Big Data and Artificial Intelligence for Precision Medicine in the Neuro-ICU: Bla, Bla, Bla

Big data are a major driver in the development of precision medicine. Efficient analysis methods are needed to transform big data into clinically-actionable knowledge. To accomplish this, many researchers are turning toward machine learning (ML), an approach of artificial intelligence (AI) that utilizes modern algorithms to give computers the ability to learn. Much of the effort to advance ML for precision medicine has been focused on the development and implementation of algorithms and the generation of ever larger quantities of genomic sequence data and electronic health records. However, relevance and accuracy of the data are as important as quantity of data in the advancement of ML for precision medicine. For common diseases, physiological genomic readouts in disease-applicable tissues may be an effective surrogate to measure the effect of genetic and environmental factors and their interactions that underlie disease development and progression. Disease-applicable tissue may be difficult to obtain, but there are important exceptions such as kidney needle biopsy specimens. As AI continues to advance, new analytical approaches, including those that go beyond data correlation, need to be developed and ethical issues of AI need to be addressed. Physiological genomic readouts in disease-relevant tissues, combined with advanced AI, can be a powerful approach for precision medicine for common diseases.

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Reactive Artificial Intelligence Using Big Data in the Era of Precision Medicine—Reply

JAMA Surgery ◽

10.1001/jamasurg.2020.0856 ◽

2020 ◽

Vol 155 (7) ◽

pp. 671

Author(s):

Tyler J. Loftus ◽

Gilbert R. Upchurch ◽

Azra Bihorac

Keyword(s):

Artificial Intelligence ◽

Big Data ◽

Precision Medicine

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Translation of Genomics into Precision Medicine with Artificial Intelligence: A Narrative Review on Applications, Challenges and Future Perspectives

10.14293/s2199-1006.1.sor-.pp75wqs.v1 ◽

2020 ◽

Author(s):

Arin Natania. S

Keyword(s):

Artificial Intelligence ◽

Big Data ◽

Precision Medicine ◽

Language Processing ◽

Variant Calling ◽

Genomic Analysis ◽

Current Status ◽

Sequencing Technologies ◽

Main Driver ◽

Beneficial Impact

In the field of genomics, the broad availability of genetic information offered by next-generation sequencing technologies and rapid growth in biomedical publication has led to the advent of the big-data era. Integration of artificial intelligence. (AI) approaches such as machine learning, deep learning, and natural language processing (NLP) to tackle the challenges of scalability and high dimensionality of data and to transform big data into clinically actionable knowledge is expanding and becoming the foundation of precision medicine. In this paper, we review the current status and future directions of AI application in genomics within the context of workflows to integrate genomic analysis for precision cancer care. The existing solutions of AI and their limitations in cancer genetic testing and diagnostics such as variant calling and interpretation are critically analyzed. In addition, the present paper highlights the challenges to AI adoption in digital healthcare with regard to data requirements, algorithmic transparency, reproducibility, and real-world assessment, and discusses the importance of preparing patients and physicians for modern digitized healthcare. We believe that AI will remain the main driver to healthcare transformation toward precision medicine, yet the unprecedented challenges posed should be addressed to ensure safety and beneficial impact to healthcare.

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Application of Data Science Approaches to Investigate Autoimmune Thyroid Disease in Precision Medicine

10.5772/intechopen.101220 ◽

2021 ◽

Author(s):

Ayodeji Folorunsho Ajayi ◽

Emmanuel Tayo Adebayo ◽

Iyanuoluwa Oluwadunsi Adebayo ◽

Olubunmi Simeon Oyekunle ◽

Victor Oluwaseyi Amos ◽

...

Keyword(s):

Artificial Intelligence ◽

Big Data ◽

Precision Medicine ◽

Thyroid Disease ◽

Autoimmune Thyroid Disease ◽

Data Science ◽

Genetic Difference ◽

Genetic Data ◽

Autoimmune Thyroid ◽

Disease Study

In recent times, the application of artificial intelligence in facilitating, capturing, and restructuring Big data has transformed the accuracy of diagnosis and treatment of diseases, a field known as precision medicine. Big data has been established in various domains of medicine for example, artificial intelligence has found its way into immunology termed as immunoinformatics. There is evidence that precision medicine tools have made an effort to accurately detect, profile, and suggest treatment regimens for thyroid dysfunction using Big data such as imaging and genetic sequences. In addition, the accumulation of data on polymorphisms, autoimmune thyroid disease, and genetic data related to environmental factors has occurred over time resulting in drastic development of clinical autoimmune thyroid disease study. This review emphasized how genetic data plays a vital role in diagnosing and treating diseases related to autoimmune thyroid disease like Graves’ disease, subtle subclinical thyroid dysfunctions, Hashimoto’s thyroiditis, and hypothyroid autoimmune thyroiditis. Furthermore, connotation between environmental and endocrine risk factors in the etiology of the disease in genetically susceptible individuals were discussed. Thus, endocrinologists’ potential hurdles in cancer and thyroid nodules field include unreliable biomarkers, lack of distinct therapeutic alternatives due to genetic difference. Precision medicine data may improve their diagnostic and therapeutic capabilities using artificial intelligence.

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A Study on the Development of Appointment Algorithm of CT With Big Data using AI (Artificial Intelligence)

Korean Society of Computed Tomographic Technology ◽

10.31320/jksct.2018.20.2.1 ◽

2018 ◽

Vol 20 (2) ◽

pp. 1-5

Author(s):

Sang-ho Jeon ◽

Sung-yeul Yang ◽

In-beom Shin ◽

Dae-mok Son ◽

Tae-han Kwon ◽

...

Keyword(s):

Artificial Intelligence ◽

Big Data

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The Social Representations and Legal Theory of Artificial Intelligence, Robotics and Big Data in Healthcare

SSRN Electronic Journal ◽

10.2139/ssrn.3603106 ◽

2020 ◽

Author(s):

Julia M. Puaschunder ◽

Martin Gelter

Keyword(s):

Artificial Intelligence ◽

Big Data ◽

Legal Theory ◽

Social Representations ◽

The Social

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Artificial Intelligence as a Business Partner in Cardiovascular Precision Medicine: an Emerging Approach for Disease Detection and Treatment Optimization

Current Medicinal Chemistry ◽

10.2174/0929867328666201218122633 ◽

2020 ◽

Vol 28 ◽

Author(s):

Valeria Visco ◽

Germano Junior Ferruzzi ◽

Federico Nicastro ◽

Nicola Virtuoso ◽

Albino Carrizzo ◽

...

Keyword(s):

Artificial Intelligence ◽

Heart Failure ◽

Atrial Fibrillation ◽

Precision Medicine ◽

Clinical Decision Making ◽

Disease Detection ◽

Physician Patient Relationship ◽

Invasive Approach ◽

Tailored Treatment ◽

The Impact

Background: In the real world, medical practice is changing hand in hand with the development of new Artificial Intelligence (AI) systems and problems from different areas have been successfully solved using AI algorithms. Specifically, the use of AI techniques in setting up or building precision medicine is significant in terms of the accuracy of disease discovery and tailored treatment. Moreover, with the use of technology, clinical personnel can deliver a very much efficient healthcare service. Objective: This article reviews AI state-of-the-art in cardiovascular disease management, focusing on diagnostic and therapeutic improvements. Methods: To that end, we conducted a detailed PubMed search on AI application from distinct areas of cardiology: heart failure, arterial hypertension, atrial fibrillation, syncope and cardiovascular rehabilitation. Particularly, to assess the impact of these technologies in clinical decision-making, this research considers technical and medical aspects. Results: On one hand, some devices in heart failure, atrial fibrillation and cardiac rehabilitation represent an inexpensive, not invasive or not very invasive approach to long-term surveillance and management in these areas. On the other hand, the availability of large datasets (big data) is a useful tool to predict the development and outcome of many cardiovascular diseases. In summary, with this new guided therapy, the physician can supply prompt, individualised, and tailored treatment and the patients feel safe as they are continuously monitored, with a significant psychological effect. Conclusion: Soon, tailored patient care via telemonitoring can improve the clinical practice because AI-based systems support cardiologists in daily medical activities, improving disease detection and treatment. However, the physician-patient relationship remains a pivotal step.

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