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Suresh V, Joseph JD, Manasa G, Seshadri H, Singla P, Rajagopal V, Advancements of artificial intelligence-driven approaches in the use of stem cell therapy in diseases or disorders: clinical applications and ethical issues. The Evi. 2024:2(3):1-. DOI:10.61505/evidence.2024.2.3.88
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Received: 2024-07-06
Accepted: 2024-07-11
Published: 2024-07-11
Evidence in Context

• AI improves diagnostic algorithms and treatment strategies in stem cell therapy. • AI optimizes stem cell differentiation and characterization for better outcomes. • Predictive AI models enhance post-transplant success and automate cell classification. • AI-driven stem cell therapy shows promise for neurodegenerative, orthopedic, and cardiovascular diseases. • The manuscript addresses ethical and regulatory challenges of AI in stem cell therapy.

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Advancements of artificial intelligence-driven approaches in the use of stem cell therapy in diseases or disorders: clinical applications and ethical issues

Vinay Suresh1, Jomon De Joseph2, Goudicherla Manasa3, Hariharan Seshadri4, Priyanka Singla5, Vineeth Rajagopal6*

1 King Georges Medical University, Lucknow, India.

2 Mahatma Gandhi Medical College and Research Institute, Puducherry, India.

3 National Institute of Pharmaceutical Education and Research, Hajipur, India.

4 Institute of Internal Medicine, Madras Medical College, Chennai, India.

5 Department of Community Medicine, Maharishi Markandeshwar Institute of Medical Sciences and Research, Mullana, India.

6 Department of Community Medicine and School of Public Health, Postgraduate Institute of Medical Education and Research, Chandigarh, India.

*Correspondence:drvineethrajagopal@gmail.com

Abstract

The convergence of artificial intelligence (AI) and stem cell therapy marks a transformative advancement in regenerative medicine. This manuscript explores how AI-driven approaches are being integrated into stem cell research and therapy, enhancing disease mechanism insights, therapeutic strategies, and clinical practices. AI's role spans from diagnostic algorithms to predictive analytics for patient outcomes, particularly in complex biomedical data analysis. This integration addresses challenges in stem cell therapy, such as precise cell characterization and optimization of cell differentiation processes. AI-enhanced therapies are showing promise in treating various conditions, including neurodegenerative diseases, orthopedic ailments, and cardiovascular disorders. The manuscript highlights several case studies demonstrating AI's impact on stem cell therapy, such as predictive analytics in post-transplant relapse and automated cell classification. It also discusses the broadening scope of AI in medical fields, economic and accessibility considerations, and the ethical and regulatory challenges posed by this technological integration. The future direction emphasizes ongoing AI advancements, improving predictive models, and robust ethical frameworks. This synthesis underscores the potential of AI and stem cell therapy to revolutionize healthcare by offering new treatment avenues for previously incurable diseases.

Keywords: artificial intelligence, stem cell therapy, regenerative medicine, machine learning, biomedical data analysis, disease mechanisms, therapeutic strategies

Introduction

In the rapidly evolving landscape of medical science, two fields have emerged as particularly transformative: artificial intelligence (AI) and stem cell therapy. While each has its trajectory, their convergence is setting the stage for groundbreaking advances in regenerative medicine [1-3]. This article explores how AI and machine learning (ML) are being integrated into stem cell research and therapy, offering new insights into disease mechanisms, enhancing therapeutic strategies, and reshaping clinical practices. Table 1 in the manuscript provides a comprehensive overview of key studies that demonstrate the transformative impact of AI-driven stem cell therapies across various medical specialties. For instance, the study by Ramović

© 2024 The author(s) and Published by the Evidence Journals. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Hamzagić et al [4] showcases how machine learning models can predict the development of cancer stem cell markers in colon and breast cancer, emphasizing the role of AI in advancing oncological treatments. Other studies, such as those by Umar et al [5] and Capponi et al [6], focus on improving the quality of stem cell production and the design of cell therapy technologies respectively. These studies exemplify the potential of AI to not only improve the efficacy and precision of stem cell therapies but also to advance our understanding of complex biological processes, thereby facilitating more effective treatments and innovations in the field of regenerative medicine.

Table 1: Overview of key studies of transformative impact of AI-driven stem cell therapies across medical specialties

Study  

Characteristics

Key findings

Ramović Hamzagić et al., (2024)[4]

The authors investigated polystyrene nanoparticles' impact on cancer stem cells (CSCs) in colon and breast cancer, finding increased stemness and tumor aggressiveness. An ML model accurately predicted CSC marker development, highlighting genetic algorithms' potential in CSC progression prediction.

·  CSCs drive tumor progression, drug resistance, and metastasis. This study shows PSNPs increase cancer stemness in colon and breast cancer cells.

·  Using in vitro and ML models, it predicts CSC marker development, highlighting PSNPs' role in tumor aggressiveness.

Umar et al., (2023) [5]

In this study, Artificial Intelligence (AI) is implemented to enhance the quality of stem cell production and distribution, thereby aiding in the assessment of the feasibility, efficiency, effectiveness, and safety of stem cells.

·  AI enhances the production and distribution processes of stem cells.

·  AI plays a crucial role in assessing the feasibility, efficiency, and security of stem cells.

Mehta et al., (2023)

The authors delve into the substantial influence of Artificial Intelligence (AI) within the field of medicine, emphasizing its potential advantages as well as the obstacles that remain, and underscore the most notable instances of AI-facilitated diagnosis, which have the capacity to aid in precise and effective diagnosis.

·  AI improves accuracy of diagnosis and reduces false positives.

·  AI enables personalized treatment and precision medicine.

Madhvi et al., (2023)[23]

The emergence of Artificial Intelligence is leading to a significant shift in the healthcare industry by improving data access and expediting the development of analytical tools, as highlighted by authors.

·  AI is being used to improve clinical practice and healthcare systems.

·  AI has the potential to automate tasks and improve efficiency in healthcare.

Marzec-Schmidt et al., (2023) [24]

In this article, the authors used a deep learning model to predict the differentiation stage of pluripotent stem cells undergoing differentiation towards hepatocytes, based on morphological features of cell cultures.

·  A deep learning model successfully attained near-optimal classification accuracy when analyzing images of stem cells.

·  The model's predictions based on cell morphology reflected the functional maturation of cells.

Capponi et al., (2023)[6]

Authors explore the possibilities of integrating experimental library screenings and artificial intelligence (AI) to construct prognostic frameworks for the advancement of modular cell therapy technologies. These frameworks have the capability to expedite cell therapy progress by formulating prognostic frameworks, design principles, and enhanced blueprints.

·  Utilizing experimental library screenings alongside artificial intelligence in order to construct prognostic frameworks for the advancement of modular cell therapy technologies.

·  AI and ML models can accelerate cell therapy development with predictive models and improved designs.

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Ledziński et al., (2023)[25]

Authors provided an overview of the complete AI workflow, along with elucidations of frequently utilized machine learning algorithms and delineations of assessment criteria for regression and classification tasks.

·  The paper highlights AI's success in molecular chemistry and drug discovery, reducing costs and time for predicting pharmacological activities.

·  It reviews key AI applications in cardiology, focusing on various learning methods and natural language processing.

Salave et al., (2023)[26]

In this manuscript, the authors underscore fundamental principles of AI and emphasize the interdisciplinary integration of Computational Neuroscience, Cognitive Science, and AI. They elucidate significant discoveries concerning AI in neuroscience, spanning its multifaceted applications across various fields.

·  The collaboration across different disciplines such as computational neuroscience, cognitive science, and artificial intelligence is evident in the field of healthcare.

·  AI showcases its potential in addressing various neurological disorders like neuroinflammation, schizophrenia, Parkinson’s disease, epilepsy, autism, Alzheimer’s disease, brain tumors, and anesthesiology.

Figure 1 illustrates the integration of artificial intelligence in enhancing stem cell therapy across various stages. It depicts how AI-driven approaches, using neural networks and large datasets, facilitate improved prediction, classification, and treatment strategies within stem cell therapy. Key components shown include AI's role in analyzing complex cellular data, predictive analytics for assessing post-transplant relapse, and the customization of therapies through precise cell classification. Additionally, it highlights the application of AI in optimizing treatment protocols to achieve better patient outcomes in clinical settings. This visualization effectively communicates the multifaceted impact of AI on advancing stem cell therapeutic approaches [Figure 1].

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Figure 1: Enhancing Stem Cell Therapy through Artificial Intelligence: Predictive Analytics, Cell Classification, and Treatment Optimization

The Rise of AI in Medical Applications

AI’s role in medical science has been expanding, with applications ranging from diagnostic algorithms to predictive analytics for patient outcomes. Machine learning models are now capable of analyzing complex biomedical data [7], which can include everything from genetic sequences

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to cellular images, helping to uncover patterns that elude human detection [8-10]. This capability is particularly valuable in stem cell research, where the vast amount of data generated can be daunting and intricate.

Enhancing Stem Cell Therapy with AI

Stem cell therapy promises to treat a plethora of conditions, from neurodegenerative diseases like Parkinson’s to orthopedic ailments such as osteoarthritis [11,12]. However, the development and implementation of these therapies are fraught with challenges, including the need for precise cell characterization and the optimization of cell differentiation and integration processes. AI is proving to be an invaluable asset in addressing these challenges. For instance, machine learning algorithms are being used to predict patient responses to various stem cell therapies, thereby personalizing treatments to increase efficacy and minimize adverse effects.

Case Studies: AI-driven Successes in Stem Cell Applications

Several recent studies have demonstrated the power of AI in enhancing stem cell therapies:

  • Predictive Analytics in Post-Transplant Relapse: AI models have been successfully used to predict relapse rates in patients following stem cell transplants, particularly in cases of acute lymphoblastic leukemia [13]. These models process post-transplant data to identify risk factors and optimize prophylactic treatments.
  • Cell Differentiation and Treatment Optimization: In cardiology, AI algorithms analyze patterns in stem cell differentiation to improve the outcomes of therapies for heart diseases [14]. These insights are critical in refining therapeutic approaches that replace damaged myocardial tissue.
  • Automated Cell Classification and Therapy Customization: In neurology, AI facilitates the classification of different stem cell types, enabling the customization of therapies for diseases such as multiple sclerosis and spinal cord injuries, tailoring treatments according to the specific cell types and states found in individual patients [15].

Broadening the Scope: AI in Various Medical Fields

The integration of artificial intelligence (AI) in regenerative medicine, particularly stem cell therapy, promises revolutionary advancements across a spectrum of incurable and debilitating diseases.

  • Orthopedic Disorders: In orthopedic disorders such as osteoarthritis, tendinopathy, avascular necrosis, and others involving cartilage defects and bone non-union, AI-driven stem cell therapy is proving superior to standard care [15]. AI algorithms optimize the selection and differentiation of stem cells, enhancing their ability to regenerate damaged tissues, thus offering hope for lasting recovery without the need for invasive surgical interventions.
  • Neurological Disorders: Neurological applications of stem cell therapy, particularly for conditions like multiple sclerosis, spinal cord injuries, amyotrophic lateral sclerosis, and stroke, benefit significantly from AI's predictive capabilities [16]. AI models predict patient outcomes and tailor stem cell treatments to individual needs, potentially improving recovery rates and functionality in patients suffering from these severe conditions.
  • Ophthalmologic Disorders: For ophthalmologic conditions such as retinitis pigmentosa, corneal surface defects, and macular degeneration, AI-integrated stem cell therapies are being developed to restore vision or halt disease progression [17]. AI helps in accurately modeling disease progression and tailoring cell therapies to the specific cellular needs of the eye, thus offering a cutting-edge alternative to conventional treatments that often offer limited improvement.
  • Cardiac Disorders: In cardiac care, stem cell therapies guided by AI are used to treat myocardial infarction and dilated cardiomyopathy [14]. These therapies regenerate
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    heart tissue and improve cardiac function by analyzing patient-specific cardiac data and predicting the optimal stem cell types and treatment courses, thereby enhancing the success rates of these regenerative interventions.
  • Microvascular and Surgical Disorders: AI-driven stem cell therapies are transforming the treatment of microvascular and surgical disorders such as chronic wounds and critical limb ischemia [12]. By facilitating better understanding and application of stem cells to improve blood flow and tissue repair, AI enhances the healing process in conditions traditionally treated with less effective surgical methods.
  • Pediatric Disorders: Children suffering from cerebral palsy, autism spectrum disorder, osteogenesis imperfecta, bronchopulmonary dysplasia, hypoxic ischemic encephalopathy, muscular dystrophy, and spinal muscular atrophy are experiencing new treatment avenues through AI-enhanced stem cell therapies [18]. These treatments are specifically tailored to the developmental needs of young patients, offering improvements in conditions that lacked effective therapies previously.
  • Respiratory Disorders: In treating acute respiratory distress syndrome, AI-enhanced stem cell therapy offers a potential for significant improvement in lung function and patient outcomes [19]. AI models help in predicting therapy responses and optimizing stem cell dosages, thereby improving the survival rates and recovery speeds of affected patients.
  • Dermatologic Disorders: For dermatologic applications such as skin rejuvenation, AI-driven stem cell therapies provide personalized treatments based on detailed analysis of the patient’s skin type and condition [20]. This approach not only enhances the efficacy of treatments but also minimizes the risks associated with traditional skin rejuvenation techniques.
  • Endocrinologic Disorders: In endocrinology, particularly diabetes, stem cell therapies assisted by AI are being developed to regenerate insulin-producing beta cells and restore the body’s insulin regulation capability [21]. AI algorithms help in predicting patient-specific responses to these therapies, thus potentially revolutionizing diabetes treatment.
  • Gastroenterologic/Hepatologic Disorders: Patients with liver cirrhosis can benefit from AI-driven stem cell therapies that regenerate liver tissue and improve liver function. AI enhances the precision of these therapies, allowing for tailored treatments that address the specific extent and nature of liver damage in individual patients.

Future Directions

Looking forward, the integration of AI into stem cell research and therapy holds immense promise. The ongoing advancement of AI technologies, such as deep learning and neural networks, is likely to further enhance our understanding and utilization of stem cells. Future research will likely focus on improving the accuracy of predictive models, enhancing the efficiency of stem cell differentiation techniques, and developing more robust frameworks for ethical and regulatory oversight.

Enhanced Precision in Medicine

AI's precision in data analysis is refining stem cell therapies to meet individual patient needs, increasing the effectiveness of treatments for conditions ranging from spinal cord injuries to cardiac failures. Machine learning algorithms predict outcomes, personalize therapies, and ensure the real-time viability of stem cells used in treatments.

Streamlining Clinical Trials

AI algorithms are revolutionizing the design, monitoring, and data processing of clinical trials for stem cell therapies. This results in more efficient trials, with machine learning providing predictive insights into efficacy and safety, thereby accelerating the path from research to clinical practice.

Overcoming Technological Challenges

Integration issues, such as data sharing and system interoperability, are being addressed through

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AI solutions that enhance the compatibility and functionality of medical systems. Advanced neural networks are crucial for improving the reliability of stem cell characterization and therapy outcomes.

Training and Education

The intersection of AI and stem cell therapy demands specialized knowledge. There is a growing emphasis on educational programs that equip healthcare professionals with the necessary skills to utilize AI tools effectively in clinical settings, fostering a knowledgeable workforce adept at leveraging these advancements.

Economic and Accessibility Considerations

AI-enhanced therapies promise to reduce healthcare costs and expand access. However, economic barriers remain, particularly in low-resource settings. This section explores the impact of AI on healthcare economics and the potential for broader access to cutting-edge treatments.

Global Implementation and Collaboration

The global variability in the adoption of AI-enhanced therapies reflects differing regulatory, economic, and technological landscapes. Opportunities for international collaboration could standardize practices and improve outcomes worldwide, highlighting the need for a unified approach to data sharing and AI application.

Long-term Impact and Sustainability

The manuscript concludes by considering the long-term sustainability of integrating AI into stem cell therapy. It calls for ongoing evaluation of ethical practices, technological advancements, and societal impacts, ensuring that this integration supports enduring improvements in patient care and medical research.

Ethical and Regulatory Considerations

As AI and stem cell therapy intersect, they bring about not only scientific and medical advances but also ethical and regulatory challenges [22]. The ability of AI to manipulate and predict biological processes raises significant ethical questions regarding consent, privacy, and the potential for misuse. Regulatory frameworks are struggling to keep pace with the rapid development of these technologies, necessitating a re-evaluation of ethical guidelines and legal standards.

Conclusion

The integration of AI with stem cell therapy represents a transformative movement within healthcare, transcending mere scientific advancement to revolutionize regenerative medicine. This fusion not only enhances patient outcomes and streamlines clinical practices but also navigates the complexities of modern medicine with a focus on innovation and ethical considerations. As we explore this promising frontier, it becomes crucial to foster collaboration among scientists, clinicians, ethicists, and regulators. This concerted effort will ensure that the potential of AI and stem cell therapy is realized in ways that are beneficial, ethical, and sustainable, offering new hope for the treatment of previously incurable diseases and reshaping healthcare for future generations.

Supporting information

None

Ethical Considerations

None

Acknowledgments

None

Funding

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This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Author contribution statement

All authors contributed equally and attest they meet the ICMJE criteria for authorship and gave final approval for submission.

Data availability statement

Data included in article/supp. material/referenced in article.

Additional information

No additional information is available for this paper.

Declaration of competing interest

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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