Abstract
Artificial intelligence (AI) is gradually changing medical practice. With recent progress in digitized data acquisition, machine learning and computing infrastructure, AI applications are expanding into areas that were previously thought to be only the province of human experts. In this Review Article, we outline recent breakthroughs in AI technologies and their biomedical applications, identify the challenges for further progress in medical AI systems, and summarize the economic, legal and social implications of AI in healthcare.

Abstract
Interrogation of tissue informs on patient management through delivery of a diagnosis together with associated clinically relevant data. The diagnostic pathologist will usually evaluate the morphological appearances of a tissue sample and, occasionally, the pattern of expression of a limited number of biomarkers. Recent developments in sequencing technology mean that DNA and RNA from tissue samples can now be interrogated in great detail. These new technologies, collectively known as next-generation sequencing (NGS), generate huge amounts of data which can be used to support patient management. In order to maximize the utility of tissue interrogation, the molecular data need to be interpreted and integrated with the morphological data. However, in order to interpret the molecular data, the pathologist must understand the utility and the limitations of NGS data. In this review, the principles behind NGS technologies are described. In addition, the caveats in the interpretation of the data are discussed, and a scheme is presented to “classify” the types of data which are generated. Finally, a glossary of new terminology is included to help pathologists become familiar with the lexicon of NGS-derived molecular data.

Abstract
Biomarker assays have brought significant challenges to bioanalytical laboratories that historically have provided pharmacokinetic analytical services to the drug development industry. This has largely been due to two reasons: the lack of regulatory guidance in how to validate biomarker assays and the lack of scientists in bioanalytical laboratories with experience in this clinical arena. Since biomarkers have been measured for many decades in clinical laboratories globally, this article reviews the different types of analytical laboratories and their practices and case studies will demonstrate the potential outcomes of using biomarker assays in drug development when they are not validated correctly.