Artificial intelligence for precision medicine in autoimmune liver disease

AI Innovations in Liver Transplantation: From Big Data to Better Outcomes

Artificial intelligence (AI) has emerged as a transformative field in computational research with diverse applications in medicine, particularly in the field of liver transplantation (LT) given its ability to analyze and build upon complex and multidimensional data. This literature review investigates the application of AI in LT, focusing on its role in pre-implantation biopsy evaluation, development of recipient prognosis algorithms, imaging analysis, and decision-making support systems, with the findings revealing that AI can be applied across a variety of fields within LT, including diagnosis, organ allocation, and surgery planning. As a result, algorithms are being developed to assess steatosis in pre-implantation biopsies and predict liver graft function, with AI applications displaying great accuracy across various studies included in this review. Despite its relatively recent introduction to transplantation, AI demonstrates potential in delivering cost and time-efficient outcomes. However, these tools cannot replace the role of healthcare professionals, with their widespread adoption demanding thorough clinical testing and oversight.

The current status and future directions of artificial intelligence in the prediction, diagnosis, and treatment of liver diseases

Early detection, accurate diagnosis, and effective treatment of liver diseases are of paramount importance for improving patient survival rates. However, traditional methods are frequently influenced by subjective factors and technical limitations. With the rapid progress of artificial intelligence (AI) technology, its applications in the medical field, particularly in the prediction, diagnosis, and treatment of liver diseases, have drawn increasing attention. This article offers a comprehensive review of the current applications of AI in hepatology. It elaborates on how AI is utilized to predict the progression of liver diseases, diagnose various liver conditions, and assist in formulating personalized treatment plans. The article emphasizes key advancements, including the application of machine learning and deep learning algorithms. Simultaneously, it addresses the challenges and limitations within this domain. Moreover, the article pinpoints future research directions. It underscores the necessity for large-scale datasets, robust algorithms, and ethical considerations in clinical practice, which is crucial for facilitating the effective integration of AI technology and enhancing the diagnostic and therapeutic capabilities of liver diseases.

Shining a spotlight on sarcopenia and myosteatosis in liver disease and liver transplantation: Potentially modifiable risk factors with major clinical impact

Muscle-wasting and disease-related malnutrition are highly prevalent in patients with chronic liver diseases (CLD) as well as in liver transplant (LT) candidates. Alterations of body composition (BC) such as sarcopenia, myosteatosis and sarcopenic obesity and associated clinical frailty were tied to inferior clinical outcomes including hospital admissions, length of stay, complications, mortality and healthcare costs in various patient cohorts and clinical scenarios. In contrast to other inherent detrimental individual characteristics often observed in these complex patients, such as comorbidities or genetic risk, alterations of the skeletal muscle and malnutrition are considered as potentially modifiable risk factors with a major clinical impact. Even so, there is only limited high-level evidence to show how these pathologies should be addressed in the clinical setting. This review discusses the current state-of-the-art on the role of BC assessment in clinical outcomes in the setting of CLD and LT focusing mainly on sarcopenia and myosteatosis. We focus on the disease-related pathophysiology of BC alterations. Based on these, we address potential therapeutic interventions including nutritional regimens, physical activity, hormone and targeted therapies. In addition to summarizing existing knowledge, this review highlights novel trends, and future perspectives and identifies persisting challenges in addressing BC pathologies in a holistic way, aiming to improve outcomes and quality of life of patients with CLD awaiting or undergoing LT.

The Promise of Explainable AI in Digital Health for Precision Medicine: A Systematic Review

This review synthesizes the literature on explaining machine-learning models for digital health data in precision medicine. As healthcare increasingly tailors treatments to individual characteristics, the integration of artificial intelligence with digital health data becomes crucial. Leveraging a topic-modeling approach, this paper distills the key themes of 27 journal articles. We included peer-reviewed journal articles written in English, with no time constraints on the search. A Google Scholar search, conducted up to 19 September 2023, yielded 27 journal articles. Through a topic-modeling approach, the identified topics encompassed optimizing patient healthcare through data-driven medicine, predictive modeling with data and algorithms, predicting diseases with deep learning of biomedical data, and machine learning in medicine. This review delves into specific applications of explainable artificial intelligence, emphasizing its role in fostering transparency, accountability, and trust within the healthcare domain. Our review highlights the necessity for further development and validation of explanation methods to advance precision healthcare delivery.

Artificial Intelligence and liver: Opportunities and barriers

Artificial Intelligence (AI) has recently been shown as an excellent tool for the study of the liver; however, many obstacles still have to be overcome for the digitalization of real-world hepatology. The authors present an overview of the current state of the art on the use of innovative technologies in different areas (big data, translational hepatology, imaging, and transplant setting). In clinical practice, physicians must integrate a vast array of data modalities (medical history, clinical data, laboratory tests, imaging, and pathology slides) to achieve a diagnostic or therapeutic decision. Unfortunately, machine learning and deep learning are still far from really supporting clinicians in real life. In fact, the accuracy of any technological support has no value in medicine without the support of clinicians. To make better use of new technologies, it is essential to improve clinicians' knowledge about them. To this end, the authors propose that collaborative networks for multidisciplinary approaches will improve the rapid implementation of AI systems for developing disease-customized AI-powered clinical decision support tools. The authors also discuss ethical, educational, and legal challenges that must be overcome to build robust bridges and deploy potentially effective AI in real-world clinical settings.

Risk Stratification in Primary Biliary Cholangitis

Primary Biliary Cholangitis (PBC) is a chronic cholestatic liver disease with a heterogeneous presentation, symptomatology, disease progression, and response to therapy. The current risk stratification assessment, aimed at identifying patients with a higher risk of disease progression, encompasses an in-depth analysis of demographic data, clinical and laboratory findings, antibody profiles, and the evaluation of liver fibrosis using both invasive and noninvasive techniques. Treatment response scores after one year of therapy remain to date a major factor influencing the prognosis of PBC patients. While the initial therapeutic approach with ursodeoxycholic acid (UDCA) is universally applied, new second-line treatment options have recently emerged, with many others under investigation. Consequently, the prevailing one-size-fits-all approach is poised to be supplanted by tailored strategies, ensuring high-risk patients receive the most appropriate treatment regimen from diagnosis. This will require the development of a risk prediction model to assess, at the time of diagnosis, the course, outcome, and response to first and additional treatments of PBC patients. This manuscript provides a comprehensive overview of the current and emerging tools used for risk stratification in PBC and speculates on how these developments might shape the disease landscape in the near future.

Artificial intelligence in liver cancers: Decoding the impact of machine learning models in clinical diagnosis of primary liver cancers and liver cancer metastases

Liver cancers are the fourth leading cause of cancer-related mortality worldwide. In the past decade, breakthroughs in the field of artificial intelligence (AI) have inspired development of algorithms in the cancer setting. A growing body of recent studies have evaluated machine learning (ML) and deep learning (DL) algorithms for pre-screening, diagnosis and management of liver cancer patients through diagnostic image analysis, biomarker discovery and predicting personalized clinical outcomes. Despite the promise of these early AI tools, there is a significant need to explain the 'black box' of AI and work towards deployment to enable ultimate clinical translatability. Certain emerging fields such as RNA nanomedicine for targeted liver cancer therapy may also benefit from application of AI, specifically in nano-formulation research and development given that they are still largely reliant on lengthy trial-and-error experiments. In this paper, we put forward the current landscape of AI in liver cancers along with the challenges of AI in liver cancer diagnosis and management. Finally, we have discussed the future perspectives of AI application in liver cancer and how a multidisciplinary approach using AI in nanomedicine could accelerate the transition of personalized liver cancer medicine from bench side to the clinic.