Sharing Biomedical Data: Strengthening AI Development in Healthcare
Open accessAbstract
Artificial intelligence (AI)-based solutions have revolutionized our world, using extensive datasets and computational resources to create automatic tools for complex tasks that, until now, have been performed by humans. Massive data is a fundamental aspect of the most powerful AI-based algorithms. However, for AI-based healthcare solutions, there are several socioeconomic, technical/infrastructural, and most importantly, legal restrictions, which limit the large collection and access of biomedical data, especially medical imaging. To overcome this important limitation, several alternative solutions have been suggested, including transfer learning approaches, generation of artificial data, adoption of blockchain technology, and creation of an infrastructure composed of anonymous and abstract data. However, none of these strategies is currently able to completely solve this challenge. The need to build large datasets that can be used to develop healthcare solutions deserves special attention from the scientific community, clinicians, all the healthcare players, engineers, ethicists, legislators, and society in general. This paper offers an overview of the data limitation in medical predictive models; its impact on the development of healthcare solutions; benefits and barriers of sharing data; and finally, suggests future directions to overcome data limitations in the medical field and enable AI to enhance healthcare. This perspective is dedicated to the technical requirements of the learning models, and it explains the limitation that comes from poor and small datasets in the medical domain and the technical options that try or can solve the problem related to the lack of massive healthcare data.
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Blockchain technology provides a secure, decentralized method for storing and managing healthcare data, using a distributed ledger system to ensure patient records are immutable and verifiable, thereby thwarting unauthorized access and potential data breaches.
“Blockchain technology provides a secure, decentralized method for storing and managing healthcare data, utilizing a distributed ledger system to ensure that patient records are immutable and verifiable, thereby thwarting unauthorized access and potential data breaches.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [12]
Mishandling sensitive medical data could result in significant privacy violations and misuse.
“Mishandling such data could result in significant privacy violations and misuse [7,12].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [12]
Blockchain can be integrated with AI and IoT devices to enable secure and real-time monitoring of patient health data, providing healthcare professionals with reliable data for decision-making while preserving patient privacy.
“Moreover, blockchain can be integrated with other technologies, such as AI and IoT devices, to enable secure and real-time monitoring of patient health data, providing healthcare professionals with reliable data for decision-making while preserving patient privacy [7,12].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [12]
There is only a slight decrease in accuracy for models trained with synthetic data compared to those trained with accurate data.
“While there is only a slight decrease in accuracy for models trained with synthetic data compared to those trained with accurate data, challenges in synthetic data generation remain, especially in medical imaging.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [12]
Generating synthetic electronic health records involves creating large databases encompassing linear and nonlinear associations among medical elements and random associations.
“The complexities and challenges of generating synthetic electronic health records are explored in depth, focusing on creating large databases encompassing linear and nonlinear associations among medical elements and random associations.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [12]
Preserving the interactions and correlations in the original data introduces complexity, necessitating comprehensive capture of high-order relationships within the data.
“Additionally, preserving the interactions and correlations in the original data introduces another layer of complexity, necessitating the comprehensive capture of high-order relationships within the data.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [12]
Synthetic data generation faces challenges related to data realism and validation.
“However, it faces challenges related to data realism and validation [12].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [12]
Synthetic data protects patient privacy while preserving data utility.
“This method protects patient privacy while preserving data utility.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [12]
Synthetic data generation is crucial to maintaining privacy and security in healthcare AI applications, enabling the creation of extensive datasets for research and development while protecting patient privacy and AI model integrity.
“In conclusion, synthetic data generation is crucial to maintaining privacy and security in healthcare AI applications, enabling the creation of extensive datasets for research and development while protecting patient privacy and AI model integrity.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [12]
Synthetic data replicating the statistical characteristics and correlations of original data overcomes common data-sharing obstacles, allowing for secure access and sharing across institutions.
“Synthetic data, replicating the statistical characteristics and correlations of original data, overcomes common data-sharing obstacles, allowing for secure access and sharing across institutions.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [12]
Smaller databases may only partially capture the extensive statistical characteristics of the original data, which can impact the realism and utility of the synthetic data.
“Smaller databases may only partially capture the extensive statistical characteristics of the original data, which can impact the realism and utility of the synthetic data.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [12]
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MethodThe GATE Crowdsourcing Plugin: Crowdsourcing Annotated Corpora Made Easy
Bontcheva, K., Roberts, I. G., Derczynski, L., & Rout, D. (2014) · doi:10.3115/v1/e14-2025 · Cited by 30 · cited 1× · see citing works
“There are several annotation platforms, such as MTurk [32] and Figure Eight (formerly Crowdflower) [33], which are usually based on nonspecialized annotators that follow a very restrict and objective set of rules to define the classes for labeling [34].”
MethodUnknown Examples & Machine Learning Model GeneralizationPreprint
Chung, Y., Haas, P. J., Upfal, E., & Kraska, T. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.08294 · Cited by 28 · cited 1× · see citing works
“One way to test generalization is to evaluate existing models using new independent data that are identically distributed to the original training set [10].”
MethodData Augmentation Using Generative Adversarial NetworkPreprint
Lata, K., Dave, M., & Nishanth, K. N. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3349576 · Cited by 26 · cited 1× · see citing works
“Generative models have recently been applied to generate augmented data for biomedical datasets, thus emerging as a useful tool to increase the number and variability of available examples [47].”
MethodArchetype-Based Semantic Integration and Standardization of Clinical Data
Moner, D., Maldonado, J. A., Boscá, D., Fernández‐Breis, J. T., Angulo, C., Crespo, P., Vivancos-Vicente, P. J., & Robles, M. (2006) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2006.260124 · Cited by 25 · cited 1× · see citing works
“Biomedical data are usually distributed among several heterogeneous and semantically incompatible health information systems, leading to interoperability problems [27].”
BackgroundFrom Privacy to Data Protection in the eu: Implications for Big Data Health Research
Mostert, M., Bredenoord, A. L., van der Slootb, B., & van Delden, J. J. M. (2017) · European Journal of Health Law · doi:10.1163/15718093-12460346 · Cited by 24 · cited 1× · see citing works
“In the healthcare sector there is an ethical and scientific imperative to share personal data for research purposes [60].”
BackgroundTen principles for data sharing and commercialization
Cole, C. L., Sengupta, S., Rossetti, S. C., Vawdrey, D. K., Halaas, M., Maddox, T. M., Gordon, G. D., Dave, T., Payne, P., Williams, A. E., et al. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa260 · Cited by 23 · cited 1× · see citing works
“More recently, ten principles for data sharing and commercialization have been proposed, which will help guide healthcare institutions to share clinical data with the aim of improving patient care and fostering innovation [55].”
BackgroundMedical Data Integration and the Semantic Annotation of Medical Protocols
Berlanga, R., Jiménez-Ruiz, E., Nebot, V., Manset, D., Branson, A., Hauer, T., McClatchey, R., Rogulin, D., Shamdasani, J., Zillner, S., et al. (2008) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms.2008.90 · Cited by 21 · cited 1× · see citing works
“The goal of data integration is to create a unique semantic reference to ensure data consistency and reuse and, consequently, improve clinical practice, medical research, and personalized medicine [28].”
BackgroundZillner, S., & Neururer, S. B. (2016) · Springer eBooks · doi:10.1007/978-3-319-21569-3_10 · Cited by 18 · cited 1× · see citing works
“A common infrastructure with common standards for the integration of multiple data sources into one platform (data integration) is fundamental to allow data sharing [63].”
BackgroundSonsilphong, S., & Arch‐int, N. (2013) · Journal of Convergence Information Technology · doi:10.4156/jcit.vol8.issue3.18 · Cited by 15 · cited 1× · see citing works
“Most of these institutions lack comprehensive semantic definitions of the information they contain and have limitations in extracting parameters to solve semantic service discrepancies [30].”
BackgroundA Flexible Semantic Integration Framework for Fully-integrated EHR based on FHIR Standard
Dridi, A., Sassi, S., Chbeir, R., & Faïz, S. (2020) · International Conference on Agents and Artificial Intelligence (ICAART) · doi:10.5220/0008981506840691 · Cited by 12 · cited 2× · see citing works
“Despite the extreme importance of standards-based data interoperability and EHR integration, their implementation is not consensual [61].”
BackgroundThe Zenome Project: Whitepaper blockchain-based genomic ecosystem
Kulemin, N., Popov, S., & Gorbachev, A. Y. (2017) · doi:10.13140/rg.2.2.25865.13925 · Cited by 12 · see citing works
Weiler, G., Schwarz, U., Rauch, J., Röhm, K. D., Lehr, T., Theobald, S., Kiefer, S., Katharina, G., Och, K., Pfeifer, N., et al. (2018) · Studies in health technology and informatics · doi:10.3233/978-1-61499-852-5-21 · Cited by 10 · cited 1× · see citing works
“Clinical annotations, medical reports, lab results, imagiological findings, and expressive description of data using standardized procedures are key elements to maximize the quality and applicability of the next generation of the AI-based models [62].”
MethodCampion, E. W., & Jarcho, J. A. (2019) · New England Journal of Medicine · doi:10.1056/nejme1913980 · Cited by 9 · cited 2× · see citing works
“Data owners will shape the future by creating the new generation of tech solutions [22,23].”
Background
Flagged first, then by citations
3 of the 69 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- [26]ALLEA EASAC FEAM International Sharing of Personal Health Data for Research The ALLEA, EASAC and FEAM Joint Initiative on Resolving the Barriers of Transferring Public Sector Data Outside the EU/EEA 2021 63 Available online: 10.26356/IHDT (accessed on 3 March 2021) 10.26356/IHDT · DOI
- [34]de Herrera A.G.S. Foncubierta-Rodrıguez A. Markonis D. Schaer R. Müller H. Crowdsourcing for medical image classification Proceedings of the Annual Congress SGMI 2014 Volume 2014 Available online: https://hesso.tind.io/record/698 (accessed on 3 May 2021)
Cited by
Sorted by citations
Williamson, S. M., & Prybutok, V. (2024) · Applied Sciences · doi:10.3390/app14020675 · Cited by 567 · see citing works
“FL’s ability to integrate data from various sources without compromising privacy is a testament to its potential to enhance the capabilities of healthcare AI systems.”
BackgroundArtificial Intelligence and Biosensors in Healthcare and Its Clinical Relevance: A Review
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“ing the role of AI is presented in [127].”
BackgroundAKKAŞ, T., Reshadsedghi, M., Şen, M., Kılıç, V., & Horzum, N. (2025) · Advanced Materials · doi:10.1002/adma.202504796 · Cited by 197 · see citing works
Generative artificial intelligence (AI) literacy in nursing education: A crucial call to action
Simms, R. C. (2024) · Nurse Education Today · doi:10.1016/j.nedt.2024.106544 · Cited by 181 · see citing works
Blockchain, artificial intelligence, and healthcare: the tripod of future—a narrative review
Bathula, A., Gupta, S. K., Merugu, S., Saba, L., Khanna, N. N., Laird, J. R., Sanagala, S. S., Singh, R. K., Garg, D., Fouda, M. M., et al. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10873-5 · Cited by 100 · see citing works
3D printing of nutritious dysphagia diet: Status and perspectives
Liu, Z., Chen, X., Li, H., Chitrakar, B., Zeng, Y., Hu, L., & Mo, H. (2024) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2024.104478 · Cited by 72 · see citing works
Achieving large-scale clinician adoption of AI-enabled decision support
Scott, I. A., van der Vegt, A., Lane, P., McPhail, S., & Magrabi, F. (2024) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2023-100971 · Cited by 65 · see citing works
“47 This requires the provision of educational resources, 48 sets of AI competencies 49 52 Concurrently, data privacy and security must be safe-guarded under umbrella instruments such as the General Data Protection Regulation.”
BackgroundArtificial intelligence for drug repurposing against infectious diseases
Singh, A. (2024) · Artificial Intelligence Chemistry · doi:10.1016/j.aichem.2024.100071 · Cited by 53 · see citing works
Towards Machine Learning-Aided Lung Cancer Clinical Routines: Approaches and Open Challenges
Silva, F., Pereira, T., Neves, I., Morgado, J., Freitas, C., Malafaia, M., Sousa, J. V., Fonseca, J., Negrão, E., de Lima, B. F., et al. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12030480 · Cited by 46 · see citing works
“Lack of large datasets remains a problem for healthcare solutions since models cannot learn how to deal with complex data due to insufficient training samples [205,206].”
BackgroundSelf-citationEphraim, R. K. D., Kotam, G. P., Duah, E., Ghartey, F. N., Mathebula, E. M., & Mashamba-Thompson, T. P. (2024) · Smart Health · doi:10.1016/j.smhl.2024.100505 · Cited by 43 · see citing works
Wang, A., Zhou, Y. J., Ma, H., Tang, X., Li, S., Pei, R., & Piao, M. (2024) · Digital Health · doi:10.1177/20552076241284903 · Cited by 31 · see citing works
Robustness Analysis of Deep Learning-Based Lung Cancer Classification Using Explainable Methods
Malafaia, M., Silva, F., Neves, I., Pereira, T., & Oliveira, H. P. (2022) · IEEE Access · doi:10.1109/access.2022.3214824 · Cited by 25 · see citing works
“LIDC-IDRI dataset used in this work is the most well know dataset for lung cancer-related research activities and it was extensively used for computer-aided decision systems for lung cancer detection [1].”
BackgroundMethodSelf-citationArtificial Intelligence in Health Promotion and Disease Reduction: Rapid Review
Yousefi, F., Naye, F., Ouellet, S., Yameogo, A. R., Sasseville, M., Bergeron, F., Ozkan, M., Cousineau, M., Amil, S., Rhéaume, C., et al. (2025) · Journal of Medical Internet Research · doi:10.2196/70381 · Cited by 28 · see citing works
“In comparison to related studies, our findings align with previous reviews, emphasizing the constraints of insufficient data, poor validation, ethical concerns, legal barriers [23,112,113], and technical obstacles [114].”
Chavez, J. M., & Tang, W. (2022) · Sensors · doi:10.3390/s22124463 · Cited by 18 · see citing works
“The use of synthetic data has also been applied in the biomedical field, where augmented data has been developed to increase the number and variability of examples [55].”
MethodDubois, C., Eigen, D., Delmas, E., Einfalt, M., Lemaçon, C., Berteloot, L., Bossuyt, P. M., Drummond, D., Scherdel, P., Simon, F., et al. (2024) · BMJ · doi:10.1136/bmj-2023-076703 · Cited by 21 · see citing works
“73 DL diagnostic tools might be particularly accurate for prediction in diagnostic areas with a high signal-to-noise ratio, such as the ones described here.”
A Systematic Literature Review on the Revolutionary Impact of Blockchain in Modern Business
Mohammed, M. A., De‐Pablos‐Heredero, C., & Botella, J. L. M. (2024) · Applied Sciences · doi:10.3390/app142311077 · Cited by 18 · see citing works
“Studies have shown that incorporating blockchain into e-commerce enhances security and privacy, thereby providing superior customer protection for customers [77].”
Jiang, J., Domingues, L., & Mendes, J. M. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1620270 · Cited by 14 · see citing works
Revolutionizing Innovations and Impact of Artificial Intelligence in Healthcare
-, I. C., -, R. G., -, S. S., -, K. D., & Kundu, M. (2024) · International Journal For Multidisciplinary Research · doi:10.36948/ijfmr.2024.v06i03.19333 · Cited by 15 · see citing works
Federated Learning in Glaucoma
Hallaj, S., Chuter, B., Lieu, A. C., Singh, P., Kalpathy–Cramer, J., Xu, B. Y., Christopher, M. A., Zangwill, L. M., Weinreb, R. N., & Baxter, S. L. (2024) · Ophthalmology Glaucoma · doi:10.1016/j.ogla.2024.08.004 · Cited by 10 · see citing works
Qiu, Y., & Hu, Z. (2025) · Digital Health · doi:10.1177/20552076251320302 · Cited by 12 · see citing works
“34 Researchers in the fi elds of life sciences and medicine should keep abreast of and educate themselves on big data and arti fi cial intelligence technologies.”
AΙ-Driven Drug Repurposing: Applications and Challenges
Keramida, P., Syrigos, N. K., Kouvela, M., Poulakou, G., Charpidou, A. G., & Fiste, O. (2025) · Medicines · doi:10.3390/medicines12040028 · Cited by 12 · see citing works
“Promoting data exchange between institutions, implementing data pre-processing, and adopting strategies to reduce dataset size could minimize data quality issues, such as noise, missing values, or biased datasets, and enhance performance of AI models [279].”
Challenges and Solutions for Artificial Intelligence Adoption in Healthcare – A Literature Review
Doniņš, U., & Behmane, D. (2023) · Smart innovation, systems and technologies · doi:10.1007/978-981-99-3311-2_6 · Cited by 11 · see citing works
Rollo, C., Pancotti, C., Birolo, G., Rossi, I., Sanavia, T., & Fariselli, P. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108288 · Cited by 9 · see citing works
Liu, K., Weicheng, Q., & Yang, X. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1595060 · Cited by 9 · see citing works
Indrasiri, P. L., Kashyap, B., & Pathirana, P. N. (2025) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-025-02613-0 · Cited by 8 · see citing works
“This issue is critical in healthcare, where data on rare diseases is inherently scarce, yet vital for understanding disease progression and aiding patient care [13–15].”
A Review of Biosensors and Artificial Intelligence in Healthcare and their Clinical Significance
Tariq, M. (2024) · Psychology & Psychological Research International Journal · doi:10.23880/pprij-16000392 · Cited by 8 · see citing works
Wahid, K. A., Cárdenas, C., Marquez, B., Netherton, T., Kann, B. H., Court, L. E., He, R., Naser, M. A., Moreno, A. C., Fuller, C. D., et al. (2024) · Advances in Radiation Oncology · doi:10.1016/j.adro.2024.101521 · Cited by 6 · see citing works
“Additionally, unlike other industries where vast data repositories exist, medical research is marked by a relative data shortage, 25 making the pursuit of a data-optimization strategy potentially more fruitful than model-optimization in the current landscape.”
BackgroundImproving Prospective Healthcare Outcomes by Leveraging Open Data and Explainable AI
Alam, G., McChesney, I., Nicholl, P., & Rafferty, J. (2023) · International Conference on Computing and Information Technology (ICCIT) · doi:10.1109/iccit58132.2023.10273878 · Cited by 5 · see citing works
“They have the potential to be utilised in the creation of a development process that is more collaborative and participatory, which is vital for ensuring that AI-based healthcare solutions are adapted to meet the requirements of a variety of stakeholders [32].”
BackgroundZhang, P.-F., Ting, H.-N., & Chang, S. (2025) · IEEE Access · doi:10.1109/access.2025.3584401 · Cited by 4 · see citing works
“Limited data coverage is evident in insufficient sample sizes, incomplete disease type representation, and lack of demographic diversity [79].”
Artificial Intelligence in Ophthalmology: Current Status, Challenges, and Future Perspectives
She, C., & Yong, T. (2026) · Health care science · doi:10.1002/hcs2.70052 · Cited by 3 · see citing works
“Hospitals struggle with data organization and sharing due to regulatory, privacy, ethical, and legal concerns, while computer science companies lack access to clinical data despite their computational expertise [69].”
Privacy-Preserving AI for Medical Applications
Bhimavarapu, U. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-1205-7.ch006 · Cited by 2 · see citing works
Yang, A. C., Pan, M.-L., Lu, H. H., Lien, C.-Y., Wang, D.-W., Chen, C., Tarng, D., Niu, D., Chiou, S.-H., Wu, C.-Y., et al. (2025) · Journal of Medical Internet Research · doi:10.2196/58954 · Cited by 4 · see citing works
“databases to AI research is the democratization of data [103].”
Balancing Technology and Humanity
Sachdeva, C., & Grover, V. (2024) · Advances in healthcare information systems and administration book series · doi:10.4018/979-8-3693-5468-1.ch001 · Cited by 2 · see citing works
Caw-It, J. G., Ricalde, G. T., & Bustillo, J. C. M. (2025) · International Conference on Soft Computing for Security Applications (ICSCSA) · doi:10.1109/icscsa66339.2025.11170964 · Cited by 1 · see citing works
Integrating and Enhancing Diverse Categories of Medical Data for AI-Driven Healthcare Solutions
Abir, S. I., Shoha, S., & Khan, N. I. (2025) · Springer eBooks · doi:10.1007/978-3-031-94386-7_8 · Cited by 1 · see citing works
MedFMIT: A Foundation Model-Driven Multimodal Fusion Method for Image-Text Disease Diagnosis
Liang, S., Fu, J., Li, W., Chen, M., Wang, Y., & Guo, Y. (2025) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc58623.2025.11252735 · Cited by 1 · see citing works
Developing AI Literacy for Nurse Leaders and Educators
Garcia-Dia, M. J. (2026) · Springer eBooks · doi:10.1007/978-3-032-12402-9_4 · Cited by 1 · see citing works
Lu, Y., Tian, Y., Zhou, T., Xia, J., Zhao, Z., Sun, H., Liu, K., & Li, J. (2026) · JMIR Formative Research · doi:10.2196/87306 · Cited by 1 · see citing works
“…to train robust models for specific tasks, such as studying rare diseases [1]; achieve results with higher reproducibility and generalizability [2]; accelerate the translation of research findings into clinical practice [3]; and discover novel patterns that are undetectable in any single…”
Applications of the Blockchain Technology in Emergency Crisis Management and Rescue Processes
Bayat, A., Bayat, M., & Pezhhan, A. (2021) · Journal of Rescue and Relief · doi:10.32592/jorar.2021.13.3.7 · Cited by 2 · see citing works
“However, in addition to technical barriers, such as access to and storage of data in the blockchain, there are barriers in policy and privacy issues (20).”
BackgroundDevelopment, Application and Prospect of Artificial Intelligence in Biomedical Field
Lu, Z. (2023) · Highlights in Science Engineering and Technology · doi:10.54097/hset.v39i.6651 · Cited by 1 · see citing works
Exploring AI in Healthcare: How the Acceleration of Data Processing can Impact Life Saving Diagnoses
Janes, J., Bansal, A., & Baron, T. (2022) · Electronic workshops in computing · doi:10.14236/ewic/odak22.2 · Cited by 1 · see citing works
Wahid, K. A., Cardenas, C. E., Marquez, B., Netherton, T., Kann, B. H., Court, L. E., He, R., Naser, M. A., Moreno, A. C., Fuller, C. D., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2310.10867 · Cited by 1 · see citing works
“Additionally, unlike other industries where vast data repositories exist, medical research is marked by a relative data shortage, 25 making the pursuit of a data-optimization strategy potentially more fruitful than model-optimization in the current landscape.”
BackgroundYang, A. C., Pan, M.-L., Lu, H. H., Lien, C.-Y., Wang, D., Chen, C., Tarng, D., Niu, D., Chiou, S.-H., Wu, C., et al. (2024) · doi:10.2196/preprints.58954 · Cited by 1 · see citing works
Artificial Intelligence in Advancing Mental Health Support Systems: Challenges and ProspectsPreprint
Sakib, S. M. N. (2023) · doi:10.31235/osf.io/dt7wz
A review of the development of intelligent delineation of radiotherapy contouring
Ren, R., Chen, G., Yang, F., Cui, T., Zhong, L., Zhang, Y., Luo, B., Zhao, L., Qian, J., & Sun, J. (2022) · Digital Medicine · doi:10.4103/digm.digm_25_22
Artificial Intelligence in Advancing Mental Health Support Systems
Bhardwaj, B., Sharma, P., Bag, A., & Singh, T. (2024) · Advances in healthcare information systems and administration book series · doi:10.4018/979-8-3693-4402-6.ch012
Big Data in Health Care: Opportunities, Challenges and Future Direction
Zaib, N., Kumar, S. S., & Ahsaan, S. U. (2025) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815313383125010007
AI Algorithms in Sustainable Healthcare
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Artificial Intelligence in Cardiovascular Pathology: Toward a Diagnostic Revolution
Marzullo, A., Quaranta, A., Cazzato, G., & Salzillo, C. (2026) · BioMedInformatics · doi:10.3390/biomedinformatics6020018
“The implementation of approaches such as federated learning will enable the training of complex models while safeguarding sensitive data, thereby fostering collaboration across centres [52,57,58].”
Blockchain-Anchored Federated AI for Patient-Centric Collaborative Diagnostics
Ravikumar, R. N., & Aarthi, S. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5906-9.ch006
User experience and ethical design for AI-driven behavioral health technologies
Armstrong, C. M., Luxton, D. D., & June, J. D. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-33151-0.00006-x
Integrating Fuzzy Logic and Neural Networks for Big Data-Based Biomedical Classification
Bakhtiyarov, B., Jabiyeva, A. J., & Khudaverdiyeva, M. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-3-032-23369-1_26
Securing Healthcare Data Using AI-Driven Approaches to Privacy and Compliance
Chaudhary, S. D., Reena, K. J., Sidhu, K. S., Satyanarayana, P., Bakyarani, E. S., Dhanraj, J. A., & Abozibid, H. K. (2026) · Springer eBooks · doi:10.1007/978-981-95-8828-2_15
- Authors
- Pereira, T., Morgado, J., Silva, F., Pelter, M. M., Dias, V. R., Barros, R., Freitas, C., Negrão, E., Flor de Lima, B., Correia da Silva, M., Madureira, A. J., Ramos, I., Hespanhol, V., Costa, J. L., Cunha, A., & Oliveira, H. P.
- Affiliations
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal
- Universidade do Porto (PT)
- INESC TEC (PT)
- University of California, San Francisco (US)
- Hospital de São João (PT)
- CHUSJ—Centro Hospitalar e Universitário de São João, 4200-319 Porto, Portugal
- FMUP—Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
- University of Trás-os-Montes and Alto Douro (PT)
- ORCID
- Venue
- Healthcare
- Publisher
- MDPI AG
- Published
- 2021-06-30
- Volume
- 9
- Issue
- 7
- Pages
- 827
- ISSN
- 2227-9032 (ISSN-L)
- PMC
- PMC8303863
- OpenAlex
- W3175382607
- Cited by
- 54
- References
- 69
- Citations by year
- 2026: 8 · 2025: 16 · 2024: 18 · 2023: 6 · 2022: 5 · 2021: 1
- FWCI
- 2.12
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Healthcare (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eScholarship (California Digital Library) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Funders
- Fundação para a Ciência e a Tecnologia (01-0145, COMPETE, POCI-01-0145, POCI-01-0145-FEDER-030263, POCI-01-0145-FEDER-, COMPETE_2020, POCI-01-)
- European Regional Development Fund (COMPETE, POCI-01, 01-0145, COMPETE2020, POCI-01-0145, POCI-01-0145- FEDER)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Keywords
- biomedical data sharing, medical imaging data, artificial intelligence in healthcare, data scarcity, data privacy, data access restrictions, synthetic data generation, transfer learning, blockchain, data anonymization
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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