Abstract
Multimodality refers to the utilization of different data types with different representational modes. Medical and health data are becoming more and more multimodal. Emerging multimodal technologies enable users to access, integrate and process multi-modal data and interact with a system in different modalities at the same time. Multimodal artificial intelligence (AI) particularly attempts to process, manage and understand these multimodal data through making multimodal inferences. In biology, medicine, and health, multimodal AI can assist in analyzing complex associations and relationships between various biological processes, health indicators, risk factors, and health outcomes, and developing exploratory and explanatory models. This chapter aims to introduce the concept of multimodal AI and discuss some of its applications in health and biomedicine.
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References
Huang, S. C., Pareek, A., & Seyyedi, S., et al. (2020). Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines. npj Digital Medicine, 3, 136.
Shaban-Nejad, A., & Michalowski, M. (2020). Precision health and medicine—A digital revolution in healthcare. Studies in Computational Intelligence, 843, Springer, ISBN 978-3-030-24408-8.
Shaban-Nejad, A., Michalowski, M., Peek, N., Brownstein, J. S., & Buckeridge, D. L. (2020). Seven pillars of precision digital health and medicine. Artificial Intelligence in Medicine, 103, 101793.
Shaban-Nejad, A., Michalowski, M., Brownstein, J. S., & Buckeridge, D. L. (2021). Guest editorial explainable AI: Towards fairness, accountability, transparency and trust in healthcare. IEEE Journal of Biomedical Health Informatics, 25(7), 2374–2375.
Shaban-Nejad, A., Michalowski, M., Buckeridge, D. L. (2021). Explainability and interpretability: Keys to deep medicine. In A. Shaban-Nejad, M. Michalowski, D. L. Buckeridge (Eds.), Explainable AI in healthcare and medicine (Vol. 914). Studies in computational intelligence. Springer, Cham. https://doi.org/10.1007/978-3-030-53352-6_1
Mamiya, H., Shaban-Nejad, A., Buckeridge, D. L. (2017). Online public health intelligence: Ethical considerations at the big data era. In A. Shaban-Nejad, J. Brownstein, D. Buckeridge (Eds.), Public health intelligence and the internet. Lecture notes in social networks. Springer, Cham. https://doi.org/10.1007/978-3-319-68604-2_8
Santosh, K. C. (2020). AI-driven tools for coronavirus outbreak: Need of active learning and cross-population train/test models on multitudinal/multimodal data. Journal of Medical Systems, 44(5), 1–5.
Chen, J., & See, K. C. (2020). Artificial intelligence for COVID-19: Rapid review. Journal of Medical Internet Research, 22(10), e21476.
Brakefield, W. S., Ammar, N., & Shaban-Nejad, A. (2021). UPHO: Leveraging an explainable multimodal big data analytics framework for COVID-19 surveillance and research. In 2021 IEEE International Conference on Big Data (Big Data) (pp. 5854–5858). https://doi.org/10.1109/BigData52589.2021.9671429
Brakefield, W. S., Ammar, N., Olusanya, O. A., & Shaban-Nejad, A. (2021). An urban population health observatory system to support COVID-19 pandemic preparedness, response, and management: Design and development study. JMIR Public Health and Surveillance, 7(6), e28269. https://doi.org/10.2196/28269
Mason, A. E., Hecht, F. M., Davis, S. K., et al. (2022). Detection of COVID-19 using multimodal data from a wearable device: Results from the first TemPredict Study. Science and Reports, 12(1), 3463. https://doi.org/10.1038/s41598-022-07314-0.Erratum.In:SciRep.2022Mar16;12(1):4568
Domingo-Fernández, D., Baksi, S., Schultz, B., Gadiya, Y., Karki, R., Raschka, T., Ebeling, C., Hofmann-Apitius, M., & Kodamullil, A. T. (2021). COVID-19 Knowledge Graph: A computable, multi-modal, cause-and-effect knowledge model of COVID-19 pathophysiology. Bioinformatics, 37(9), 1332–1334. https://doi.org/10.1093/bioinformatics/btaa834
Naumov, V., Putin, E., Pushkov, S., et al. (2021). COVIDomic: A multi-modal cloud-based platform for identification of risk factors associated with COVID-19 severity. PLoS Computational Biology, 17(7), e1009183. https://doi.org/10.1371/journal.pcbi.1009183
Tan, T., Das, B., Soni, R., et al. (2022). Multi-modal trained artificial intelligence solution to triage chest X-ray for COVID-19 using pristine ground-truth, versus radiologists. Neurocomputing, 485, 36–46. 7 May 2022. https://doi.org/10.1016/j.neucom.2022.02.040
Chen, Y., Ouyang, L., Bao, F. S., Li, Q., Han, L., Zhang, H., Zhu, B., Ge, Y., Robinson, P., Xu, M., Liu, J., & Chen, S. (2021). A Multimodality machine learning approach to differentiate severe and nonsevere COVID-19: Model development and validation. Journal of Medical Internet Research, 23(4), e23948. https://doi.org/10.2196/23948
Brakefield, W. S., Ammar, N., & Shaban-Nejad, A. (2022). An urban population health observatory for disease causal pathway analysis and decision support: Underlying explainable artificial intelligence model. JMIR Formative Research, 6(7), e36055. https://doi.org/10.2196/36055
Ammar, N., & Shaban-Nejad, A. (2020). Explainable artificial intelligence recommendation system by leveraging the semantics of adverse childhood experiences: Proof-of-concept prototype development. JMIR Medical Informatics, 4;8(11), e18752.
Ammar, N., Zareie, P., Hare, M.E., Rogers, L., Madubuonwu, S., Yaun, J., & Shaban-Nejad, A. (2021). SPACES: Explainable multimodal ai for active surveillance, diagnosis, and management of adverse childhood experiences (ACEs). In 2021 IEEE International Conference on Big Data (Big Data) (pp. 5843–5847)
Boehm, K. M., Khosravi, P., Vanguri, R., Gao, J., & Shah, S. P. (2022). Harnessing multimodal data integration to advance precision oncology. Nature Reviews Cancer, 22(2), 114–126. https://doi.org/10.1038/s41568-021-00408-3.(2021)
Skrede, O. J., De Raedt, S., Kleppe, A., et al. (2020). Deep learning for prediction of colorectal cancer outcome: A discovery and validation study. Lancet, 395(10221), 350–360. https://doi.org/10.1016/S0140-6736(19)32998-8
Marechal, C., Mikołajewski, D., Tyburek, K., Prokopowicz, P., Bougueroua, L., Ancourt, C., & Węgrzyn-Wolska, K. (2019). Survey on AI-based multimodal methods for emotion detection. In J. Kołodziej, H. González-Vélez (Eds.), High-Performance modelling and simulation for big data applications. (Vol. 11400, pp. 307–324). Lecture notes in computer science. Cham: Springer. https://doi.org/10.1007/978-3-030-16272-6_11
Xiong, J., Li, F., Song, D., Tang, G., He, J., et al. (2022). Multimodal machine learning using visual fields and peripapillary circular OCT scans in detection of glaucomatous optic neuropathy. Ophthalmology, 129(2), 171–180. https://doi.org/10.1016/j.ophtha.2021.07.032
Ammar, N., Bailey, J. E., Davis, R. L., & Shaban-Nejad, A. (2021). Using a personal health library-enabled mHealth recommender system for self-management of diabetes among underserved populations: Use case for knowledge graphs and linked data. JMIR Formative Research, 16;5(3), e24738. https://doi.org/10.2196/24738
Ilias, L., & Askounis, D. (2022). Multimodal deep learning models for detecting dementia from speech and transcripts. Frontiers in Aging Neuroscience, 17(14), 830943. https://doi.org/10.3389/fnagi.2022.830943
Baltrusaitis, T., Ahuja, C., & Morency, L. P. (2019). Multimodal machine learning: A survey and taxonomy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(2), 423–443.
Tanwar, A., Zhang, J., Ive, J., Gupta, V., & Guo, Y. (2022). Unsupervised numerical reasoning to extract phenotypes from clinical text by leveraging external knowledge. In Multimodal AI in healthcare: A paradigm shift in health intelligence. Studies in computational intelligence. Springer.
Nanayakkara, G., Wiratunga, N., Corsar, D., Martin, K., & Wijekoon, A. (2022). Clinical dialogue transcription error correction using Seq2Seq models. In Multimodal AI in healthcare: A paradigm shift in health intelligence. Studies in computational intelligence. Springer.
Liu, Z., Krishnaswamy, P., & Chen, N. F. (2022). Domain-specific language pre-training for dialogue comprehension on clinical inquiry-answering conversations. In Multimodal AI in healthcare: A paradigm shift in health intelligence. Studies in computational intelligence. Springer.
Giyahchi, T., Singh, S., Harris, I., & Pechmann, C. (2022). Customized training of pretrained language models to detect post intents in online health support groups. In Multimodal AI in healthcare: A paradigm shift in health intelligence. Studies in computational intelligence. Springer.
Johnson, D., Dragojlovic, N., Kopac, N., Chen, Y., Lenzen, M., Le Huray, S., Pollard, S., Regier, D., Harrison, M., George, A., Carenini, G., Ng, R., & Lynd, L. (2022). EXPECT-NLP: An integrated pipeline and user interface for exploring patient preferences directly from patient-generated text. In Multimodal AI in healthcare: A paradigm shift in health intelligence. Studies in computational intelligence. Springer.
Jiang, Y., & Poellabauer, C. (2022). Medication error detection using contextual language models. In Multimodal AI in healthcare: A paradigm shift in health intelligence. Studies in computational intelligence. Springer.
Li, B., Sun, M., Yu, Y., Zhao, Y., Xiang, Z., & An, Z. (2022). Latent representation weights learning of the indefinite length of views for conception diagnosis. In Multimodal AI in healthcare: A paradigm shift in health intelligence. Studies in computational intelligence. Springer.
Vauvelle, A., Tomlinson, H., Sim, A., & Denaxas, S. (2022). Phenotyping with positive unlabelled learning for genome-wide association studies. In Multimodal AI in healthcare: A paradigm shift in health intelligence. Studies in computational intelligence. Springer.
Zadorozhny, K., Thoral, P., Elbers, P., & Cinà, G. (2022). Out-of-distribution detection for medical applications: Guidelines for practical evaluation. In Multimodal AI in healthcare: A paradigm shift in health intelligence. Studies in computational intelligence. Springer
Gupta, A., & Srivastava, B. (2022). A robust system to detect and explain public mask wearing behavior. In Multimodal AI in healthcare: A paradigm shift in health intelligence. Studies in computational intelligence. Springer.
Zhang, D. K., Toni, F., & Williams, M. (2022). A federated cox model with non-proportional hazards. In Multimodal AI in healthcare: A paradigm shift in health intelligence. Studies in computational intelligence. Springer.
Debnath, B., O’brien, M., Kumar, S., & Behera, A. (2022). A step towards automated functional assessment of activities of daily living. In Multimodal AI in healthcare: A paradigm shift in health intelligence. Studies in computational intelligence. Springer.
Valsson, S., & Arandjelovic, O. (2022). The interpretation of deep learning based analysis of medical images—An examination of methodological and practical challenges using chest X-ray data. In Multimodal AI in healthcare: A paradigm shift in health intelligence. Studies in computational intelligence. Springer.
Das, P., & Mazumder, D. H. (2022). Predicting drug functions from adverse drug reactions by multi-label deep neural network. In Multimodal AI in healthcare: A paradigm shift in health intelligence. Studies in computational intelligence. Springer.
Tavabi, N., & Lerman, K. (2022). Pattern discovery in physiological data with byte pair encoding. In Multimodal AI in healthcare: A paradigm shift in health intelligence. Studies in computational intelligence. Springer.
Cao, Y., Cao, P., Chen, H., Kochendorfer, K. M., Trotter, A. B., Galanter, W. L., Arnold, P. M., & Iyer, R. K. (2022). Predicting ICU admissions for hospitalized COVID-19 patients with a factor graph-based model. In Multimodal AI in healthcare: A paradigm shift in health intelligence. Studies in computational intelligence. Springer.
Melton, C. A., Bae, J., Olusanya, O. A., Brenas, J. H., Shin, E. K., & Shaban-Nejad, A. (2022). Semantic network analysis of COVID-19 vaccine related text from reddit. In Multimodal AI in healthcare: A paradigm shift in health intelligence. Studies in computational intelligence. Springer.
Bhambhoria, R., Saab, J., Uppa, S., Li, X., Yakimovich, A., Bhatti, J., Valdamudi, N. K., Moyano, D., Bales, M., Dolatabadi, E., & Kocak, S. A. (2022). Towards providing clinical insights on long Covid from twitter data. In Multimodal AI in healthcare: A paradigm shift in health intelligence. Studies in computational intelligence. Springer.
Zehtabian, S., Khodadadeh, S., Turgut, D., & Bölöni, L. (2022). Predicting infections in the Covid-19 pandemic—Lessons learned. In Multimodal AI in healthcare: A paradigm shift in health intelligence. Studies in computational intelligence. Springer.
Wang, L., & Chen, J. (2022). Improving radiology report generation with adaptive attention. In Multimodal AI in healthcare: A paradigm shift in health intelligence. Studies in computational intelligence. Springer.
Revanur, A., Dasari, A., Tucker, C. S., & Jeni, L. A. (2022). Instantaneous physiological estimation using video transformers. In Multimodal AI in healthcare: A paradigm shift in health intelligence. Studies in computational intelligence. Springer.
Huang, X., Wicaksana, J., Li, S., & Cheng, K. T. (2022). Automated vision-based wellness analysis for elderly care centers. In Multimodal AI in healthcare: A paradigm shift in health intelligence. Studies in computational intelligence. Springer.
Sehanobish, A., Brown, N., Daga, I., Pawar, J., Torres, D., Das, A., Becker, M., Herzog, R., Odry, B., & Vianu, R. (2022). Efficient extraction of pathologies from C-Spine radiology reports using multi-task learning. In Multimodal AI in healthcare: A paradigm shift in health intelligence. Studies in computational intelligence. Springer.
Xia, T., Han, J., & Mascolo, C. (2022). Benchmarking uncertainty quantification on biosignal classification tasks under dataset shift. In Multimodal AI in healthcare: A paradigm shift in health intelligence. Studies in computational intelligence. Springer.
Ul Haq, H., Kocaman, V., & Talby, D. (2022). Mining adverse drug reactions from unstructured mediums at scale. In Multimodal AI in healthcare: A paradigm shift in health intelligence. Studies in computational intelligence. Springer.
Viñas, R., Zheng, X., & Hayes, J. (2022). A graph-based imputation method for sparse medical records. In Multimodal AI in healthcare: A paradigm shift in health intelligence. Studies in computational intelligence. Springer.
Jana, S., Dasgupta, T., & Dey, L. (2022). Using nursing notes to predict length of stay in ICU for critically ill patient. In Multimodal AI in healthcare: A paradigm shift in health intelligence. Studies in computational intelligence. Springer.
Han, H. J., BN, S., Qiu, L., & Abdullah, S. (2022). Automatic classification of dementia using text and speech data. In Multimodal AI in healthcare: A paradigm shift in health intelligence. Studies in computational intelligence. Springer.
Hoang, T., Nguyen, T. T., & Nguyen, H. D. (2022). Unified tensor network for multimodal dementia detection. In Multimodal AI in healthcare: A paradigm shift in health intelligence. Studies in computational intelligence. Springer.
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Shaban-Nejad, A., Michalowski, M., Bianco, S. (2023). Multimodal Artificial Intelligence: Next Wave of Innovation in Healthcare and Medicine. In: Shaban-Nejad, A., Michalowski, M., Bianco, S. (eds) Multimodal AI in Healthcare. Studies in Computational Intelligence, vol 1060. Springer, Cham. https://doi.org/10.1007/978-3-031-14771-5_1
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