Grandes Desafios da Computação Aplicada à Saúde

Adebayo, Julius et al. (2018). “Sanity checks for saliency maps”. In: Advances in neural information processing systems 31.

Agência Nacional de Vigilância Sanitária - Anvisa (2020) “Princípios e práticas de cibersegurança em dispositivos médicos”. Guia nº 38/2020, versão 1, de 14/09/2020. Brasília: Anvisa.

Agência Nacional de Vigilância Sanitária - Anvisa (2025) “Manual para regularização de equipamento médico e software como dispositivo médico na Anvisa”. Versão 1.3. Gerência de Tecnologia em Equipamentos Médicos (GQUIP). Brasília: Anvisa.

Al Khatib, H. S., Neupane, S., Kumar Manchukonda, H., Golilarz, N. A., Mittal, S., Amirlatifi, A., and Rahimi, S. (2024). Patient-centric knowledge graphs: a survey of current methods, challenges, and applications. Frontiers in Artificial Intelligence, 7:1388479.

ALDERMAN, J. E. et al. Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations. Lancet Digital Health, v. 7, n. 1, p. e64–e88, jan. 2025.

Almeida, G. C., Santos, A. C. N., Soares, C. L. A., Pinto, P. C. A., Dal Bello, F. S., Boechat, Y. E. M., Seixas, F. L., dos Santos, A. A. S. M. D., Mesquita, C. T., Mesquita, E. T., Muchaluat-Saade, D. C., and Fernandes, N. C. (2024). Nova geração da telessaúde: Oportunidades, tendências e desafios. In Anais SBC.

Alsaedi, S., Ogasawara, M., Alarawi, M., Gao, X., and Gojobori, T. (2025). Ai-powered precision medicine: utilizing genetic risk factor optimization to revolutionize healthcare. NAR Genomics and Bioinformatics, 7(2):lqaf038.

Amazon.com, Inc. (2024). Amazon Alexa. Seattle, WA. Assistente virtual baseada em nuvem.

ARIYO, O. Bridging the AI divide: Building Africa's future in the age of intelligent machines. Medium, out. 2025. Disponível em: [link]. Acesso em: 27 mar. 2026.

Astorga, M., Cruz-Sandoval, D., and Favela, J. (2023). A social robot to assist in addressing disruptive eating behaviors by people with dementia. Robotics, 12(1):29.

Bal, F., Tekerek, M., Palacz, M., Gök, M., and ¸Simşir, R. (2024). Human robot interaction with social humanoid robots. El-Cezeri, 11(1):94–102.

Barbosa, J. M. L. and Gottschalg-Duque, C. (2026). Um modelo de arquitetura da informação para otimização do fluxo informacional no suporte ao usuário das plataformas sus digital e rede nacional de dados em saúde (rnds). Encontros Bibli, 31:e105200.

Bin, Y., , et al. (2025). Mental health professionals’ attitudes towards virtual reality therapies: A systematic review. Journal of Technology in Behavioral Science.

Bischoff, R. and Graefe, V. (2002). Dependable multimodal communication and interaction with robotic assistants. In Proceedings. 11th IEEE International Workshop on Robot and Human Interactive Communication, pages 300–305. IEEE.

Bommasani, Rishi et al. (2021). “On the opportunities and risks of foundation models”. In: arXiv preprint arXiv:2108.07258.

Borisov, V., Leemann, T., Seßler, K., Haug, J., Pawelczyk, M., and Kasneci, G. (2024). Deep neural networks and tabular data: A survey. IEEE Transactions on Neural Networks and Learning Systems, 35(6):7499–7519.

Bossa, S., Ribeiro, B., Santos, G., Gomes, L., and Vale, Z. (2026). Peak-acl: A fipa-compliant agent communication package for python-based multi-agent systems. SoftwareX, 34:102647.

Bradwell, H. L., Noury, G. E. A., Edwards, K. J., Winnington, R., Thill, S., and Jones, R. B. (2021). Design recommendations for socially assistive robots for health and social care based on a large scale analysis of stakeholder positions: Social robot design recommendations. Health Policy and Technology, 10(3):100544.

Brasil (2018) “Lei nº 13.709, de 14 de agosto de 2018. Lei Geral de Proteção de Dados Pessoais (LGPD)”. Brasília: Presidência da República.

Brasil (2018). Lei nº 13.709, de 14 de agosto de 2018 (Lei Geral de Proteção de Dados Pessoais – LGPD). [link]. Access in: 23 fev. 2026.

Brasil (2018). Lei nº 13.709, de 14 de agosto de 2018. Lei Geral de Proteção de Dados Pessoais (LGPD). Diário Oficial da União, Brasília.

BRASIL. Lei nº 13.709, de 14 de agosto de 2018. Lei Geral de Proteção de Dados Pessoais (LGPD). Brasília: Presidência da República, 2018. Disponível em: [link].

Brasil. Ministério da Saúde (2020) “Estratégia de Saúde Digital para o Brasil 2020-2028”. Brasília: Ministério da Saúde.

Buzuti, Lucas et al. (2024). “Generative AI for neonatal pain assessment: a sound approach to improve data-driven automatic recognition in intensive care unit”. In: Available at SSRN 4994076.

Bycroft, C., Freeman, C., Petkova, D., Band, G., Elliott, L. T., Sharp, K., Motyer, A., Vukcevic, D., Delaneau, O., O’onnell, J., et al. (2018). The uk biobank resource with deep phenotyping and genomic data. Nature, 562(7726):203–209.

Campos, J. M., Gomes, R. M., Chaves, D. T., Meira Jr., W., Cherchiglia, M. L., Rocha, H. A., Rocha, L., and Gonçalves, M. A. (2026). Mechanistic dynamic interpretability for tabular foundation models in healthcare. In XAI World Conference 2026. Accepted paper.

CARROLL, S. R. et al. The CARE Principles for Indigenous Data Governance. Data Science Journal, v. 19, n. 1, p. 43, 2020. DOI: 10.5334/dsj-2020-043.

CELI, L. A. et al. Sources of bias in artificial intelligence that perpetuate healthcare disparities. A global review. PLOS Digital Health, v. 1, n. 3, e0000022, 2022. DOI: 10.1371/journal.pdig.0000022.

Chen, Y., Xi, Z., Saunders, R., Simmons, D., Totsika, V., and Mandy, W. (2024). A systematic review and meta-analysis of the relationship between sensory processing differences and internalising/externalising problems in autism. Clinical Psychology Review, 114:102516.

COIMBRA JR., C. E. A.; SANTOS, R. V. Saúde e povos indígenas no Brasil: reflexões a partir do I Inquérito Nacional de Saúde e Nutrição Indígena. Cadernos de Saúde Pública, v. 28, n. 4, p. 641–644, 2012.

Comitê Gestor da Internet no Brasil (2025). Tic saúde 2024: Pesquisa sobre o uso das tecnologias de informação e comunicação nos estabelecimentos de saúde brasileiros. Technical report, CGI.br, São Paulo.

Conselho Nacional de Saúde (2012). Resolução nº 466, de 12 de dezembro de 2012. [link]. Access in: 23 fev. 2026.

Craig, F., Tenuta, F., De Giacomo, A., Trabacca, A., and Costabile, A. (2021). A systematic review of problematic video-game use in people with autism spectrum disorders. Research in Autism Spectrum Disorders, page 101726.

Cruz-Sandoval, D. and Favela, J. (2019). A Conversational Robot to Conduct Therapeutic Interventions for Dementia. IEEE Pervasive Computing, 18(2):10–19.

Cui, T., Ariffin, R., Wang, X., , et al. (2026). Effects of virtual reality exercise on social skills and emotional recognition among children with autism spectrum disorder: a meta-analysis of randomized controlled trials. BMC Psychology, 14:387.

Čyras, K., Rago, A., Albini, E., Baroni, P., and Toni, F. (2021). Argumentative XAI: A survey. Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI), pages 4392–4399.

da Silva Vanin, F. N., Melo, B., da Rosa Righi, R., da Costa, C. A., & Antunes, R. S. (2025). Enhancing Health Data Integrity Through On-Chain Verification and Cryptographic Proofs. Blockchain: Research and Applications, 100373.

da Silva, P. L. P., Cecim, J. V. C., da Rocha, M. M., and Muchaluat-Saade, D. C. (2024). Jogos sérios para auxiliar o exercício cognitivo de idosos utilizando o robô social eva. In Simpósio Brasileiro de Sistemas Multimídia e Web (Web-Media), pages 95–98. SBC.

Davis, K., Iosif, A.-M., Nordahl, C. W., Solomon, M., and Krug, M. K. (2023). Video game use, aggression, and social impairment in adolescents with autism spectrum disorder. Journal of Autism and Developmental Disorders, 53:3567–3580.

de Carvalho, A. P., Braz, C. S., and Prates, R. O. (2024). An analysis of the evaluation methods being applied to serious games for autistic children. Journal on Interactive Systems, 15(1):55–78.

de Mello, B. H., Rigo, S. J., da Costa, C. A., Righi, R. d. R., Donida, B., Bez, M. R., and Schunke, L. C. (2022). Semantic interoperability in health records standards: a systematic literature review. Health and Technology, 12(2):255–272.

de Oliveira, T. C. and Lopes-Cendes, I. (2025). Population molecular genetics in brazil: From genomic databases and research to the implementation of precision medicine. Journal of Community Genetics, 16(4):409–420.

Douglas, A. O., Senkaiahliyan, S., Bulstra, C. A., Mita, C., Reddy, C. L., and Atun, R. (2025). Global adoption of value-based health care initiatives within health systems: a scoping review. In JAMA Health Forum, volume 6, pages e250746–e250746. American Medical Association.

Dupuis, A., Mudiyanselage, P., Burton, C. L., Arnold, P. D., Crosbie, J., and Schachar, R. J. (2022). Hyperfocus or flow? attentional strengths in autism spectrum disorder. Frontiers in Psychiatry, 13:886692.

Dwyer, P., Williams, Z. J., Lawson, W. B., and Rivera, S. M. (2024). A trans-diagnostic investigation of attention, hyper-focus, and monotropism in autism, attention dysregulation hyperactivity development, and the general population. SAGE Open Medicine.

Dzhoroev, T., Park, H., Lee, J., Kim, B., and Lee, H. S. (2023). Human perception on social robot’s face and color expression using computational emotion model. In 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pages 2484–2491. IEEE.

European Commission (2022). Proposal for a regulation on the european health data space. [link].

Fachantidis, N., Syriopoulou-Delli, C. K., and Zygopoulou, M. (2020). The Effectiveness of Socially Assistive Robotics in Children With Autism Spectrum Disorder. International Journal of Developmental Disabilities, 66(2):113–121.

Fernandes, M. E., & Nuzzi, A. P. E. (2022). Fundamentos da Lei Geral de Proteção de Dados (LGPD): uma revisão narrativa. Research, Society and Development, 11(12), e310111234247-e310111234247.

Ferreira, Leonardo Antunes et al. (2025). “Disclosing neonatal pain in real-time: AIderived pain sign from continuous assessment of facial expressions”. In: Computers in Biology and Medicine 189, p. 109908.

FIELDS, C. T. et al. Governance for anti-racist AI in healthcare: integrating racism-related stress in psychiatric algorithms for Black Americans. Frontiers in Digital Health, v. 7, art. 1492736, 2025. DOI: 10.3389/fdgth.2025.1492736.

FIGUEIREDO, B. S. et al. Mortalidade materna no Brasil (2020–2024): distribuição macrorregional, causas diretas e indiretas. Revista JRG de Estudos Acadêmicos, 2025. DOI: 10.55892/jrg.v8i18.2423.

Gabrielli, S., Cristofolini, M., Dianti, M., , et al. (2023). Co-design of a virtual reality multiplayer adventure game for adolescents with autism spectrum disorder: Mixed methods study. JMIR Serious Games, 11:e51719.

Gama, J., ˇ Zliobait˙e, I., Bifet, A., Pechenizkiy, M., and Bouchachia, A. (2014). A survey on concept drift adaptation. ACM computing surveys (CSUR), 46(4):1–37.

Gao, J., , et al. (2025). The effect of game-based interventions on children and adolescents with autism spectrum disorder: A systematic review and metaanalysis. Frontiers in Pediatrics.

Gao, Y., Sharma, T., and Cui, Y. (2023). Addressing the challenge of biomedical data inequality: An artificial intelligence perspective. Annual Review of Biomedical Data Science, 6(Volume 6, 2023):153–171.

Geifman, Yonatan and Ran El-Yaniv (2017). “Selective classification for deep neural networks”. In: Advances in neural information processing systems 30.

Ghassemi, Marzyeh, Luke Oakden-Rayner, and Andrew L Beam (2021). “The false hope of current approaches to explainable artificial intelligence in health care”. In: The lancet digital health 3.11, e745–e750.

Ghebrehiwet, I., Zaki, N., Damseh, R., and Mohamad, M. S. (2024). Revolutionizing personalized medicine with generative ai: a systematic review. Artificial Intelligence Review, 57(5):128.

Gorishniy, Y., Rubachev, I., Khrulkov, V., and Babenko, A. (2021). Revisiting deep learning models for tabular data. In Proceedings of the 35th International Conference on Neural Information Processing Systems, NIPS ’21.

Grinsztajn, L., Oyallon, E., and Varoquaux, G. (2022). Why do tree-based models still outperform deep learning on typical tabular data? In Proceedings of the 36th International Conference on Neural Information Processing Systems, NIPS ’22.

Grunau, Ruth Eckstein (2013). “Neonatal pain in very preterm infants: long-term effects on brain, neurodevelopment and pain reactivity”. In: Rambam Maimonides medical journal 4.4, e0025.

Grunau, Ruth VE and Kenneth D Craig (1987). “Pain expression in neonates: facial action and cry”. In: Pain 28.3, pp. 395–410.

Guerra Junior, A. A., Pereira, R. G., Gurgel, E. I., Cherchiglia, M., Dias, L. V., Ávila, J. D., Santos, N., Reis, A., Acurcio, F. A., andMeira Junior, W. (2018). Building the national database of health centred on the individual: Administrative and epidemiological record linkage - brazil, 2000-2015. International Journal of Population Data Science, 3(1):446.

Guo, Chuan et al. (2017). “On calibration of modern neural networks”. In: International conference on machine learning. PMLR, pp. 1321–1330.

Gyrard, A. and et al. (2025). Lessons learned from european health data projects with cancer use cases: Implementation of health standards and internet of things semantic interoperability. J Med Internet Res, 27:e66273.

Hasanzadeh, F., Josephson, C. B., Waters, G., Adedinsewo, D., Azizi, Z., and White, J. A. (2025). Bias recognition and mitigation strategies in artificial intelligence healthcare applications. NPJ Digital Medicine, 8(1):154.

Hedqvist, A.-T., Praetorius, G., Ekstedt, M., and Lindberg, C. (2025). Entangled in complexity: An ethnographic study of organizational adaptability and safe care transitions for patients with complex care needs. Journal of Advanced Nursing, 81(9):5528–5545.

Heiderich, Tatiany M. et al. (2023). “Face-based automatic pain assessment: challenges and perspectives in neonatal intensive care units”. In: Jornal de Pediatria 99.6, pp. 546–560. ISSN: 0021-7557.

Heydari, Soroush and Qusay H Mahmoud (2025). “Tiny machine learning and on-device inference: A survey of applications, challenges, and future directions”. In: Sensors 25.10, p. 3191.

HL7 International (2023). FHIR Release 5: Fast Healthcare Interoperability Resources. Health Level Seven International, Ann Arbor, MI. Disponível em: [link].

Hollmann, N., Müller, S., Eggensperger, K., and Hutter, F. (2025). Accurate predictions on small data with a tabular foundation model. Nature, 637(8045):319–326.

Hornback, A., Marteau, B., Tan, S. Q., Kim, K., Patil, O., Traynelis, J., ... & Wang, M. D. (2025). FHIR in Focus: Enabling Biomedical Data Harmonization for Intelligent Healthcare Systems. IEEE Reviews in Biomedical Engineering.

IBGE INSTITUTO BRASILEIRO DE GEOGRAFIA E ESTATÍSTICA. Censo Demográfico 2022: Quilombolas e Indígenas, por sexo e idade, segundo recortes territoriais específicos: Resultados do universo. Rio de Janeiro: IBGE, 3 maio 2024.Disponível em: [link]

IBGE INSTITUTO BRASILEIRO DE GEOGRAFIA E ESTATÍSTICA. Censo Demográfico 2022: Quilombolas: Primeiros Resultados. Rio de Janeiro: IBGE, 27 jul. 2023. Dipsonível em: [link]

IEPS INSTITUTO DE ESTUDOS PARA POLÍTICAS DE SAÚDE. Saúde dos Povos Indígenas e Quilombolas no Brasil: Relatório Técnico nº 1/2023. São Paulo: IEPS, jun. 2023. Disponível em: [link]

Ito, M., Carvalho, D. R., and Moro, C. (2023). Mineração de processos no apoio à saúde: Perspectiva clínica e de gestão. Computação Brasil, (49):25–28.

Jennings, N. R. (2001). An agent-based approach for building complex software systems. Communications of the ACM, 44(4):35–41.

Jiang, J.-P., Liu, S.-Y., Cai, H.-R., Zhou, Q.-L., and Ye, H.-J. (2025). Representation Learning for Tabular Data: A Comprehensive Survey . IEEE Transactions on Pattern Analysis & Machine Intelligence, (01):1–20.

Jiménez-Muñoz, L., Peñuelas-Calvo, I., Calvo-Rivera, P., Díaz-Oliván, I., Moreno, M., Baca-García, E., and Porras-Segovia, A. (2022). Video games for the treatment of autism spectrum disorder: A systematic review. Journal of Autism and Developmental Disorders, 52(1):169–188.

JOSEPH, J. Algorithmic bias in public health AI: a silent threat to equity in low-resource settings. Frontiers in Public Health, v. 13, art. 1643180, 23 jul. 2025. DOI: 10.3389/fpubh.2025.1643180.

Jung, H. W., Park, J. Y., Holoubek, T., Kim, W. J., and Park, J. (2025). Socially assistive robots in mental healthcare: principles and conceptual framework for user-centered design. International Journal of Social Robotics, 17(11):2827–2851.

Karami, V., Yaffe, M. J., Gore, G., Moon, A., and Rahimi, S. A. (2024). Socially assistive robots for patients with alzheimer’s disease: a scoping review. Archives of gerontology and geriatrics, 123:105409.

Kendall, Alex and Yarin Gal (2017). “What uncertainties do we need in bayesian deep learning for computer vision?” In: Advances in neural information processing systems 30.

Khan, A. M., Işık, Esra Büşra, and Tan, T. W. (2024). A global initiative on addressing bioinformatics’ grand challenges. Briefings in Bioinformatics, 25(4):bbae278.

Kientz, J. A., Goodwin, M. S., Hayes, G. R., and Abowd, G. D. (2013). Interactive technologies for autism. Synthesis lectures on assistive, rehabilitative, and health-preserving technologies, 2(2):1–177.

Kim, S.-I., , et al. (2024). Promoting self-efficacy of individuals with autism in practicing social skills in the workplace using virtual reality and physiological sensors: Mixed methods study. JMIR Formative Research, 8:e52157.

Koh, W. Q., Felding, S. A., Toomey, E., and Casey, D. (2021). Barriers and facilitators to the implementation of social robots for older adults and people with dementia: a scoping review protocol. Systematic reviews, 10(1):49.

Kokorelias, K. M., Shiers-Hanley, J. E., Rios, J., Knoepfli, A., and Hitzig, S. L. (2021). Factors influencing the implementation of patient navigation programs for adults with complex needs: a scoping review of the literature. Health Services Insights, 14:11786329211033267.

Kouroupa, A., Laws, K. R., Irvine, K., Mengoni, S. E., Baird, A., and Sharma, S. (2022). The use of social robots with children and young people on the autism spectrum: A systematic review and meta-analysis. Plos one, 17(6):e0269800.

Kuziemsky, C., Maeder, A. J., John, O., Gogia, S. B., Basu, A., Meher, S., and Ito, M. (2019). Role of artificial intelligence within the telehealth domain. Yearbook of medical informatics, 28(01):035–040.

Lahiri, U., Bekele, E., Dohrmann, E., Warren, Z., and Sarkar, N. (2015). A physiologically informed virtual reality based social communication system for individuals with autism. Journal of Autism and Developmental Disorders, 45:919–931.

Landowska, A. and Robins, B. (2020). Robot eye perspective in perceiving facial expressions in interaction with children with autism. In Workshops of the International Conference on Advanced Information Networking and Applications, pages 1287–1297. Springer.

Laurent, S., Maller, B., and Rossi, C. (2026). Understanding patient clinician interaction patterns in telemedicine based consultations. Qriset Indonesia Journal of Medical Science, 1(1):33–41.

Lee, J., Takehashi, H., Nagai, C., Obinata, G., and Stefanov, D. (2012). Which robot features can stimulate better responses from children with autism in robotassisted therapy? International Journal of Advanced Robotic Systems, 9.

Li, K., Lohachab, A., Dumontier, M., and Urovi, V. (2025). Privacy preservation in blockchain-based healthcare data sharing: A systematic review. Peer-to-Peer Networking and Applications.

Lima, R. A. G. (2025). Uma arquitetura distribuída de gerenciamento de dados em saúde inteligente baseada em interoperabilidade e auditoria.

Llerena, Amelia et al. (2023). “Neonatal pain assessment: Do we have the right tools?” In: Frontiers in pediatrics 10, p. 1022751.

Lorenzo, G. G., Newbutt, N. N., and Lorenzo-Lled’o, A. A. (2023). Designing virtual reality tools for students with autism spectrum disorder: A systematic review. Education and Information Technologies, 28:9557–9605.

Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., and Zhang, G. (2019). Learning under concept drift: A review. IEEE Transactions on Knowledge and Data Engineering, 31(12):2346–2363.

Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS’17, page 4768–4777.

Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.-I. (2020). From local explanations to global understanding with explainable ai for trees. Nature machine intelligence, 2(1):56–67.

Macis, D., Perilli, S., and Gena, C. (2023). Employing socially assistive robots in elderly care (longer version). arXiv preprint arXiv:2304.14944.

Maddalon, L., , et al. (2024). Exploring adaptive virtual reality systems used in interventions for children with autism spectrum disorder: Systematic review. Journal of Medical Internet Research, page e57093.

Maida, E., Caruso, P., Bonavita, S., Abbadessa, G., Miele, G., Longo, M., Scappaticcio, L., Ruocco, E., Trojsi, F., Esposito, K., et al. (2025). Digital health in diabetes care: A narrative review from monitoring to the management of systemic and neurologic complications. Journal of Clinical Medicine, 14(12):4240.

Malihi, M., Nguyen, J., Cardy, J. O., Eldon, S., Petta, C., and Kushki, A. (2020). Short report: Evaluating the safety and usability of head-mounted virtual reality compared to monitor-displayed video for social skill training in children with autism. Autism, 24(7):1924–1929.

Manca, M., Paternò, F., Santoro, C., Zedda, E., Braschi, C., Franco, R., and Sale, A. (2021). The impact of serious games with humanoid robots on mild cognitive impairment older adults. International Journal of Human-Computer Studies, 145:102509.

Marcolino, M. S., Schettini, I., do Nascimento, G., da Rocha, L., Lana, F., and Gonçalves, M. A. (2025). Explainable artificial intelligence for predicting cardiovascular events in hospitalised covid-19 patients. BMC Infectious Diseases, 25(1):1569.

Maun, R., Fabri, M., and Trevorrow, P. (2024). Participatory methods to engage autistic people in the design of digital technology: A systematic literature review. Journal of Autism and Developmental Disorders, 54(8):2960–2971.

Maure, R. and Bruno, B. (2025). Autonomy in socially assistive robotics: a systematic review. Frontiers in Robotics and AI, 12:1586473.

McCleery, J. P., Zitter, A., Sol’orzano, R., Turnacioglu, S., Miller, J. S., Ravindran, V., and Parish-Morris, J. (2020). Safety and feasibility of an immersive virtual reality intervention program for teaching police interaction skills to adolescents and adults with autism. Autism Research, pages 1–7.

Mills, C. J., Tracey, D., Kiddle, R., , others, and Gorkin, R. (2023). Evaluating a virtual reality sensory room for adults with disabilities. Scientific Reports, 13:495.

Mills, J. and Duffy, O. (2025). Speech and language therapists’ perspectives of virtual reality as a clinical tool for autism: Cross-sectional survey. JMIR Rehabilitation and Assistive Technologies, 12:e63235.

Ministério da Saúde (2020). Estratégia de Saúde Digital para o Brasil 2020–2028. [link]. Access in: 23 fev. 2026.

Ministério da Saúde (2020). Estratégia de saúde digital para o brasil 2020–2028. Technical report, Ministério da Saúde, Brasília.

Ministério da Saúde (2024). RNDS - Guia de Integração e Perfis FHIR. Departamento de Informática do SUS (DATASUS) / Secretaria de Informação e Saúde Digital, Brasília, DF. Disponível em: [link] e [link].

Mittal, P., Bhadania, M., Tondak, N., Ajmera, P., Yadav, S., Kukreti, A., Kalra, S., and Ajmera, P. (2024). Effect of immersive virtual reality-based training on cognitive, social, and emotional skills in children and adolescents with autism spectrum disorder: A meta-analysis of randomized controlled trials. Research in Developmental Disabilities, 151:104771.

Morales, A. S. and Cazella, S. C. (2023). Internet das coisas e ambientes inteligentes no contexto da saúde.

Morales, A. S., Reis, T. d. L., Panisson, A. R., Ourique, F., and Sene, I. G. (2026). Affective intelligent systems in healthcare: A systematic review. Technologies, 14(3).

Neerincx, A., Veldhuis, D., Masthoff, J. M., and de Graaf, M. M. (2023). Co-designing a social robot for child health care. International Journal of Child-Computer Interaction, 38:100615.

Nestor, B., McDermott, M. B., Boag, W., Berner, G., Naumann, T., Hughes, M. C., Zajac, A., and Szolovits, P. (2019). Feature robustness in nonstationary health records: caveats to deployable model performance in rapidly changing clinical environments. In Machine Learning for Healthcare Conference, pages 114–150. PMLR.

Newbutt, N., Sung, C., Kuo, H.-T., Leahy, M. J., Lin, C. C., and Tong, B. (2020). Brief report: A pilot study of the use of a virtual reality headset in autism populations. Cyberpsychology, Behavior, and Social Networking.

Nichol, B., McCready, J., Erfani, G., Comparcini, D., Simonetti, V., Cicolini, G., Mikkonen, K., Yamakawa, M., and Tomietto, M. (2024). Exploring the impact of socially assistive robots on health and wellbeing across the lifespan: an umbrella review and meta-analysis. International Journal of Nursing Studies, 153:104730.

NORORI, N. et al. Addressing bias in big data and AI for health care: A call for open science. Patterns, v. 2, n. 10, art. 100347, 2021. DOI: 10.1016/j.patter.2021.100347.

Núcleo de Informação e Coordenação do Ponto BR - NIC.br (2025) “Pesquisa sobre o uso das tecnologias de informação e comunicação nos estabelecimentos de saúde brasileiros: TIC Saúde 2024”. São Paulo: Comitê Gestor da Internet no Brasil.

Nunes, K., e Silva, M. A. C., Rodrigues, M. R., Lemes, R. B., Pezo-Valderrama, P., Kimura, L., de Sena, L. S., Krieger, J. E., Varela, M. C., de Azevedo, L. O., Camargo, L. M. A., Ferreira, R. G. M., Krieger, H., Bortolini, M. C., Mill, J. G., Sacuena, P., ao F. Guerreiro, J., de Souza, C. M. B., Veronese, F. V., Vianna, F. S. L., Comas, D., Pereira, A. C., Pereira, L. V., and Hünemeier, T. (2025). Admixture’s impact on brazilian population evolution and health. Science, 388(6748):eadl3564.

OBERMEYER, Z. et al. Dissecting racial bias in an algorithm used to manage the health of populations. Science, v. 366, n. 6464, p. 447–453, 2019. DOI: 10.1126/science.aax234

Obermeyer, Ziad et al. (2019). “Dissecting racial bias in an algorithm used to manage the health of populations”. In: Science 366.6464, pp. 447–453.

of Us Research Program Investigators, A. (2019). The ”all of us” research program. New England Journal of Medicine, 381(7):668–676.

Olazarán, J., Reisberg, B., Clare, L., Cruz, I., Peña-Casanova, J., Del Ser, T., Woods, B., Beck, C., Auer, S., Lai, C., Spector, A., Fazio, S., Bond, J., Kivipelto, M., Brodaty, H., Rojo, J. M., Collins, H., Teri, L., Mittelman, M., Orrell, M., Feldman, H. H., and Muñiz, R. (2010). Nonpharmacological therapies in alzheimer’s disease: A systematic review of efficacy. Dementia and Geriatric Cognitive Disorders, 30(2):161–178.

Ophir, Y., Rosenberg, H., Tikochinski, R., Dalyot, S., and Lipshits-Braziler, Y. (2023). Screen time and autism spectrum disorder: A systematic review and meta-analysis. JAMA Network Open, 6(12):e2346775.

Ovadia, Yaniv et al. (2019). “Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift”. In: Advances in neural information processing systems 32.

Overeem, M., Spoor, M., Jansen, S., and Brinkkemper, S. (2021). An empirical characterization of event sourced systems and their schema evolution—lessons from industry. Journal of Systems and Software, 178:110970.

PASIPAMIRE, N.; MUROYIWA, A. Navigating algorithm bias in AI: ensuring fairness and trust in Africa. Frontiers in Research Metrics and Analysis, v. 9, art. 1486600, 2024. DOI: 10.3389/frma.2024.1486600.

Pati, Sarthak et al. (2024). “Privacy preservation for federated learning in health care”. In: Patterns 5.7.

Patil, O. and Kaple, M. (2023). Sensory processing differences in individuals with autism spectrum disorder: A narrative review of underlying mechanisms and sensory-based interventions. Cureus, 15(10):e48020.

Pelosi, D., Cacciagrano, D., and Piangerelli, M. (2025). Explainability and interpretability in concept and data drift: A systematic literature review. Algorithms, 18:443.

Pereira Carlini, Lucas et al. (2025). “Is this neonate feeling pain? Leveraging clinical knowledge towards high-precision Large Language Model-based neonatal pain assessment”. In: Pediatric Research, pp. 1–8.

Perry, Mallory et al. (2018). “Neonatal pain: perceptions and current practice”. In: Critical care nursing clinics of North America 30.4, p. 549.

Petretto, D. R., Carrogu, G. P., Gaviano, L., Berti, R., Pinna, M., Petretto, A. D., and Pili, R. (2024). Telemedicine, e-health, and digital health equity: A scoping review. Clinical Practice and Epidemiology in Mental Health, 20:e17450179279732.

Pirie, Craig et al. (2025). “Understanding Disagreement Between Humans and Machines in XAI: Robustness, Fidelity, and Region-Based Explanations in Automatic Neonatal Pain Assessment”. In: World Conference on XAI. Springer, pp. 274–298.

Pu, L., Moyle, W., Jones, C., and Todorovic, M. (2019). The effectiveness of social robots for older adults: a systematic review and meta-analysis of randomized controlled studies. The gerontologist, 59(1):e37–e51.

Puglisi, A., Caprì, T., Pignolo, L., Gismondo, S., Chilà, P., Minutoli, R., Marino, F., Failla, C., Arnao, A. A., Tartarisco, G., et al. (2022). Social humanoid robots for children with autism spectrum disorders: A review of modalities, indications, and pitfalls. Children, 9(7):953.

Puhl, K. B., Albuquerque, L. M. F., Sousa, M. Z. B., Campos, L. A., Conceição, R. S., Costa, B. K. F., Ávila, E. S. P., Medeiros, Y. M. F., and Cavalcante, A. K. C. B. (2026). A expansão da telemedicina no brasil: oportunidades, riscos e desafios para a equidade no acesso aos serviços de saúde coletiva. Revista Delos, 19(76):e8187.

Qu, J., Holzmüller, D., Varoquaux, G., and Le Morvan, M. (2025). TabICL: A tabular foundation model for in-context learning on large data. arXiv:2502.05564.

Rauner, Y. N. and Stummer, H. (2024). The interplay of social shaping determinants and diffusion in the implementation of digital innovations in health care systems: An explanatory approach using telemedicine in germany. Available at SSRN 5070208.

Robins, B., Ferrari, E., Dautenhahn, K., Kronreif, G., Prazak-Aram, B., Gelderblom, G. J., Tanja, B., Caprino, F., Laudanna, E., and Marti, P. (2010). Human-centred design methods: Developing scenarios for robot assisted play informed by user panels and field trials. International Journal of Human Computer Studies, 68(12):873–898.

Robinson, F. and Nejat, G. (2022). An analysis of design recommendations for socially assistive robot helpers for effective human-robot interactions in senior care. Journal of Rehabilitation and Assistive Technologies Engineering, 9:20556683221101389.

Rocha, M., Valentim, P., Barreto, F., Mitjans, A., Cruz-Sandoval, D., Favela, J., and Muchaluat-Saade, D. (2021). Towards enhancing the multimodal interaction of a social robot to assist children with autism in emotion regulation. In International Conference on Pervasive Computing Technologies for Healthcare, pages 398–415. Springer.

Roehrs, A., Da Costa, C. A., & da Rosa Righi, R. (2017). OmniPHR: A distributed architecture model to integrate personal health records. Journal of biomedical informatics, 71, 70-81.

Roehrs, A., da Costa, C. A., da Rosa Righi, R., Rigo, S. J., & Wichman, M. H. (2018). Toward a model for personal health record interoperability. IEEE journal of biomedical and health informatics, 23(2), 867-873.

Roehrs, A., da Costa, C. A., Righi, R. R., Mayer, A. H., da Silva, V. F., Goldim, J. R., & Schmidt, D. C. (2021). Integrating multiple blockchains to support distributed personal health records. Health Informatics Journal, 27(2), 14604582211007546.

Salekin, Md Sirajus et al. (2021). “Multimodal spatio-temporal deep learning approach for neonatal postoperative pain assessment”. In: Computers in biology and medicine 129, p. 104-150.

Salgado, A. C.; Motta, C. L. R.; Santoro, F. M. (orgs.) (2015) “Grandes Desafios da Computação no Brasil: relatos do 3º seminário”. Porto Alegre: Sociedade Brasileira de Computação.

Sanga, Mbah Carlson, Ngum Precious Fosah, and Geh Wilson Ejuh (2025). “Design and realization of an autonomous multi-powered IOT-based infant incubator monitoring system hardware module”. In: International Journal of Research in Advanced Electronics Engineering 6.1, pp. 56–65.

Santos, A. L. M.; Wagner, F. R. (orgs.) (2024) “IV Seminário dos Grandes Desafios da Computação no Brasil: Trabalhos Apresentados”. Porto Alegre: Sociedade Brasileira de Computação.

Santos, L., Geminiani, A., Schydlo, P., Olivieri, I., Santos-Victor, J., and Pedrocchi, A. (2021). Design of a robotic coach for motor, social and cognitive skills training toward applications with asd children. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 29:1223–1232.

SASSEVILLE, M. et al. Bias mitigation in primary health care artificial intelligence models: scoping review. Journal of Medical Internet Research, v. 27, e60269, 7 jan. 2025. DOI: 10.2196/60269.

SCHEFFER, M. et al. Demografia Médica no Brasil 2023. São Paulo: FMUSP/AMB, 2023. Disponível em: [link]

Schiavenato, Martin et al. (2008). “Neonatal pain facial expression: Evaluating the primal face of pain”. In: Pain 138.2, pp. 460–471.

SEYYED-KALANTARI, L. et al. Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nature Medicine, v. 27, n. 12, p. 2176–2182, 2021. DOI: 10.1038/s41591-021-01595-0.

Shamsuddin, S., Yussof, H., Ismail, L. I., Mohamed, S., Hanapiah, F. A., and Zahari, N. I. (2012). Initial response in hri-a case study on evaluation of child with autism spectrum disorders interacting with a humanoid robot nao. Procedia Engineering, 41:1448–1455.

Shen, Y., Yu, J., Zhou, J., and Hu, G. (2025). Twenty-five years of evolution and hurdles in electronic health records and interoperability in medical research: Comprehensive review. Journal of Medical Internet Research, 27:e59024.

Shi, Weisong et al. (2016). “Edge computing: Vision and challenges”. In: IEEE internet of things journal 3.5, pp. 637–646.

Shibata, T. (2004). An overview of human interactive robots for psychological enrichment. Proceedings of the IEEE, 92(11):1749–1758.

Shibata, T. (2012). Therapeutic seal robot as biofeedback medical device: Qualitative and quantitative evaluations of robot therapy in dementia care. Proceedings of the IEEE, 100(8):2527–2538.

Shibata, T., Inoue, K., and Irie, R. (1996). Emotional robot for intelligent system-artificial emotional creature project. In Proceedings 5th IEEE International Workshop on Robot and Human Communication. RO-MAN’96 TSUKUBA, pages 466–471. IEEE.

Shortliffe, E. H. and Sepúlveda, M. J. (2018). Clinical decision support in the era of artificial intelligence. Jama, 320(21):2199–2200.

Silva, D. S. M. d., Assumpção, D. d., Francisco, P. M. S. B., Yassuda, M. S., Neri, A. L., and Borim, F. S. A. (2021). Doenças crônicas não transmissíveis considerando determinantes sociodemográficos em coorte de idosos. Revista brasileira de geriatria e gerontologia, 25(5):e210204.

Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S.,Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al. (2023). Large language models encode clinical knowledge. Nature, 620(7972):172–180.

Sociedade Brasileira de Computação - SBC (2006) “Grandes Desafios da Pesquisa em Computação no Brasil – 2006–2016”. Relatório sobre o Seminário realizado em 8 e 9 de maio de 2006. Porto Alegre: Sociedade Brasileira de Computação.

Sociedade Brasileira de Computação - SBC (2010) “II Seminário sobre Grandes Desafios da Computação no Brasil: Integração com a Indústria e uma Perspectiva para 2020”. Manaus, 3 e 4 de março de 2009. Porto Alegre: Sociedade Brasileira de Computação.

Sociedade Brasileira de Computação - SBC (2024) “Revisão e atualização do Código de Conduta para Autores”. Resolução 2024-001. Porto Alegre: SBC.

Spitale, M. and Gunes, H. (2022). Affective robotics for wellbeing: A scoping review. In 2022 10th International conference on affective computing and intelligent interaction workshops and demos (ACIIW), pages 1–8. IEEE.

Sunwoo, John and Mohamed El-Dib (2026). “Beyond the face: advancing multimodal AI for neonatal pain assessment”. In: Pediatric Research, pp. 1–3.

Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., and Kroeker, K. I. (2020). An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ digital medicine, 3(1):17.

Temporão, J. G., Santini, L. A., Santos, A. T. C. d., Fernandes, F. M. B., and Zoss,W. P. (2022). Desafios atuais e futuros do uso da medicina de precisão no acesso ao diagnóstico e tratamento de câncer no brasil. Cadernos de Saúde Pública, 38:e00006122.

Thalmann, N. M. (2022). Social robots: Their history and what they can do for us. Perspectives on digital humanism, pages 9–17.

TOPOL, E. J. Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Updated Edition. New York: Basic Books, 2024.

Tran, H. N., Phan, P. H., Nguyen, K. H., Hua, H. K., Nguyen, A. Q., Nguyen, H. N., and Nguyen, N. V. (2024). Augmentation-enhanced deep learning for face detection and emotion recognition in elderly care robots.

Tsymbal, A. (2004). The problem of concept drift: definitions and related work.

Vagnetti, R., Di Nuovo, A., Mazza, M., and Valenti, M. (2024). Social robots: A promising tool to support people with autism. a systematic review of recent research and critical analysis from the clinical perspective. Review Journal of Autism and Developmental Disorders, pages 1–25.

Vandemeulebroucke, T., Dierckx de Casterlé, B., and Gastmans, C. (2021). Socially assistive robots in aged care: ethical orientations beyond the care-romantic and technology-deterministic gaze. Science and Engineering Ethics, 27(2):17.

Vanin, F. N. D. S., Policarpo, L. M., Righi, R. D. R., Heck, S. M., da Silva, V. F., Goldim, J., & da Costa, C. A. (2022). A blockchain-based end-to-end data protection model for personal health records sharing: a fully homomorphic encryption approach. Sensors, 23(1), 14.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, !., and Polosukhin, I. (2017). Attention is all you need. In NeuriPS, volume 30.

Vasylkiv, Y., Ma, Z., Li, G., Brock, H., Nakamura, K., Pourang, I., and Gomezv, R. (2021). Shaping affective robot haru’s reactive response. In 2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN), pages 989–996. IEEE.

VICTOR, A. Artificial intelligence in global health: An unfair future for health in Sub-Saharan Africa? Health Affairs Scholar, v. 3, n. 2, art. qxaf023, 2025. DOI: 10.1093/haschl/qxaf023.

Victor, Audêncio et al. (2025). “National and regional Temporal trends and forecasting of preterm birth in brazil: evidence from National birth data (2014–2023) with projections to 2030”. In: BMC pregnancy and childbirth 25.1, p. 1339.

Vlachos, E. and Tan, Z.-H. (2020). Can robots express facial emotions dominantly enough for use in dementia care? International Psychogeriatrics, 32(7):891–892.

Wah, J. N. K. (2025). Ai, robotics, and telemedicine: Shaping the future of elderly care and global health.

Wang, T., Ma, Y., Du, X., Li, C., Peng, Z., Wang, Y., and Zhou, H. (2024). Digital interventions for autism spectrum disorders: A systematic review and meta-analysis. Pediatric Investigation, 8(3):224–236.

WHITTAKER, R. et al. An example of governance for AI in health services from Aotearoa New Zealand. npj Digital Medicine, v. 6, n. 1, art. 164, 2023. DOI: 10.1038/s41746-023-00882-z.

WHOi WORLD HEALTH ORGANIZATION. Ethics and governance of artificial intelligence for health: Guidance on large multi-modal models. Geneva: WHO, 18 jan. 2024.

Wooldridge, M. (2009). An Introduction to MultiAgent Systems. John Wiley & Sons, Chichester, 2 edition.

World Health Organization (2021). Global strategy on digital health 2020–2025. Technical report, WHO.

World Health Organization A (2021). Ethics and Governance of Artificial Intelligence for Health. [link]. Access in: 23 fev. 2026.

World Health Organization B (2023). Regulatory Considerations on Artificial Intelligence for Health. [link]. Access in: 23 fev. 2026.

WU, Jiageng et al. Clinical text datasets for medical artificial intelligence and large language models: a systematic review. NEJM AI, v. 1, n. 6, 2024. DOI: 10.1056/AIra2400012

Yang, X., Wu, J., Ma, Y., Yu, J., Cao, H., Zeng, A., Fu, R., Tang, Y., and Ren, Z. (2025). Effectiveness of virtual reality technology interventions in improving the social skills of children and adolescents with autism: Systematic review. Journal of Medical Internet Research, 27:e60845.

Yu, C., Sommerlad, A., Sakure, L., and Livingston, G. (2022). Socially assistive robots for people with dementia: systematic review and meta-analysis of feasibility, acceptability and the effect on cognition, neuropsychiatric symptoms and quality of life. Ageing research reviews, 78:101633.

ZEPEDA, L. E. G.; MARTÍNEZ PINTO, C. E. Inteligencia artificial centrada en los pueblos indígenas: perspectivas desde América Latina y el Caribe. Paris: UNESCO, 2023. Disponível em: [link]

Zhang, Y., Rosli, Y., and Tang, S. (2025). Scoping review of the e-health literature in older adults. Digital Health.

Zhang, Y., Yang, X., Xu, X., Gao, Z., Huang, Y., Mu, S., Feng, S., Wang, D., Zhang, Y., Song, K., et al. (2024). Affective computing in the era of large language models: A survey from the nlp perspective. arXiv preprint arXiv:2408.04638.

ZHOU, Z., Chen, X., Li, E., Zeng, L., Luo, K. and Zhang, J. (2019) Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing. Proceedings of the IEEE, 107, 1738-1762. DOI: 10.1109/jproc.2019.2918951