Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation

Using ChatGPT for Kidney Transplantation: Perceived Information Quality by Race and Education Levels

BACKGROUND

Kidney transplantation is a complex process requiring extensive preparation and ongoing monitoring. Artificial intelligence (AI)-powered chatbots hold potential for providing accessible health information, but our understanding of their role in offering health advice for kidney transplantation and how individuals assess such advice remains limited. This study investigates how individuals evaluate ChatGPT's responses to kidney transplantation questions in terms of information quality and empathy, focusing on potential differences across race/ethnicity and educational backgrounds.

METHODS

We collected Reddit posts (N = 4624) regarding kidney transplantation and selected 86 questions to represent typical clinician inquiries. These questions were used as input prompts for ChatGPT. A total of 565 participants assessed ChatGPT's responses through online surveys, rating information quality and empathy using Likert scales.

RESULTS

Multilevel analyses (N = 2825) show that there is a significant interaction between race/ethnicity and education levels in various measures related to perceived information quality, but not perceived empathy of ChatGPT's responses: accuracy (p < 0.05); authenticity (p < 0.01); believability (p < 0.05); informativeness (p = 0.053); usefulness (p < 0.05); recognizing users' feelings (p = 0.70) and understanding feelings and situations (p = 0.65). Among non-White individuals, higher education levels predicted higher perceived quality of ChatGPT's responses across all information quality measures. Notably, this trend was reversed for White individuals, where higher education levels led to lower perceived information quality.

CONCLUSIONS

Our results highlight the importance of developing AI tools sensitive to diverse communication styles and information needs.

Why your doctor is not an algorithm: Exploring logical principles of different clinical inference methods using liver transplantation as a model

The development of machine learning (ML) tools in many different medical settings is largely increasing. However, the use of the resulting algorithms in daily medical practice is still an unsolved challenge. We propose an epistemological approach (i.e., based on logical principles) to the application of computational tools in clinical practice. We rely on the classification of scientific inference into deductive, inductive, and abductive comparing the characteristics of ML tools with those derived from evidence-based medicine [EBM] and experience-based medicine, as paradigms of well-known methods for generation of knowledge. While we illustrate our arguments using liver transplantation as an example, this approach can be applied to other aspects of the specialty. Regarding EBM, it generates general knowledge that clinicians apply deductively, but the certainty of its conclusions is not guaranteed. In contrast, automatic algorithms primarily rely on inductive reasoning. Their design enables the integration of vast datasets and mitigates the emotional biases inherent in human induction. However, its poor capacity for abductive inference (a logical mechanism inherent to human clinical experience) constrains its performance in clinical settings characterized by uncertainty, where data are heterogeneous, results are highly influenced by context, or where prognostic factors can change rapidly.

Role of Complement-dependent Cytotoxicity Crossmatch and HLA Typing in Solid Organ Transplant

BACKGROUND

Solid organ transplantation is a life-saving medical operation that has progressed greatly because of developments in diagnostic tools and histocompatibility tests. Crossmatching for complement-dependent cytotoxicity (CDC) and human leukocyte antigen (HLA) typing are two important methods for checking graft compatibility and reducing the risk of graft rejection. HLA typing and CDC crossmatching are critical in kidney, heart, lung, liver, pancreas, intestine, and multi-organ transplantation.

METHODS

A systematic literature search was conducted on the internet, using PubMed, Scopus, and Google Scholar databases, to identify peer-reviewed publications about solid organ transplants, HLA typing, and CDC crossmatching.

CONCLUSION

Recent advances in HLA typing have allowed for high-resolution evaluation, epitope matching, and personalized therapy methods. Genomic profiling, next-generation sequencing, and artificial intelligence have improved HLA typing precision, resulting in better patient outcomes. Artificial intelligence (AI) driven virtual crossmatching and predictive algorithms have eliminated the requirement for physical crossmatching in the context of CDC crossmatching, boosting organ allocation and transplant efficiency. This review elaborates on the importance of HLA typing and CDC crossmatching in solid organ transplantation.

Should AI allocate livers for transplant? Public attitudes and ethical considerations

BACKGROUND

Allocation of scarce organs for transplantation is ethically challenging. Artificial intelligence (AI) has been proposed to assist in liver allocation, however the ethics of this remains unexplored and the view of the public unknown. The aim of this paper was to assess public attitudes on whether AI should be used in liver allocation and how it should be implemented.

METHODS

We first introduce some potential ethical issues concerning AI in liver allocation, before analysing a pilot survey including online responses from 172 UK laypeople, recruited through Prolific Academic.

FINDINGS

Most participants found AI in liver allocation acceptable (69.2%) and would not be less likely to donate their organs if AI was used in allocation (72.7%). Respondents thought AI was more likely to be consistent and less biased compared to humans, although were concerned about the "dehumanisation of healthcare" and whether AI could consider important nuances in allocation decisions. Participants valued accuracy, impartiality, and consistency in a decision-maker, more than interpretability and empathy. Respondents were split on whether AI should be trained on previous decisions or programmed with specific objectives. Whether allocation decisions were made by transplant committee or AI, participants valued consideration of urgency, survival likelihood, life years gained, age, future medication compliance, quality of life, future alcohol use and past alcohol use. On the other hand, the majority thought the following factors were not relevant to prioritisation: past crime, future crime, future societal contribution, social disadvantage, and gender.

CONCLUSIONS

There are good reasons to use AI in liver allocation, and our sample of participants appeared to support its use. If confirmed, this support would give democratic legitimacy to the use of AI in this context and reduce the risk that donation rates could be affected negatively. Our findings on specific ethical concerns also identify potential expectations and reservations laypeople have regarding AI in this area, which can inform how AI in liver allocation could be best implemented.

Computer image analysis with artificial intelligence: a practical introduction to convolutional neural networks for medical professionals

Artificial intelligence tools, particularly convolutional neural networks (CNNs), are transforming healthcare by enhancing predictive, diagnostic, and decision-making capabilities. This review provides an accessible and practical explanation of CNNs for clinicians and highlights their relevance in medical image analysis. CNNs have shown themselves to be exceptionally useful in computer vision, a field that enables machines to 'see' and interpret visual data. Understanding how these models work can help clinicians leverage their full potential, especially as artificial intelligence continues to evolve and integrate into healthcare. CNNs have already demonstrated their efficacy in diverse medical fields, including radiology, histopathology, and medical photography. In radiology, CNNs have been used to automate the assessment of conditions such as pneumonia, pulmonary embolism, and rectal cancer. In histopathology, CNNs have been used to assess and classify colorectal polyps, gastric epithelial tumours, as well as assist in the assessment of multiple malignancies. In medical photography, CNNs have been used to assess retinal diseases and skin conditions, and to detect gastric and colorectal polyps during endoscopic procedures. In surgical laparoscopy, they may provide intraoperative assistance to surgeons, helping interpret surgical anatomy and demonstrate safe dissection zones. The integration of CNNs into medical image analysis promises to enhance diagnostic accuracy, streamline workflow efficiency, and expand access to expert-level image analysis, contributing to the ultimate goal of delivering further improvements in patient and healthcare outcomes.

Artificial Intelligence Advances in Transplant Pathology

Transplant pathology plays a critical role in ensuring that transplanted organs function properly and the immune systems of the recipients do not reject them. To improve outcomes for transplant recipients, accurate diagnosis and timely treatment are essential. Recent advances in artificial intelligence (AI)-empowered digital pathology could help monitor allograft rejection and weaning of immunosuppressive drugs. To explore the role of AI in transplant pathology, we conducted a systematic search of electronic databases from January 2010 to April 2023. The PRISMA checklist was used as a guide for screening article titles, abstracts, and full texts, and we selected articles that met our inclusion criteria. Through this search, we identified 68 articles from multiple databases. After careful screening, only 14 articles were included based on title and abstract. Our review focuses on the AI approaches applied to four transplant organs: heart, lungs, liver, and kidneys. Specifically, we found that several deep learning-based AI models have been developed to analyze digital pathology slides of biopsy specimens from transplant organs. The use of AI models could improve clinicians' decision-making capabilities and reduce diagnostic variability. In conclusion, our review highlights the advancements and limitations of AI in transplant pathology. We believe that these AI technologies have the potential to significantly improve transplant outcomes and pave the way for future advancements in this field.

Artificial Intelligence and Robotics in Transplant Surgery: Advancements and Future Directions

This editorial explores the transformative impact of artificial intelligence (AI) and robotics in transplant surgery. By merging robotic precision with AI analysis, this integration enhances organ transplantation outcomes. AI algorithms scrutinize patient data, elevating organ compatibility during allocation. Robotic systems such as the da Vinci Surgical System enable intricate operations with reduced complications and faster recovery. AI-driven post-transplant monitoring identifies early rejection signs, while tailored immunosuppressive regimens enhance patient care. Future prospects encompass predictive organ availability, telemedicine-enabled expertise dissemination, bioengineered organs, and personalized immunosuppression. Ethical considerations include privacy and algorithmic bias. In striking a balance, responsible AI and robotics application can revolutionize transplant surgery, offering a brighter future for patients in need.

Beyond donation to organ utilization in the UK

PURPOSE OF REVIEW

Optimizing deceased donor organ utilization is gaining recognition as a topical and important issue, both in the United Kingdom (UK) and globally. This review discusses pertinent issues in the field of organ utilization, with specific reference to UK data and recent developments within the UK.

RECENT FINDINGS

A multifaceted approach is likely required in order to improve organ utilization. Having a solid evidence-base upon which transplant clinicians and patients on national waiting lists can base decisions regarding organ utilization is imperative in order to bridge gaps in knowledge regarding the optimal use of each donated organ. A better understanding of the risks and benefits of the uses of higher risk organs, along with innovations such as novel machine perfusion technologies, can help clinician decision-making and may ultimately reduce the unnecessary discard of precious deceased donor organs.

SUMMARY

The issues facing the UK with regards to organ utilization are likely to be similar to those in many other developed countries. Discussions around these issues within organ donation and transplantation communities may help facilitate shared learning, lead to improvements in the usage of scarce deceased donor organs, and enable better outcomes for patients waiting for transplants.

What We Are Missing: Using Machine Learning Models to Predict Vitamin C Deficiency in Patients with Metabolic and Bariatric Surgery

PURPOSE

Vitamin C (VC) is implicated in many physiological pathways. Vitamin C deficiency (VCD) can compromise the health of patients with metabolic and bariatric surgery (patients). As symptoms of VCD are elusive and data on VCD in patients is scarce, we aim to characterize patients with measured VC levels, investigate the association of VCD with other lab abnormalities, and create predictive models of VCD using machine learning (ML).

METHODS

A retrospective chart review of patients seen from 2017 to 2021 at a tertiary care center in Northeastern USA was conducted. A 1:4 case mix of patients with VC measured to a random sample of patients without VC measured was created for comparative purposes. ML models (BayesNet and random forest) were used to create predictive models and estimate the prevalence of VCD patients.

RESULTS

Of 5946 patients reviewed, 187 (3.1%) had VC measures, and 73 (39%) of these patients had VC<23 μmol/L(VCD. When comparing patients with VCD to patients without VCD, the ML algorithms identified a higher risk of VCD in patients deficient in vitamin B1, D, calcium, potassium, iron, and blood indices. ML models reached 70% accuracy. Applied to the testing sample, a "true" VCD prevalence of ~20% was predicted, among whom ~33% had scurvy levels (VC<11 μmol/L).

CONCLUSION

Our models suggest a much higher level of patients have VCD than is reflected in the literature. This indicates a high proportion of patients remain potentially undiagnosed for VCD and are thus at risk for postoperative morbidity and mortality.

Mortality scoring systems for liver transplant recipients: before and after model for end-stage liver disease score

The mortality scoring systems for patients with end-stage liver disease have evolved from the Child-Turcotte-Pugh score to the model for end-stage liver disease (MELD) score, affecting the wait list for liver allocation. There are inherent weaknesses in the MELD score, with the gradual decline in its accuracy owing to changes in patient demographics or treatment options. Continuous refinement of the MELD score is in progress; however, both advantages and disadvantages exist. Recently, attempts have been made to introduce artificial intelligence into mortality prediction; however, many challenges must still be overcome. More research is needed to improve the accuracy of mortality prediction in liver transplant recipients.