Artificial Intelligence and Its Integration with Regenerative Medicine Approach

Artificial Intelligence and Its Integration with Regenerative Medicine Approach

Copyright: © 2023 |Pages: 26
DOI: 10.4018/978-1-6684-8337-4.ch003
(Individual Chapters)
No Current Special Offers


Artificial intelligence (AI) and regenerative medicine are becoming inextricably linked. As a result, the long-term outlook for the advancement of AI, regenerative medicine, and stem cells in the healthcare industry is incredibly bright. AI is being used in healthcare to discover new drugs and to provide personalized medicine based on big data. It can improve medical diagnosis and treatment plans. Regenerative medicine has the potential to repair damaged tissues and organs through stem cell-based regenerative medicine. Stem cell research is also undergoing significant development as a major component of the approach to regenerative medicine. In the future, it is expected that it will contribute to more personalized and more effective treatments. Integrating AI can help model and simulate cellular behavior, analyze, and process images for cell tracking and tissue engineering, and analyze large amounts of genomic and proteomic data. Together, these fields have the potential to transform healthcare and improve patient outcomes.
Chapter Preview

Basics Of Artificial Intelligence

AI is not a single technology, but rather a combination of several interlinked modalities (Figure 1). Although most technologies could be related directly to the healthcare industry, the processes and tasks they support vary considerably (Howard, 2019). Machine learning (ML) currently dominates AI research in medicine. This is mostly because ML can be applied to a wide range of complex tasks, such as assisting physicians with disease diagnosis and personalizing treatment for each patient. A key characteristic of ML is its capacity to “learn and apply” acquired knowledge to similar situations by using various algorithmic methods (Chen & Decary, 2020; Howard, 2019).

Figure 1.

Common technologies involved in the AI field

Source: Author’s creation

Since ML can process vast quantities of digital data, prediction tools are the most extensively used and studied methods in the healthcare sector. In the classical model, ML can be divided into task-driven Supervised Learning (SL) and data-driven Unsupervised Learning (UL). Whilst SL deals with predictions and prediction models (pre-categorized data), UL is involved with pattern and structure recognition (unlabeled data) (Serey et al., 2021).

Key Terms in this Chapter

Artificial Intelligence: A field that combines several interconnected technologies and modalities with sophisticated learning processes. It lies at the intersection of engineering and computer science.

Genomic Science: Interdisciplinary branch of biology focusing on the architecture, function, evolution, mapping, and alteration of genomes.

Healthcare: The systematic delivery of medical care to a population or a person.

Stem Cells: The foundation of regenerative medicine and can be employed to restore organs or tissues that have been injured.

Machine Learning: Major component of artificial intelligence in the healthcare industry. Its main capabilities include acquiring and applying knowledge to a specific problem through the use of programmed algorithmic strategies.

Regenerative Medicine: A field of medicine that aims to repair or reestablish physiological function by replacing or rejuvenating human cells, tissues, or organs.

Technology: Practical utilization of scientific information, particularly in industry.

Complete Chapter List

Search this Book: