Security and Privacy Issues in Biomedical AI Systems and Potential Solutions

Niranjana, G.; Chatterjee, Deya

doi:10.1201/9781003045564-13

Cited by 5 publications

(4 citation statements)

References 1 publication

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“…The ways in which data privacy can be violated are, among others, indirect data leakage, data poisoning, linkage attacks, dataset reconstruction from published results, adversarial examples, transferability attacks, model theft, etc. (Niranjana & Chatterjee, 2021).…”

Section: Data Securitymentioning

confidence: 99%

“…Data privacy is a central issue in training and testing AI models, including many different strategies developed to achieve data security. Finally, testing AI models in real-time clinical situations in order to further understand the fragility of such models and where their vulnerabilities can be exploited, can be devised to counter the problem (Niranjana & Chatterjee, 2021).…”

Section: Data Securitymentioning

confidence: 99%

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Ethical Issues Arising from the Use of AI in Drug Discovery

Livieri,

Anastasopoulou,

Briola

et al. 2024

jpentai

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Lately, the pharmaceutical industry has been implementing artificial intelligence (AI) in research in multiple ways for the purpose of drug discovery. AI has great benefits to offer by performing tasks that a human may not be capable of. However, it also raises many ethical issues that need to be resolved. After describing the general picture, this paper explores the benefits and disadvantages of AI and attempts to find answers to the challenges it poses in order to make sure that its use will be ethical, fair, unbiased and beneficial to people. The challenges addressed below focus on ensuring patients’ safety from biased AI algorithms and violation of their data privacy, the need to train and inform staff properly, as well as the necessity of achieving an optimal level of trust towards AI. In the end, some policy recommendations are being presented, aiming to contribute to the centralized approach and to the balanced sense among the uses of AI. The key to success is always taking into account human agency in order to make AI’s use transparent and robust, while minimizing its risks.

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Section: Data Securitymentioning

confidence: 99%

Section: Data Securitymentioning

confidence: 99%

Ethical Issues Arising from the Use of AI in Drug Discovery

Livieri,

Anastasopoulou,

Briola

et al. 2024

jpentai

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“…In addition, medical multimedia (2D and 3D images, bio-signals, etc.) can be distorted when EPFs are exchanged via IoMTs [14]. Moreover, the transmission of EPFs can be unsecured, which causes a serious risk since EPFs contain confidential medical information about the patients [15].…”

Section: Introductionmentioning

confidence: 99%

Efficient Biomedical Signal Security Algorithm for Smart Internet of Medical Things (IoMTs) Applications

et al. 2022

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Due to the rapid development of information and emerging communication technologies, developing and implementing solutions in the Internet of Medical Things (IoMTs) field have become relevant. This work developed a novel data security algorithm for deployment in emerging wireless biomedical sensor network (WBSN) and IoMTs applications while exchanging electronic patient folders (EPFs) over unsecured communication channels. These EPF data are collected using wireless biomedical sensors implemented in WBSN and IoMTs applications. Our algorithm is designed to ensure a high level of security for confidential patient information and verify the copyrights of bio-signal records included in the EPFs. The proposed scheme involves the use of Hahn’s discrete orthogonal moments for bio-signal feature vector extraction. Next, confidential patient information with the extracted feature vectors is converted into a QR code. The latter is then encrypted based on a proposed two-dimensional version of the modified chaotic logistic map. To demonstrate the feasibility of our scheme in IoMTs, it was implemented on a low-cost hardware board, namely Raspberry Pi, where the quad-core processors of this board are exploited using parallel computing. The conducted numerical experiments showed, on the one hand, that our scheme is highly secure and provides excellent robustness against common signal-processing attacks (noise, filtering, geometric transformations, compression, etc.). On the other hand, the obtained results demonstrated the fast running of our scheme when it is implemented on the Raspberry Pi board based on parallel computing. Furthermore, the results of the conducted comparisons reflect the superiority of our algorithm in terms of robustness when compared to recent bio-signal copyright protection schemes.

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“…Here this research extends the method by using the HMM so that the tumor classification based on previous and subsequent segmentation results will be concluded. This approach uses probabilistic reasoning over time and space for brain tumor segmentation from 4D MRI [8][9][10][11][12]. The diagnosis of brain tumor and CSF liquid usually requires image segmentation to evaluate tumor detection in brain images.…”

Section: Introductionmentioning

confidence: 99%

Hybrid GrabCut Hidden Markov Model for Segmentation

Saeed¹,

Abdullah²,

Jhanjhi³

et al. 2022

Computers, Materials &Amp; Continua

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Diagnosing data or object detection in medical images is one of the important parts of image segmentation especially those data which is less effective to identify in MRI such as low-grade tumors or cerebral spinal fluid (CSF) leaks in the brain. The aim of the study is to address the problems associated with detecting the low-grade tumor and CSF in brain is difficult in magnetic resonance imaging (MRI) images and another problem also relates to efficiency and less execution time for segmentation of medical images. For tumor and CSF segmentation using trained light field database (LFD) datasets of MRI images. This research proposed the new framework of the hybrid k-Nearest Neighbors (k-NN) model that is a combination of hybridization of Graph Cut and Support Vector Machine (GCSVM) and Hidden Markov Model of k-Mean Clustering Algorithm (HMMkC). There are four different methods are used in this research namely (1) SVM, (2) GrabCut segmentation, (3) HMM, and (4) k-mean clustering algorithm. In this framework, on the one hand, phase one is to perform the classification of SVM and Graph Cut algorithm to create the maximum margin distance. This research use GrabCut segmentation method which is the application of the graph cut algorithm and extract the data with the help of scaleinvariant features transform. On the other hand, in phase two, segment the low-grade tumors and CSF using a method adapted for HMkC and extract the information of tumor or CSF fluid by GCHMkC including iterative conditional maximizing mode (ICMM) with identifying the range of distant. Comparative evaluation is also performing by the comparison of existing techniques in this research. In conclusion, our proposed model gives better results than existing. This proposed model helps to common man and doctor that can identify their condition of brain easily. In future, this will model will use for other brain related diseases.

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Security and Privacy Issues in Biomedical AI Systems and Potential Solutions

Cited by 5 publications

References 1 publication

Ethical Issues Arising from the Use of AI in Drug Discovery

Ethical Issues Arising from the Use of AI in Drug Discovery

Efficient Biomedical Signal Security Algorithm for Smart Internet of Medical Things (IoMTs) Applications

Hybrid GrabCut Hidden Markov Model for Segmentation

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