Industry Perspective on Artificial Intelligence/Machine Learning in Pharmacovigilance

Trustworthy Artificial Intelligence in Dentistry: Learnings from the EU AI Act

Artificial intelligence systems (AISs) gain relevance in dentistry, encompassing diagnostics, treatment planning, patient management, and therapy. However, questions about the generalizability, fairness, and transparency of these systems remain. Regulatory and governance bodies worldwide are aiming to address these questions using various frameworks. On March 13, 2024, members of the European Parliament approved the Artificial Intelligence Act (AIA), which emphasizes trustworthiness and human-centeredness as relevant aspects to regulate AISs beyond safety and efficacy. This review presents the AIA and similar regulatory and governance efforts in other jurisdictions and lays out that regulations such as the AIA are part of a complex ecosystem of interdependent and interwoven legal requirements and standards. Current efforts to regulate dental AISs require active input from the dental community, with participation of dental research, education, providers, and patients being relevant to shape the future of dental AISs.

ChatGPT in medicine: A cross-disciplinary systematic review of ChatGPT's (artificial intelligence) role in research, clinical practice, education, and patient interaction

BACKGROUND

ChatGPT, a powerful AI language model, has gained increasing prominence in medicine, offering potential applications in healthcare, clinical decision support, patient communication, and medical research. This systematic review aims to comprehensively assess the applications of ChatGPT in healthcare education, research, writing, patient communication, and practice while also delineating potential limitations and areas for improvement.

METHOD

Our comprehensive database search retrieved relevant papers from PubMed, Medline and Scopus. After the screening process, 83 studies met the inclusion criteria. This review includes original studies comprising case reports, analytical studies, and editorials with original findings.

RESULT

ChatGPT is useful for scientific research and academic writing, and assists with grammar, clarity, and coherence. This helps non-English speakers and improves accessibility by breaking down linguistic barriers. However, its limitations include probable inaccuracy and ethical issues, such as bias and plagiarism. ChatGPT streamlines workflows and offers diagnostic and educational potential in healthcare but exhibits biases and lacks emotional sensitivity. It is useful in inpatient communication, but requires up-to-date data and faces concerns about the accuracy of information and hallucinatory responses.

CONCLUSION

Given the potential for ChatGPT to transform healthcare education, research, and practice, it is essential to approach its adoption in these areas with caution due to its inherent limitations.

The Coming of Age of AI/ML in Drug Discovery, Development, Clinical Testing, and Manufacturing: The FDA Perspectives

Artificial intelligence (AI) and machine learning (ML) represent significant advancements in computing, building on technologies that humanity has developed over millions of years-from the abacus to quantum computers. These tools have reached a pivotal moment in their development. In 2021 alone, the U.S. Food and Drug Administration (FDA) received over 100 product registration submissions that heavily relied on AI/ML for applications such as monitoring and improving human performance in compiling dossiers. To ensure the safe and effective use of AI/ML in drug discovery and manufacturing, the FDA and numerous other U.S. federal agencies have issued continuously updated, stringent guidelines. Intriguingly, these guidelines are often generated or updated with the aid of AI/ML tools themselves. The overarching goal is to expedite drug discovery, enhance the safety profiles of existing drugs, introduce novel treatment modalities, and improve manufacturing compliance and robustness. Recent FDA publications offer an encouraging outlook on the potential of these tools, emphasizing the need for their careful deployment. This has expanded market opportunities for retraining personnel handling these technologies and enabled innovative applications in emerging therapies such as gene editing, CRISPR-Cas9, CAR-T cells, mRNA-based treatments, and personalized medicine. In summary, the maturation of AI/ML technologies is a testament to human ingenuity. Far from being autonomous entities, these are tools created by and for humans designed to solve complex problems now and in the future. This paper aims to present the status of these technologies, along with examples of their present and future applications.

Provision and Characterization of a Corpus for Pharmaceutical, Biomedical Named Entity Recognition for Pharmacovigilance: Evaluation of Language Registers and Training Data Sufficiency

INTRODUCTION AND OBJECTIVE

Machine learning (ML) systems are widely used for automatic entity recognition in pharmacovigilance. Publicly available datasets do not allow the use of annotated entities independently, focusing on small entity subsets or on single language registers (informal or scientific language). The objective of the current study was to create a dataset that enables independent usage of entities, explores the performance of predictive ML models on different registers, and introduces a method to investigate entity cut-off performance.

METHODS

A dataset has been created combining different registers with 18 different entities. We applied this dataset to compare the performance of integrated models with models created with single language registers only. We introduced fractional stratified k-fold cross-validation to determine model performance on entity level by using training dataset fractions. We investigated the course of entity performance with fractions of training datasets and evaluated entity peak and cut-off performance.

RESULTS

The dataset combines 1400 records (scientific language: 790; informal language: 610) with 2622 sentences and 9989 entity occurrences and combines data from external (801 records) and internal sources (599 records). We demonstrated that single language register models underperform compared to integrated models trained with multiple language registers.

CONCLUSIONS

A manually annotated dataset with a variety of different pharmaceutical and biomedical entities was created and is made available to the research community. Our results show that models that combine different registers provide better maintainability, have higher robustness, and have similar or higher performance. Fractional stratified k-fold cross-validation allows the evaluation of training data sufficiency on the entity level.

A Science-Based Methodology Framework for the Assessment of Combination Safety Risks in Clinical Trials

Multiple components factor into the assessment of combination safety risks when two or more novel individual products are used in combination in clinical trials. These include, but are not limited to, biology, biochemistry, pharmacology, class effects, and preclinical and clinical findings (such as adverse drug reactions, drug target and mechanism of action, target expression, signaling, and drug-drug interactions). This paper presents a science-based methodology framework for the assessment of combination safety risks when two or more investigational products are used in clinical trials. The aim of this methodology framework is to improve prediction of the risks, to enable the appropriate safety risk mitigation and management to be put in place for the combination, and the development of the project combination safety strategy.

The Use of Artificial Intelligence in Pharmacovigilance: A Systematic Review of the Literature

INTRODUCTION

Artificial intelligence through machine learning uses algorithms and prior learnings to make predictions. Recently, there has been interest to include more artificial intelligence in pharmacovigilance of products already in the market and pharmaceuticals in development.

OBJECTIVE

The aim of this study was to identify and describe the uses of artificial intelligence in pharmacovigilance through a systematic literature review.

METHODS

Embase and MEDLINE database searches were conducted for articles published from January 1, 2015 to July 9, 2021 using search terms such as 'pharmacovigilance,' 'patient safety,' 'artificial intelligence,' and 'machine learning' in the title or abstract. Scientific articles that contained information on the use of artificial intelligence in all modalities of patient safety or pharmacovigilance were reviewed and synthesized using a pre-specified data extraction template. Articles with incomplete information and letters to editor, notes, and commentaries were excluded.

RESULTS

Sixty-six articles were identified for evaluation. Most relevant articles on artificial intelligence focused on machine learning, and it was used in patient safety in the identification of adverse drug events (ADEs) and adverse drug reactions (ADRs) (57.6%), processing safety reports (21.2%), extraction of drug-drug interactions (7.6%), identification of populations at high risk for drug toxicity or guidance for personalized care (7.6%), prediction of side effects (3.0%), simulation of clinical trials (1.5%), and integration of prediction uncertainties into diagnostic classifiers to increase patient safety (1.5%). Artificial intelligence has been used to identify safety signals through automated processes and training with machine learning models; however, the findings may not be generalizable given that there were different types of data included in each source.

CONCLUSION

Artificial intelligence allows for the processing and analysis of large amounts of data and can be applied to various disease states. The automation and machine learning models can optimize pharmacovigilance processes and provide a more efficient way to analyze information relevant to safety, although more research is needed to identify if this optimization has an impact on the quality of safety analyses. It is expected that its use will increase in the near future, particularly with its role in the prediction of side effects and ADRs.