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Rebranding Transplantation: Eliminating the Organ Shortage Through a Combination of Artificial Intelligence, Xenotransplantation, Regenerative Medicine/Tissue Engineering, and Bio-artificial Organs

Mahal H, Solez, K

Time is Kidney Qing Yin Wang (1).jpg

Time is Kidney

AI: The Push Towards Precision Medicine

  1. Genetic Editing and Compatibility Enhancement:

    • Genetically editing pig kidneys for increased human compatibility.

    • Identifying specific genetic modifications that enhance compatibility and reduce the risk of organ rejection.

    • Analyzing large datasets to predict the most effective genetic changes.

  2. Risk Assessment and Patient Selection:

    • Assessing patient data, medical history, and genetic profiles to determine the suitability of xenotransplantation.

    • Identifying patients who are likely to benefit from pig-to-human kidney transplants.

    • Estimating the success rate and potential complications for individual patients.

  3. Immunosuppression Optimization:

    • Optimizing drug dosages based on patient-specific factors, minimizing side effects while maintaining efficacy.

    • Automating continuous monitoring and adjustment of immunosuppression

  4. Virus Detection and Prevention:

    • Analyze zoonotic viral genomes and predict potential risks.

    • Early detection of viral infections to improve patient outcomes.

  5. Organ Allocation and Prioritization:

    • Optimizing organ allocation by considering factors like waiting time, medical urgency, and compatibility.

    • Prioritization models to ensure fair distribution of organs based on need.

  6. Predictive Modeling for Long-Term Outcomes:

    • Predicting long-term outcomes of pig-to-human kidney transplants.

    • By analyzing historical data, machine learning models can estimate survival rates, rejection rates, and overall patient health.

  7. Data Integration and Knowledge Discovery:

    • Integrating diverse data sources, including medical records, genetic data, and clinical trials.

    • Uncovering hidden patterns and associations can contribute to scientific discoveries and treatment advancements.


The 23 big challenges of humanity, a concept first suggested by computing scientist Marcus Hutter (see this video):

1. Human aggression, 2. Nuclear war, 3. Climate emergency, 4. Systemic racism, 5. Pandemics, 6. Neocolonialism, 7. AI alignment, 8. Energy, 9. Avatars, 10. Water scarcity, 11. Asteroids, 12. Nanotechnology, 13. Biodiversity, 14. Environment, 15. Resource depletion, 16. Solar winds, 17. Behavioral biology, 18. Financial crisis, 19. Genetic engineering, 20. Ideology, 21. Complexity fragility, 22. Unknown unknowns, 23. Other.

A significant impediment is the absence of a practical positive goal to work toward. Nick Bostrom’s book Deep Utopia, released on March 27th, 2024, and its six follow-up videos are significant advances in this regard.


Recent Discourse 

We have identified 31 PubMed articles from 2023 and 2024 that support our

idea of the positive outcome with AI and transplantation from 153 total search results with ChatGPT and other entities (see Reference 5).

a. Predicting Success in Lung Transplantation with Frailty Measures AI is Not Frustrated by Complexity and So Holds the Promise of Incredible Infinite Improvements Over the Status Quo

b. OpenAI’s ChatGPT and Its Potential Impact on Narrative and Scientific Writing in Nephrology’ includes a poem expresses our gratitude to pigs and the potential for xenotransplantation to replace the need for dialysis.

c. ‘ChatGPT versus Human Memory: A Historical Exploration of the 4 Hs and 4 Ts’ Similarly this article is a moving account of using old fashioned human detective work to find the originator of a commonly used the mnemonic ‘4 Hs and 4 Ts’ used to recall the reversible causes of cardiac arrest.

d. ‘Performance of ChatGPT and Bard in Self-Assessment Questions for Nephrology Board Renewal’ GPT-4 is now able to pass the nephrology boards which previous LLMs were not able to do.

e. Navigating the Landscape of Personalized Medicine: The Relevance of ChatGPT, BingChat, and Bard AI in Nephrology Literature Searches’ However eventually all the errors will be eliminated.

f. ‘Chatting Beyond ChatGPT: Advancing Equity Through AI-Driven Language

Interpretation’. The Problem of Medical Jargon. LLMs are making such great strides in language translation that the challenges of translating medical jargon to regular speech will also be easily dealt with.

g. ‘Chatbot Responses Suggest That Hypothetical Biology Questions Are Harder than Realistic Ones’. LLMs will even change our basic ideas about which problems are hard and which are easy!


It is the ultimate dream of transplantation to provide organs for everyone. This plan will accomplish that, and sooner than you think! Being the first institution, first province, to propose completely solving the organ shortage in our lifetime provides considerable First Mover Advantage and is made easier by the fact that we already have coauthor relationships with the main players in the three relevant areas of xenotransplantation, regenerative medicine, and the bioartificial kidney. Solving the organ shortage in transplantation is just the beginning! We Are Like the AI Equivalent of Fusion Energy!


1. Solez K, Mahal H, Farris, AB, and Montgomery, R: AGI Will Help Solve the Organ Shortage in Our Lifetime, Paper for AGI-2024

2. Solez K, Fung KC, Saliba KA, Sheldon VLC, Petrosyan A, Perin L, Burdick JF, Fissell WH, Demetris AJ, Cornell LD. The bridge between transplantation and regenerative medicine: Beginning a new Banff classification of tissue engineering pathology. Am J Transplant. 2018 Feb;18(2):321-327. doi: 10.1111/ajt.14610. Epub 2018 Jan 16. American_Journal_of_Transplantation.pdf

3. Petrosyan, A., Martins, P. N., Solez, K., Uygun, B. E., Gorantla, V. S., & Orlando, G. (2022). Regenerative medicine applications: An overview of clinical trials. Frontiers in Bioengineering and Biotechnology, 10, 942750.

4. Solez, K., and Eknoyan, G. Transplant Nephropathology: Wherefrom, Wherein, and Whereto, Clin Transplant. 2024;38:e15309.

5. Solez, K, Mahal H, Alam A, Farris AB, Levine, DJ, Thennakoonwela P, The emerging risk v. benefit of “artificial Intelligence” Book Chapter, Transformations impacting health care in the new millennium. Crippen D, ed. Springer, 2024 (in press)

6. Mahal H, Alam A, Montgomery RA , Loupy A, Al Jurdi A, Solez, KB, and Solez, K “As Simple As Possible, But Not Simpler” Models for Xenotransplantation and for the Other 23 Big Challenges of Humanity with Solutions Aided by AI in a Global Accessible Medical World with Less Jargon, CEOT poster 2024

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