Editorial: Artificial Intelligence for Precision Medicine

Basics and Art of Immunosuppression in Liver Transplantation

Liver transplantation is one of the most challenging areas in the medical field. Despite that, it has already been established as a standard treatment option, especially in decompensated cirrhosis and selected cases of hepatocellular carcinoma and acute liver failure. Complications due to graft rejection, including mortality and morbidity, have greatly improved over time due to better immunosuppressive agents and management protocols. Currently, immunosuppression in liver transplant patients makes use of the best possible combinations of effective agents to achieve optimal immunosuppression for long-term graft survival. Induction agents are no longer used routinely, and the aim is to provide minimal immunosuppression in the maintenance phase. Currently available immunosuppressive agents are mainly classified as biological and pharmacological agents. Though the protocols may vary among the centers and over time, the basics of effective use usually remain similar. Most protocols use the combination of multiple agents with different mechanisms of action to reduce the dose and minimize the side effects. Along with the improvement in operative and perioperative techniques, this art of immunosuppression has contributed to the recent progress made in the outcomes of liver transplants. In this review, we will discuss the various types of immunosuppressive agents currently in use, the different protocols of immunosuppression used, and the art of optimal use for achieving maximum immunosuppression without increasing toxicity. We will also discuss the practical aspects of various immunosuppression regimens, including drug monitoring, and briefly discuss the concepts of immunosuppression minimization and withdrawal.

Generative Pre-Trained Transformers (GPT) and Space Health: A Potential Frontier in Astronaut Health During Exploration Missions

In anticipation of space exploration where astronauts are traveling away from Earth, and for longer durations with an increasing communication lag, artificial intelligence (AI) frameworks such as large language learning models (LLMs) that can be trained on Earth can provide real-time answers. This emerging technology may be helpful for acute medical emergencies, particularly in austere and distant space environments. In this manuscript, we provide an overview of generative pre-trained transformer (GPT) technology, a rapidly emerging AI technology, and implications, considerations, and limitations of such technology for space health.

The Evolution and Application of Artificial Intelligence in Rhinology: A State of the Art Review

OBJECTIVE

To provide a comprehensive overview on the applications of artificial intelligence (AI) in rhinology, highlight its limitations, and propose strategies for its integration into surgical practice.

DATA SOURCES

Medline, Embase, CENTRAL, Ei Compendex, IEEE, and Web of Science.

REVIEW METHODS

English studies from inception until January 2022 and those focusing on any application of AI in rhinology were included. Study selection was independently performed by 2 authors; discrepancies were resolved by the senior author. Studies were categorized by rhinology theme, and data collection comprised type of AI utilized, sample size, and outcomes, including accuracy and precision among others.

CONCLUSIONS

An overall 5435 articles were identified. Following abstract and title screening, 130 articles underwent full-text review, and 59 articles were selected for analysis. Eleven studies were from the gray literature. Articles were stratified into image processing, segmentation, and diagnostics (n = 27); rhinosinusitis classification (n = 14); treatment and disease outcome prediction (n = 8); optimizing surgical navigation and phase assessment (n = 3); robotic surgery (n = 2); olfactory dysfunction (n = 2); and diagnosis of allergic rhinitis (n = 3). Most AI studies were published from 2016 onward (n = 45).

IMPLICATIONS FOR PRACTICE

This state of the art review aimed to highlight the increasing applications of AI in rhinology. Next steps will entail multidisciplinary collaboration to ensure data integrity, ongoing validation of AI algorithms, and integration into clinical practice. Future research should be tailored at the interplay of AI with robotics and surgical education.

Prediction of disease comorbidity using explainable artificial intelligence and machine learning techniques: A systematic review

OBJECTIVE

Disease comorbidity is a major challenge in healthcare affecting the patient's quality of life and costs. AI-based prediction of comorbidities can overcome this issue by improving precision medicine and providing holistic care. The objective of this systematic literature review was to identify and summarise existing machine learning (ML) methods for comorbidity prediction and evaluate the interpretability and explainability of the models.

MATERIALS AND METHODS

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework was used to identify articles in three databases: Ovid Medline, Web of Science and PubMed. The literature search covered a broad range of terms for the prediction of disease comorbidity and ML, including traditional predictive modelling.

RESULTS

Of 829 unique articles, 58 full-text papers were assessed for eligibility. A final set of 22 articles with 61 ML models was included in this review. Of the identified ML models, 33 models achieved relatively high accuracy (80-95%) and AUC (0.80-0.89). Overall, 72% of studies had high or unclear concerns regarding the risk of bias.

DISCUSSION

This systematic review is the first to examine the use of ML and explainable artificial intelligence (XAI) methods for comorbidity prediction. The chosen studies focused on a limited scope of comorbidities ranging from 1 to 34 (mean = 6), and no novel comorbidities were found due to limited phenotypic and genetic data. The lack of standard evaluation for XAI hinders fair comparisons.

CONCLUSION

A broad range of ML methods has been used to predict the comorbidities of various disorders. With further development of explainable ML capacity in the field of comorbidity prediction, there is a significant possibility of identifying unmet health needs by highlighting comorbidities in patient groups that were not previously recognised to be at risk for particular comorbidities.

Investigations into the use of machine learning to predict drug dosage form design to obtain desired release profiles for 3D printed oral medicines

Three-dimensional (3D) printing, digitalization, and artificial intelligence (AI) are gaining increasing interest in modern medicine. All three aspects are combined in personalized medicine where 3D-printed dosage forms are advantageous because of their variable geometry design. The geometry design can be used to determine the surface area to volume (SA/V) ratio, which affects drug release from the dosage forms. This study investigated artificial neural networks (ANN) to predict suitable geometries for the desired dose and release profile. Filaments with 5% API load and polyvinyl alcohol were 3D printed using Fused Deposition Modeling to provide a wide variety of geometries with different dosages and SA/V ratios. These were dissolved in vitro, and the API release profiles were described mathematically. Using these data, ANN architectures were designed with the goal of predicting a suitable dosage form geometry. Poor accuracies of 68.5% in the training and 44.4% in the test settings were achieved with a classification architecture. However, the SA/V ratio could be predicted accurately with a mean squared error loss of only 0.05. This study shows that the prediction of the SA/V ratio using AI works, but not of the exact geometry. For this purpose, a global database could be built with a range of geometries to simplify the prescription process.