Utilizing information from multiple sources is a preferred and more precise method for medical experts to confirm a diagnosis. Each source provides critical information about the disease that might otherwise be absent in other modalities. Combining information from various medical sources boosts confidence in the diagnosis process, enabling the creation of an effective treatment plan for the patient. The scarcity of medical experts to diagnose diseases motivates the development of automatic diagnoses relying on multimodal data. With the progress in artificial intelligence technology, automated diagnosis using multimodal fusion techniques is now possible. Nevertheless, the concept of multimodal medical diagnosis is still new and requires an understanding of the diverse aspects of multimodal data and its related challenges. This review article examines the various aspects of multimodal medical diagnosis to equip readers, academicians, and researchers with necessary knowledge to advance multimodal medical research. The chosen articles in the study underwent thorough screening from reputable journals and publishers to offer high-quality content to readers, who can then apply the knowledge to produce quality research. Besides, the need for multimodal information and the associated challenges are discussed with solutions. Additionally, ethical issues of using artificial intelligence in medical diagnosis is also discussed.

Lung disease is one of the leading causes of death worldwide. This emphasizes the need for early diagnosis in order to provide appropriate treatment and save lives. Physicians typically require information about patients’ clinical symptoms, various laboratory and pathology tests, along with chest X-rays to confirm the diagnosis of lung disease. In this study, we present a transformer-based multimodal deep learning approach that incorporates imaging and clinical data for effective lung disease diagnosis on a new multimodal medical dataset. The proposed method employs a cross-attention transformer module to merge features from the heterogeneous modalities. Then unified fused features are used for disease classification. The experiments were performed and evaluated on several classification metrics to illustrate the performance of the proposed approach. The study’s results revealed that the proposed method achieved an accuracy of 95% in terms of accurate classification of tuberculosis and outperformed other traditional fusion methods on multimodal tuberculosis data used in this study.

Background Expansion of Internet and its use for on-line activities such as E-Commerce and social networking are producing large volumes of transactional data. This huge data volume resulted from these activities facilitates the analysis and understanding of global trends and interesting patterns used for several decisive purposes. Analytics involved in these processes expose sensitive information present in these datasets, which is a serious privacy threat. To overcome this challenge, few sequential heuristics have been used in past where volumes of data were comparatively accommodating to these sequential heuristics; the current situation is not that much in-line and often results in high execution time. This new challenge of scalability paves a way for experimenting with Big Data approaches (e.g., MapReduce Framework). We have agglomerated the MapReduce framework with adopted heuristics to overcome this challenge of scalability along with much-needed privacy preservation and yields efficient analytic results within bounded execution times. Methods MapReduce is a parallel programming framework [16] which provides us the opportunity to leverage largely distributed resources to deal with the Big Data analytics. MapReduce allows the resource of a largely distributed system to be utilized in a parallel fashion. The simplicity and high fault-tolerance are the key features which make MapReduce a promising framework. Therefore, we have proposed a two-phase MapReduce version of these adopted heuristics. MapReduce framework divides the whole data into ‘n’ number of data chunks D = {d 1 d ∪ 2 ∪ d 3 ..... ∪ d n } and distributes them over ‘n’ computing nodes to achieve the parallelization. The first phase of MapReduce job runs on each data chunk in order to generate intermediate results, which are further sorted and merged in the second phase to generate final sanitized dataset. Results We conducted three set of experiments, each with five different scenarios corresponding to the different cluster sizes i.e., n = 1,2,3,4,5 where ‘n’ is a number of computing nodes. We compared the approaches with respect to real as well as synthetically generated large datasets. For varying data sizes and varying number of computing nodes, it has been observed that sanitization time required by the MapReduce-based algorithm for same size dataset is much less than the sequential traditional approach. Further, the scalability can be improved by using more number of computing nodes. Lastly, another set of experiments explores the change in sanitization time with varying sizes of the sensitive content present in a dataset. We evaluated the effectiveness of proposed approach in different scenarios, with varying cluster size from 1 to 5 nodes. It has been observed that still the execution time of our approach is much less than traditional schemes. Further, no hiding failure, artifactual patterns have been observed during the experiments as well as in terms of misses cost also the MapReduce version performance is same as of traditional approaches. Conclusion Traditional approaches for data hiding primarily MaxFIA and SWA were lacking with due inability to tackle large voluminous data. To subjugate the new challenge of scalability, we have implemented these basic heuristics with Big Data approach i.e., MapReduce framework. Quantitative evaluations have shown that the fusion of MapReduce framework with these adopted heuristics fulfills its obligatory responsibility of being scalable and many-fold faster for yielding efficient analytic results.