Medical Big Data and Its Benefits

Taking a byte out of big data

Tele-radiology is a technology that helps in bringing the communication between the radiologist, patients and healthcare units situated at distant places. This involves exchange of medical centric data. The medical data may be stored as Electronic Health Records (EHR). These EHRs contain X-Rays, CT scans, MRI reports. Hundreds of scans across multiple radiology centers lead to medical big data (MBD). Healthcare Cloud can be used to handle MBD. Since lack of security to EHRs can cause havoc in medical IT, healthcare cloud must be secure. It should ensure secure sharing and storage of EHRs. This paper proposes the application of decoy technique to provide security to EHRs. The EHRs have the risk of internal attacks and external intrusion. This work addresses and handles internal attacks. It also involves study on honey-pots and intrusion detection techniques. Further it identifies the possibility of an intrusion and alerts the administrator. Also the details of intrusions are logged.

Photo by Josh Riemer on Unsplash
 Introduction
 With the rapid advancements in neurotechnological machinery and improved analytical insights from machine learning in neuroscience, the availability of big brain data has increased tremendously. Neurological health research is done using digitized brain data.[1] There must be adequate data governance to secure the privacy of subjects participating in brain research and treatments. If not properly regulated, the research methods could lead to significant breaches of the subject’s autonomy and privacy. This paper will address the necessity for neuroprotection laws, which effectively govern the use of big brain data to ensure respect for patient privacy and autonomy.
 Background
 Artificial intelligence and machine learning can be integrated with neuroscience big brain data to drive research studies. This integrative technology allows patterns of electrical activity in neurons to be studied in detail.[2]Specifically, it uses a robotic system which can reason, plan, and exhibit biologically intelligent behavior. Machine learning is a method of computer programming where the code can adapt its behavior based on big brain data.[3] The big brain data is the collection of large amounts of information for the purpose of deciphering patterns through computer analysis using machine learning.[4] The information that these technologies provide is extensive enough to allow a researcher to read a patient’s mind. AI and machine learning technologies work by finding the underlying structure of brain data, which is then described by patterns known as latent factors, eventually resulting in an understanding of the brain’s temporal dynamics.[5]
 Through these technologies, researchers are able to decipher how the human brain computes its performances and thoughts. However, due to the extensive and complex nature of the data processed through AI and machine learning, researchers may gain access to personal information a patient may not wish to reveal. From a bioethical lens, tensions arise in the realm of patient autonomy. Patients are not able to control the transmission of data from their brains that is analyzed by researchers. Governing brain data through laws may enhance the extent of patient privacy in the case where brain data is being used through AI technologies.[6] A responsible approach to governing brain data would require a sophisticated legal structure.
 Analysis
 Impact on Patient Autonomy and Privacy 
 In research pertaining to big brain data, the consent forms do not fully cover the vast amounts of information that is collected. According to research, personal data has become the most sought out commodity to provide content to corporations and the web-based service industry. Unfortunately, data leaks that release private information frequently occur.[7] The storage of an individual’s data on technologies accessible on the internet during research studies makes it vulnerable to leaks, jeopardizing an individual’s privacy. These data leaks may cause the patient to be identified easily, as the degree of information provided by AI technologies are personalized and may be decoded through brain fingerprinting methods.[8]
 There has been an extensive growth in the development and use of AI. It is efficient in providing information to radiologists who diagnose various diseases including brain cancer and psychiatric disease, and AI assists in the delivery of telemedicine.[9] However, the ethical pitfall of reduced patient autonomy must be addressed by analyzing current AI technologies and creating more options for patient preference in how the data may be used. For instance, facial recognition technology[10] commonly used in health care produces more information than listed in common consent forms, threatening to undermine informed consent. Facial recognition software collects extensive data and may disclose more information than a person would prefer to provide despite being a useful tool for diagnosing medical and genetic conditions.[11] In addition, people may not be aware that their images are being used to generate more clinical data for other purposes. It is difficult to guarantee the data is anonymized. Consent requirements must include informing people about the complexity of the potential uses of the data; software developers should maximize patient privacy.[12] Furthermore, there is a “human element” in the use of AI technologies as medical providers control the use and the extent to which data is captured or accessed through the AI technologies.[13] People must understand the scope of the technology and have clear communication with the physician or health care provider about how the medical information will be used. 
 Existing Laws for Brain Data Governance 
 A strict system of defined legal responsibilities of medical providers will ensure a higher degree of patient privacy and autonomy when AI technologies and data from machine learning are used. Governing specific algorithmic data is crucial in safeguarding a patient’s privacy and developing a gold standard treatment protocol following the procurement of the information.[14] Certain AI technologies provide more data than others, and legal boundaries should be established to ensure strong performance, quality control, and scope for patient privacy and autonomy. For instance, currently AI technologies are being used in the realm of intensive neurological care. However, there is a significant level of patient uncertainty about how much control patients have over the data’s uses.[15] Calibrated legal and ethical standards will allow important brain data to be securely governed and monitored.
 Once brain signals are recorded and processed from one individual, the data may be merged with other data in Brain Computer Interface Technology (BCI).[16] To ensure a right and ability to retrieve personal data or pull it from the collection, specific regulations for varying types of data are needed.[17] The importance of consent and patient privacy must be considered through giving patients a transparent view of how brain data is governed.[18] The legal system must address discriminatory issues and risks to patients whose data is used in studies. Laws like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Protection Act (CCPA) can serve as effective models to protect aggregated data. These laws govern consumer information and ensure the compliance when personal data is collected.[19] California voters recently approved expansion of the CCPA to health data. The Washington Privacy Act, which would have provided rights to access, change, and withdraw personal data, failed to pass. Other states should improve privacy as well,[20] although a federal bill would be preferable. Scientists at the Heidelberg Academy of Sciences argue for data security to be governed in a manner that balances patient privacy and autonomy with the commercial interests of researchers.[21] The balance could be achieved through privacy protections like those in the Washington Privacy Act. Although the Health Insurance Portability and Accountability Act (HIPAA) provides an overall framework to deter the likelihood of dangers to patient protection and privacy, more thorough laws are warranted to combat pervasive data transfer and analysis that technology has brought to the health care industry.[22] Breaches of patient privacy under current HIPAA regulations include releasing patient information to a reporter without their consent and sending HIV data to a patient’s employer without consent.[23] HIPAA does not cover information being shared with outside contractors who do not have an agreement with technology companies to keep patient data confidential. HIPAA regulations also do not always address blatant breaches on patient data confidentiality.[24] Patients must be provided with methods to monitor the data being analyzed to be able to view the extent of private information being generated via AI technologies. In health research, the medical purposes of better diagnosis, earlier detection of diseases, or prevention are ethical justifications for the use of the data if it was collected with permission, the person understood and approved the uses of the data, and the data was deidentified.
 A standard governance framework is required in providing the fairest system of care to patients who allow their brain data to be examined. Informed consent in the neuroscience field could reaffirm the privacy and autonomy of patients by ensuring that they understand the type of information collected. Laws also could protect data after a patient’s death. Malpractice in the scope of brain data could give people a cause of action critical in safeguarding patient’s rights. Data breach lawsuits will become common but generally do not cover deidentified data that becomes part of big data collection. A more synchronized approach to the collection and consent process will encourage an understanding of how big data is used to diagnose and treat patients. Some altruistic people may even be more likely to consent if they know the largescale data collection is helpful to treat and diagnose people. Others should have the ability to opt out of sharing neurological data, especially when there is not certainty surrounding deidentification.[25]
 Conclusion
 Artificial intelligence and machine learning technologies have the potential to aid in the diagnosis and treatment of people globally by extracting and aggregating brain data specific to individuals. However, the secure use of the data is necessary to build trust between care providers and patients, as well as in balancing the bioethical principles of beneficence and patient autonomy. We must ensure the highest quality of care to patients, while protecting their privacy, informed consent, and clinical trust. More sophis

There is no universally accepted definition of big data, yet it is characterized by widely recognized attributes. Medical big data embodies all these characteristics. These attributes present certain challenges for big data and its applications. We elaborates on medical big data and its interpretation, encompassing three representative of medical big data that exhibit distinct compositional features. We discuss misconceptions in medical big data applications emphasizing that the implementation of big data requires technical analysis, and address unique technological, legal, and ethical challenges. Here we focus on core issues and controversies in medical big data, particularly the challenges of confirming property rights and potnetial solutions, with special attention paid to Aggregated Data rights. We also analyze the applicability of traditional intellectual property frameworks and point out the impact of technological advancements on right confirmation.

With the accelerating pace of human work and life, as well as the trend of aging population, the construction of smart communities has received more and more attention. Internet technology has promoted the health care service model. Community health information sharing platform has become an indispensable part of smart communities. At present, the health management industry has developed rapidly, but there are many problems. Therefore, it is necessary to establish a smart community health information sharing platform with creativity, strong cooperation ability, solid theoretical construction and multi-level in-depth decision-making analysis. This study first examined the conceptual significance and current status of intelligent community health information sharing platforms, explored the role of blockchain in the health and medical big data platform, and then proposed management strategies in the context of medical big data. The application of an advanced computing intelligence system based on secure medical images and the improvement of the open data supply of the community health information sharing platform were the two angles from which the management policy of a smart community health information sharing platform based on big data was proposed. Relevant algorithms for the management of smart community health information sharing platform under medical big data were mentioned. Finally, community A was selected to implement the methods provided in this paper and compared with community B that did not implement the management methods in this paper. It was concluded that the management of community A was 7.52% higher than that of community B. The easy, intelligent, and secure community health service system could be constructed to better protect the health services of community members through the management and countermeasure analysis of the smart community health information sharing platform based on medical big data.

In the field of medicine and healthcare, giant medical and healthcare information is created by medical institutions, medical information platforms, digital medical equipments, intelligent health electronic products, wearable personal health equipment, and other health-related information platforms. The sum of these information forms the medical and healthcare big data. The development and application of this big data not only can improve the medical and health system, but also can promote the development of health industry, which has realistic and long-term strategic significance to national economic development. In this paper, the basic characteristics of medical and healthcare big data were introduced, and the problems and challenges in the application of this big data in China were analyzed. In order to improve the level of informatization and management efficiency of medical and healthcare management in China, a national medical and healthcare big data platform was proposed, which can integrate omni-directional and full-cycle health-related information of institutions and individuals in China. In addtion, the construction mode, operation mode, data source and platform function of this platform were introduced. Key words: Medical and healthcare big data; Medical and health management; Big data applications; Big data function

Big Data has attracted much attention in academia and industry fields in the last few years. The trend of utilizing medical big data has increased tremendously as well. The medical big data is generated from medical records, medicine, research, etc. but it is not well connected due to its long-term development in last decades in most hospitals without high-level strategic guide and plan. The information missing, data dispersivity, information isolated island, redundancy problems are key to the failure of the big data platform which is used to manage the medical data asset. We propose a platform called Data Mid-Platform (DMP) to solve the above problems and serve other big data projects as well as scientific assignments. The feasibility of this mid-platform is highly recommended and certified by several domain experts and it is under construction in a real project. It aims to satisfy at least 3 to 5 years big data applications development under the perspective of big data top-level design.

In the current social environment, the generation and application of medical big data is closely related to the personal information security of every citizen. With the continuous development of medical technology, the application of medical big data has become increasingly widespread. On one hand, it can promote the development of medical technology, including drug development, personalized medicine, and precision treatment. On the other hand, medical big data also concerns the private information of citizens, which is a crucial part of personal information protection. However, in practice, there often exists a contradiction between medical big data and the protection of citizens' personal information rights. From the perspective of medical big data, there are overlaps and differences among citizens' personal information, privacy information, medical health information, and medical big data information. Relying solely on the “informed consent” rule for personal information cannot effectively protect personal information comprehensively or guarantee the application and development of medical big data. This article analyzes the issues surrounding Chinese medical big data in terms of personal information from a legal perspective, and proposes corresponding solutions to these issues. It is hoped that this research can contribute to advancing the legalization of medical data openness, re-examining patients' personal information in the context of medical big data, and balancing the conflicts between individual privacy interests, public interests, and economic benefits of medical data, thus better protecting individuals' personal information interests under social and private benefits.

Medical imaging has evolved from a pure visualization tool to representing a primary source of analytic approaches towards in vivo disease characterization. Hybrid imaging is an integral part of this approach, as it provides complementary visual and quantitative information in the form of morphological and functional insights into the living body. As such, non-invasive imaging modalities no longer provide images only, but data, as stated recently by pioneers in the field. Today, such information, together with other, non-imaging medical data creates highly heterogeneous data sets that underpin the concept of medical big data. While the exponential growth of medical big data challenges their processing, they inherently contain information that benefits a patient-centric personalized healthcare. Novel machine learning approaches combined with high-performance distributed cloud computing technologies help explore medical big data. Such exploration and subsequent generation of knowledge require a profound understanding of the technical challenges. These challenges increase in complexity when employing hybrid, aka dual- or even multi-modality image data as input to big data repositories. This paper provides a general insight into medical big data analysis in light of the use of hybrid imaging information. First, hybrid imaging is introduced (see further contributions to this special Research Topic), also in the context of medical big data, then the technological background of machine learning as well as state-of-the-art distributed cloud computing technologies are presented, followed by the discussion of data preservation and data sharing trends. Joint data exploration endeavours in the context of in vivo radiomics and hybrid imaging will be presented. Standardization challenges of imaging protocol, delineation, feature engineering and machine learning evaluation will be detailed. Last, the paper will provide an outlook into the future role of hybrid imaging in view of personalized medicine, whereby a focus will be given to the derivation of prediction models as part of clinical decision support systems, to which machine learning approaches and hybrid imaging can be anchored.

<sec> <title>BACKGROUND</title> The rapid integration of artificial intelligence (AI) and medical big data into health care is transforming diagnostic accuracy, treatment planning, and research capabilities. Despite these developments, formal AI and medical big data education is limited in undergraduate medical curricula, particularly in China. </sec> <sec> <title>OBJECTIVE</title> This study aimed to investigate clinical medical undergraduates’ familiarity with AI and medical big data, their perceived need for relevant courses, and their preferred curriculum design and assessment methods. </sec> <sec> <title>METHODS</title> A cross-sectional, web-based survey was administered to students in the five-year clinical medicine program at a medical university in China from January 10 to January 17, 2025.The self-administered questionnaire was developed through a comprehensive literature review, and its content validity was assessed by experts in medical education and artificial intelligence.Descriptive statistics summarized participant demographics and survey responses. Two-way-ANOVA was performed to evaluate differences in grade and gender. </sec> <sec> <title>RESULTS</title> A total of 871 clinical medicine undergraduates participated in the study (62.6% female).While 36% of the students agreed or strongly agreed that they were familiar with the application of artificial intelligence and medical big data in the healthcare field, only 32.6% had prior learning experience in this area.A total of 94% of the students indicated that a course on artificial intelligence and medical big data is needed in their undergraduate curriculum.65% of the students indicated that if a course on artificial intelligence and medical big data is offered, they would prefer personalized teaching based on the textbook, and 73% expressed a preference for open-book examinations. Significant differences in perceptions and curriculum preferences were observed across gender and grade (P &lt; 0.05). </sec> <sec> <title>CONCLUSIONS</title> This study reveals a strong demand among clinical medicine undergraduates for AI and medical big data education. Even with limited prior experience,students showed clear interest and preferences,emphasizing the need for curriculum adjustments to forster data-driven health care. </sec>

As the healthcare environment is being digitalized and changed rapidly, research using medical big data is increasing. One of the most applicable data is electronic medical records which can provide a large amount of clinically practical meaning. Electronic medical data include patient’s demographic information, laboratory test results, imaging and biosignal data. In this article, we provide support for a wide variety of researchers in their efforts to use electronic medical record data accurately and usefully in their work. From the basic concept of the research using electronic medical records to challenging aspects like data integration between multiple institutions are described. Also, examples of each type of data are covered; structured such as numeric data and unstructured such as images, biosignals and narrative text. Using these kinds of electronic medical records, analyses are processed by data cleansing, transforming, and reducing in order. Many kinds of variables such as the exposure and outcome of interest, covariate and the research design can be chosen during the preprocessing. As many machine-learning-based studies as well as epidemiologic-based studies have been conducted using electronic medical records, various research frameworks have been proposed. However, data quality management and data standardization for multicenter data analysis are still remaining as challenging tasks.

Medical big data is accumulated numerous medical related data day by day. These data may have tips for new approach for drug development. Authors tried to find drug-development-needs in children using medical big data analysis with prescription survey. Medical big data were provided from JMDC (Japan Medical Data Centre) Inc. about 3 million participants between January 2005 and June 2017. In these, we identified randomly identified 22787 participants from 466701 participants who are aged ≤11 years. In these participants, 9644 were administered "capsule," "tablet," "orally distegrating tablet," "controlled release tablet/capsule" or "enteric coated tablet" formula drugs. In these, 514 were administered these as powderization or decapsulation. Sixty components administered in 145 participants (28.2%) are not marketed for pediatric formula. On the other hands, 92 components administered in 369 participants (71.8%) are decapsulation or powderization, though pediatric formulas are marketed. These 152 components may have a development seeds for children. In conclusion, prescription survey using medical big data may partially resolve the drug-development-need in pediatrics because by using medical big data will leads low biased data depending each institution.

A deep learning model for predicting chemical composition of gallstones with big data in medical Internet of Things

BackgroundThe field of “Big Health,” which encompasses the integration of big data in healthcare, has seen rapid development in recent years. As big data technologies continue to transform healthcare, understanding emerging trends and key advancements within the field is essential.MethodsWe retrieved and filtered articles and reviews related to big data analytics in health research from the Web of Science Core Collection, including SCI Expanded and SSCI, covering the period from 2009 to 2024. Bibliometric and co-citation analyses were conducted using VOSviewer and CiteSpace.ResultsA total of 13,609 papers were analyzed, including 10,702 original research and 2,907 reviews. Co-occurrence word analysis identified six key research areas: (1) the application of big data analytics in health decision-making; (2) challenges in the technological management of health and medical big data; (3) integration of machine learning with health monitoring; (4) privacy and ethical issues in health and medical big data; (5) data integration in precision medicine; and (6) the use of big data in disease management and risk assessment. The co-word burst analysis results indicate that topics such as personalized medicine, decision support, and data protection experienced significant growth between 2015 and 2020. With the advancement of big data technologies, research hotspots have gradually expanded from basic data analysis to more complex application areas, such as the digital transformation of healthcare, digital health strategies, and smart health cities.ConclusionThis study highlights the growing impact of big data analytics in healthcare, emphasizing its role in decision-making, disease management, and precision medicine. As digital transformation in healthcare advances, addressing challenges in data integration, privacy, and machine learning integration will be crucial for maximizing the potential of big data technologies in improving health outcomes.

Medical Big Data (MBD) is the most critical form of information. MBD over a colossal Wireless Network (WN) is a sophisticated area of concern. Every day WN is becoming more dense and complicated. The MBD can be saved to wireless network after applying suitable Wavelet Compression. The quality of MBD is the most important factor; which means efficient and reliable transmission. Wavelet Compression ensures the best image compression schemes such that the quality of data is not affected. MBD is in the form of medical reports, x-rays, CT scan image, audio video files. MBD over WN needs limited bandwidth utilization. Software Define Network (SDN) divides the network infrastructure into two planes. The Control Plane manages data flow and the Data Plane deals with the data. In-network coordinating cache via SDN support is a better way of network efficiency improvement. The cache size is very limited and can entertain only a limited number of requests. Our proposed system for MBD Cache (MBDC) is efficient to improvement the network performance using Wavelets. It is capable to work in Heterogeneous Network (Het Net) and Software Define Network (SDN) Architecture. By considering any shortest path like K-Means algorithm, the neighboring-content cache awareness reduces the latency time to fulfill the request from destination.