mibe000291 10.3205/mibe000291 urn:nbn:de:0183-mibe0002919 Research Article Building a digital platform for collaborative second opinions in rare disease: Integrating AI and healthcare networks for improved care Entwicklung einer kollaborativen digitalen Plattform für Zweitmeinungen bei seltenen Erkrankungen: Integration von KI und Gesundheitsnetzwerken für eine verbesserte Versorgung Lima Lima Vinícius V Dr.
Ribeirão Preto Medical School, University of São Paulo, BrazilRibeirão Preto Medical School, University of São Paulo, BrazilRISE-Health, Faculty of Medicine, University of Porto, Portugal
viniciuslima@alumni.usp.br author Bernardi Bernardi Filipe F
Ribeirão Preto Medical School, University of São Paulo, Brazil RISE-Health, Faculty of Medicine, University of Porto, Portugal
author Ferraz Ferraz Victor V
Ribeirão Preto Medical School, University of São Paulo, Brazil
author Alves Alves Domingos D
Ribeirão Preto Medical School, University of São Paulo, Brazil
author German Medical Science GMS Publishing House
Düsseldorf
 610 rare diseases second opinion digital health generative AI seltene Krankheiten Zweitmeinung digitale Gesundheit generative KI EFMI STC 2025 20251017 engl This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). 1860-9171 21 GMS Medizinische Informatik, Biometrie und Epidemiologie GMS Med Inform Biom Epidemiol 19 São Paulo Research Foundation (FAPESP) São Paulo Research Foundation (FAPESP) Die RD2O-Plattform ist eine digitale Lösung, die zur Unterstützung von Zweitmeinungen in der Versorgung bei seltenen Erkrankungen entwickelt wurde, insbesondere in Gesundheitssystemen, die durch regionale Ungleichheiten gekennzeichnet sind, wie beispielsweise in Brasilien. RD2O wurde im Rahmen von Aktionsforschung entwickelt und basiert auf einem soziotechnischen Ansatz. Die Plattform verbindet Allgemeinmediziner, Fachärzte und Patientenverbände über ein sicheres Webport</PlainText></TextGroup>al. &#220;ber die Plattform k&#246;nnen klinische und nicht-klinische Anfragen eingereicht werden, die durch generative KI-Modelle unterst&#252;tzt und von menschlichen Experten validiert werden. Vorl&#228;ufige Bewertungen zeigen, dass RD2O komplexe Falldiskussionen vereinfacht, die Genauigkeit von Zweitmeinungen verbessert und zu einer wachsenden, &#246;ffentlich zug&#228;nglichen Bildungsdatenbank beitr&#228;gt. Seine modulare Architektur integriert klinische Leitlinien, Datenschutzprotokolle und Telehealth-Anwendungen, wodurch es skalierbar und an andere nationale Kontexte anpassbar ist. RD2O gew&#228;hrleistet eine ethische, nutzerzentrierte und evidenzbasierte digitale Transformation im Gesundheitswesen, indem es Bewertungsmechanismen in jede Entwicklungsphase einbettet. Diese Plattform veranschaulicht, wie kollaborative, KI-gest&#252;tzte Systeme die Versorgung seltener Krankheiten st&#228;rken, die &#246;ffentliche Politik beeinflussen und validiertes Wissen durch einen sicheren, &#252;berpr&#252;fba<TextGroup><PlainText>r</PlainText></TextGroup>en Prozess generieren k&#246;nnen.</Pgraph></Abstract> <Abstract language="en" linked="yes"><Pgraph>The RD2O platform is a digital solution designed to support second opinions in rare disease care, particularly in healthcare systems marked by regional disparities, such as Brazil&#8217;s. Developed through action research and grounded in a sociotechnical approach, RD2O connects general practitioners, specialists, and patient associations via a secure web portal. The platform allows the submission of clinical and non-clinical queries, which are supported by generative AI models and validated by human experts. Preliminary evaluations demonstrate that RD2O streamlines complex case discussions, enhances the accuracy of second opinions, and contributes to a growing, publicly accessible educational database. Its modular architecture integrates clinical guidelines, privacy protocols, and telehealth tools, making it scalable and adaptable to other national contexts. RD2O ensures ethical, user-centered, and evidence-based digital health transformation by embedding evaluation mechanisms at every stage of development. This platform exemplifies how collaborative, AI-assisted systems can strengthen RD care, inform public policy, and generate validated knowledge through a secure, auditable process.</Pgraph></Abstract> <TextBlock name="Introduction" linked="yes"> <MainHeadline>Introduction</MainHeadline><Pgraph>Rare diseases (RDs) affect a small fraction of the population individually but collectively represent a significant global public health concern. With over 7,000 recognized rare diseases, most of them complex and chronically debilitating, patients often endure lengthy diagnostic odysseys, inappropriate treatments, and considerable emotional and financial burdens <TextLink reference="1"></TextLink>. In countries like Brazil, the situation is further complicated by stark regional disparities in healthcare access and the distribution of medical specialists <TextLink reference="2"></TextLink>.</Pgraph><Pgraph>Second opinions from specialized professionals are often critical to improving diagnostic precision, ensuring appropriate therapeutic approaches, and reducing the risk of unnecessary or harmful interventions <TextLink reference="3"></TextLink>.</Pgraph><Pgraph>This paper presents the Rare Disease Second Opinion (RD2O) platform, an evidence-based digital initiative developed in Brazil to support the delivery of second opinions for patients with rare diseases. Inspired by international models such as the Clinical Patient Management System (CPMS) <TextLink reference="4"></TextLink>, and EURACAN <TextLink reference="5"></TextLink>, RD2O brings an inclusive approach anchored in Brazil&#8217;s legal frameworks.</Pgraph></TextBlock> <TextBlock name="Methods" linked="yes"> <MainHeadline>Methods</MainHeadline><Pgraph>The RD2O platform was conceived using the action research methodology <TextLink reference="6"></TextLink>, which allows for continuous co-construction of knowledge and solution refinement in real-world contexts. This iterative and participatory process involved diverse stakeholders, including physicians, healthcare administrators, IT professionals, and researchers. The platform is under development in the Clinics Hospital of Ribeir&#227;o Preto, supported by the Brazilian National Rare Disease Network (RARAS) and the National Institute of Rare Diseases (INCT).</Pgraph><Pgraph>Stakeholder engagement was a central component of the design process. Multiple interactions were conducted to refine features and identify potential barriers to adoption. Healthcare professionals contributed insights into practical challenges, including the standardization of medical records and the need for context-specific clinical guidelines. Platform development was structured into the following phases: </Pgraph><Pgraph><OrderedList><ListItem level="1" levelPosition="1" numString="1.">Theoretical and empirical groundwork based on national and international experiences, including the Formative Second Opinion from the Brazilian Telehealth Program <TextLink reference="7"></TextLink>; </ListItem><ListItem level="1" levelPosition="2" numString="2.">Mapping user needs, legal requirements, and system limitations; </ListItem><ListItem level="1" levelPosition="3" numString="3.">Solution modeling using Business Process Model and Notation (BPMN) to define clinical workflows, content approval hierarchies, and user roles; and </ListItem><ListItem level="1" levelPosition="4" numString="4.">System database modeling, designing of interactive prototypes with FIGMA, and iterative testing.</ListItem></OrderedList></Pgraph><Pgraph>The technical architecture combines Python with MySQL and MongoDB to handle relational and document-based data. Generative AI models (e.g., ChatGPT, Gemini, and Llama) will automate drafts of responses and streamline query processing. Human experts review all AI-generated outputs to ensure clinical rigor.</Pgraph></TextBlock> <TextBlock name="Preliminary results" linked="yes"> <MainHeadline>Preliminary results</MainHeadline><Pgraph>Preliminary results from the first phase demonstrate the platform&#8217;s potential to streamline the process of obtaining second opinions for cases involving rare diseases. The RD2O web portal is proposed as a centralized access point. Through this interface, users will be able to: </Pgraph><Pgraph><OrderedList><ListItem level="1" levelPosition="1" numString="1.">submit detailed clinical cases for expert group discussion; </ListItem><ListItem level="1" levelPosition="2" numString="2.">submit non-clinical or administrative queries (e.g., disease classification, guideline development, or policy alignment); and </ListItem><ListItem level="1" levelPosition="3" numString="3.">access a public knowledge database of previously validated second opinions and clinical insights.</ListItem></OrderedList></Pgraph><Pgraph>Figure 1 <ImgLink imgNo="1" imgType="figure" /> presents the platform overview.</Pgraph><Pgraph>Each query submitted will be processed through the RD2O Admin System, where it will be classified and routed to the appropriate reviewers. AI models will support this process by generating draft responses or literature summaries, thereby reducing turnaround times. Case discussions and documentation will contribute to a continuously growing educational database, supporting the dissemination of knowledge. An AI audit trail is embedded to assess model reliability and track concordance between human expert responses and AI-generated content. Metrics for validation include time-to-response, clinical relevance scoring, and usability feedback from pilot users.</Pgraph></TextBlock> <TextBlock name="Final considerations" linked="yes"> <MainHeadline>Final considerations</MainHeadline><Pgraph>The development of the RD2O platform represents a pivotal step in improving the management and care of rare diseases in Brazil. The platform supports healthcare professionals with practical, validated insights and fosters a culture of continuous learning through its educational database. The modular architecture may support interop<TextGroup><PlainText>e</PlainText></TextGroup>rability with national systems. Sustainability is addressed through key partnerships (RARAS, INCT) and engagement in public policy.</Pgraph></TextBlock> <TextBlock name="Notes" linked="yes"> <MainHeadline>Notes</MainHeadline><SubHeadline>Acknowledgments</SubHeadline><Pgraph>The S&#227;o Paulo Research Foundation (FAPESP) supports this project (grant no. 2023&#47;10203-8 and 2024&#47;22679-0).</Pgraph><SubHeadline>Competing interests</SubHeadline><Pgraph>The authors declare that they have no competing interests.</Pgraph></TextBlock> <References linked="yes"> <Reference refNo="1"> <RefAuthor>Marwaha S</RefAuthor> <RefAuthor>Knowles JW</RefAuthor> <RefAuthor>Ashley EA</RefAuthor> <RefTitle>A guide for the diagnosis of rare and undiagnosed disease: beyond the exome</RefTitle> <RefYear>2022</RefYear> <RefJournal>Genome Med</RefJournal> <RefPage>23</RefPage> <RefTotal>Marwaha S, Knowles JW, Ashley EA. A guide for the diagnosis of rare and undiagnosed disease: beyond the exome. Genome Med. 2022 Feb 28;14(1):23. 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