Biomedical Computation Artificial Intelligence Challenges in Cloud Environments Volume 50- Issue 3

Philippe Funk*

• SMC Business School, Malta

Received: May 13, 2023;   Published: May 24, 2023

*Corresponding author: Philippe Funk, SMC Business School, Pope Urbanus VIII Street, B Kara BKR 1425, Malta

DOI: 10.26717/BJSTR.2023.50.007977

Abstract PDF

Biomedical computation poses unique challenges for artificial intelligence in cloud environments. With the vast amounts of medical data generated and collected every day, there is a growing need for powerful computational tools that can efficiently manage, analyze and interpret these data. However, the development of suitable AI algorithms and software solutions requires addressing several challenges, such as data privacy and security, interoperability of diverse healthcare systems, and optimization of cloudbased computing resources. In this abstract, we highlight some of the key challenges and opportunities in building robust and reliable AI systems for biomedical computation in cloud environments, and discuss promising solutions that could accelerate progress in this rapidly evolving field.

Keywords: Biomedical Computation; Artificial Intelligence; Cloud Environments

Abbreviations: AI: Artificial Intelligence; HIPAA: Health Insurance Portability and Accountability Act; FHIR: Fast Healthcare Interoperability Resources; GPUs: Graphics Processing Units

With the aim of using computational approaches to improve healthcare outcomes, the fields of biomedical computing and artificial intelligence (AI) have grown more closely linked in recent years. Artificial intelligence algorithms that help with diagnosis, therapy, and drug development are now feasible because of the processing and analysis of massive amounts of data provided by medical devices, electronic health records, and medical imaging. The sheer magnitude of these computations, however, means that they often exceed the capabilities of local devices, necessitating the use of remote servers. Researchers can handle and analyse massive volumes of data quickly and easily because of the scalable computing capabilities made available by cloud platforms. However, there are several difficulties associated with using cloud computing for AI and biomedical computation [1].

Methodology

The necessity for secure and private storage of data is a significant obstacle. In order to safeguard patients’ personal information, laws like the Health Insurance Portability and Accountability Act (HIPAA) have been enacted in the United States’ healthcare system. When healthcare data is stored and processed on servers outside of the organization’s control, the cloud poses new security threats. Protecting sensitive medical data necessitates that cloud service providers adhere to stringent security and privacy standards. Standardization in data formats and interoperability is another obstacle. Data in the healthcare business comes in many forms, making it challenging to combine and evaluate. Standardization of data formats and compatibility across multiple systems are prerequisites for cloud-based computing to serve as a platform for data integration and analysis. To overcome these problems, standards like the Fast Healthcare Interoperability Resources (FHIR) are being created and implemented. The intricacy of biological data and algorithms also presents difficulties. Clinical, imaging, and genetic data are just a few examples of the many various forms of information that may be found in the field of biomedicine. The artificial intelligence (AI) techniques used to evaluate this data are equally complicated, calling for tools like graphics processing units (GPUs) and software libraries designed specifically for training neural networks. Researchers may make use of cloud computing to get access to these tools, but they must first guarantee that their algorithms run efficiently on the cloud [2].

Another difficulty is the need for reasonably priced and effective computer resources. Significant computer resources are needed for biomedical computation and AI, which may be expensive when done on local workstations. Researchers may save money by using cloud computing, but they need to be mindful of how much they use these resources. Costs may be better managed with the aid of the various pricing models offered by cloud providers, such as pay-as-you-go and reserved instances. Finally, there is the difficulty of implementing AI algorithms in actual clinical practice. While artificial intelligence (AI) algorithms have shown promise in enhancing healthcare outcomes, there is still much to think about before they can be seamlessly integrated into clinical procedures. It’s not uncommon for doctors, nurses, and patients to all have a hand in the many processes that make up a clinical workflow. To optimize patient outcomes, AI algorithms should be developed to fit naturally into these processes. Biomedical computation as well as AI may greatly benefit from cloud computing; however, this model also has its drawbacks. Data privacy and security, standardization and interoperability, the complexity of biological data and algorithms, efficient and cost-effective computer resources, and integration into clinical processes are all examples of these difficulties [3]. Researchers, healthcare institutions, and cloud service providers will need to work together to find answers to these problems and implement them in a way that improves healthcare results while keeping patients’ personal information safe (Figure 1).

Figure 1.

Using computational approaches to enhance healthcare outcomes, biomedical computation and artificial intelligence (AI) have gained prominence in recent years. Artificial intelligence algorithms that help with diagnosis, therapy, and drug development are now feasible because of the processing and analysis of massive amounts of data provided by medical devices, electronic health records, and medical imaging. The sheer magnitude of these computations, however, means that they often exceed the capabilities of local devices, necessitating the use of remote servers. Researchers can handle and analyse massive volumes of data quickly and easily because of the scalable computing capabilities made available by cloud platforms. However, there are several difficulties associated with using cloud computing for AI and biomedical computation. Protecting people’s personal information and sensitive data is a huge obstacle. In order to safeguard patients’ personal information, laws like the Health Insurance Portability and Accountability Act (HIPAA) have been enacted in the United States’ healthcare system. When healthcare data is stored and processed on servers outside of the organization’s control, the cloud poses new security threats. Protecting sensitive medical data necessitates that cloud service providers adhere to stringent security and privacy standards. Standardization in data formats and interoperability is another obstacle. Data in the healthcare business comes in many forms, making it challenging to combine and evaluate. Standardization of data formats and compatibility across multiple systems are prerequisites for cloud-based computing to serve as a platform for data integration and analysis. To overcome these problems, standards like the Fast Healthcare Interoperability Resources (FHIR) are being created and implemented [4].

Another difficulty is the intricacy of biological data and algorithms. Clinical, imaging, and genetic data are just a few examples of the many various forms of information that may be found in the field of biomedicine. The artificial intelligence (AI) techniques used to evaluate this data are equally complicated, calling for tools like graphics processing units (GPUs) and software libraries designed specifically for training neural networks. Researchers may make use of cloud computing to get access to these tools, but they must first guarantee that their algorithms run efficiently on the cloud. Another difficulty is the need for reasonably priced and effective computer resources. Significant computer resources are needed for biomedical computation and AI, which may be expensive when done on local workstations. Researchers may save money by using cloud computing, but they need to be mindful of how much they use these resources. Costs may be better managed with the aid of the various pricing models offered by cloud providers, such as pay-as-you-go and reserved instances. Finally, there is the difficulty of implementing AI algorithms in actual clinical practice. While artificial intelligence (AI) algorithms have shown promise in enhancing healthcare outcomes, there is still much to think about before they can be seamlessly integrated into clinical procedures. It’s not uncommon for doctors, nurses, and patients to all have a hand in the many processes that make up a clinical workflow. To optimize patient outcomes, AI algorithms should be developed to fit naturally into these processes [5].

Researchers, healthcare institutions, and cloud service providers will need to work together to find answers to these problems and implement them in a way that improves healthcare results while keeping patients’ personal information safe. However, these problems must be carefully studied and solved before the full promise of cloudbased computing for biomedical computation and AI can be realized in healthcare.

Advantages

1. Scientists can swiftly and simply manage and analyses enormous datasets with the help of cloud computing due to its scalability. Researchers’ capacity to flexibly scale up or down their computer resources based on their needs may substantially cut the time and money required to manage big datasets.
2. Artificial intelligence (AI) and biomedical computing may be more cost-effective when performed on the cloud. The cloud’s flexible pricing models, such as pay-as-you-go or reserved instances, may help with cost management. Researchers only pay for the services they really utilize, and no expensive software or gear is necessary.
3. The cloud provides access to resources like graphics processing units (GPUs) and specific neural network topologies that you may not have on your local desktop. This allows universities to implement and optimize state-of-theart AI algorithms for specific purposes.

Disadvantages

1. Since cloud computing stores and processes information on machines that are not under the direct control of the healthcare provider, privacy and security problems arise. Cloud service providers must follow strict security and privacy regulations to protect confidential medical data.
2. The complexity of the associated algorithms increases with the number of different types of information that may be present in a biomedical environment. To process this data using artificial intelligence algorithms requires complex gear and software. Cloud computing provides researchers with access to these resources, but they must first ensure that their methods function effectively in the cloud.
3. The healthcare industry generates enormous amounts of data, but because of the many ways in which this data is kept and sent, it may be difficult to aggregate and analyses. For cloud computing to function as a platform for data integration and analysis, it is necessary to standardize data formats and ensure interoperability across numerous platforms.

Cloud computing’s ability to provide a framework for integrating and analysing data will grow in significance as the healthcare sector continues to create massive volumes of data. To guarantee safe, efficient, and successful use of new technologies, collaboration between healthcare organizations and researchers is crucial. Cloud computing has the ability to completely change the way that healthcare institutions use AI and biomedical computation. There are, however, obstacles that must be overcome before they may be successfully implemented. These technologies have the potential to revolutionize the healthcare system if healthcare providers and scientists work together to maximize their benefits for patients.

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