],"acf":[],"yoast_head":"\nKomplement\u00e4re Therapien bei MS und Evidence and Gap Map | Uni Z\u00fcrich<\/title>\n<meta name=\"description\" content=\"Das PEMS-Projekt verbessert Forschung zu MS und komplement\u00e4ren Therapien. 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