eDoctor: machine learning and the future of medicine.

Machine learning (ML) is a burgeoning field of medicine with huge resources being applied to fuse computer science and statistics to medical problems. Proponents of ML extol its ability to deal with large, complex and disparate data, often found within medicine and feel that ML is the future for biomedical research, personalized medicine, computer-aided diagnosis to significantly advance global health care. However, the concepts of ML are unfamiliar to many medical professionals and there is untapped potential in the use of ML as a research tool. In this article, we provide an overview of the theory behind ML, explore the common ML algorithms used in medicine including their pitfalls and discuss the potential future of ML in medicine.

Handelman GS ，Kok HK ，Chandra RV ，Razavi AH ，Lee MJ ，Asadi H ... -  《-》

Artificial intelligence in the diagnosis of cardiovascular disease.

Zeron RMC ，Serrano Junior CV 《-》

Supervised and unsupervised algorithms for bioinformatics and data science.

Bioinformatics refers to an ever evolving huge field of research based on millions of algorithms, designated to several data banks. Such algorithms are either supervised or unsupervised. In this article, a detailed overview of the supervised and unsupervised techniques is presented with the aid of examples. The aim of this article is to provide the readers with the basic understanding of the state of the art models, which are key ingredients of explainable machine learning in the field of bioinformatics.

Sohail A ，Arif F 《-》

Artificial intelligence and machine learning overview in pathology & laboratory medicine: A general review of data preprocessing and basic supervised concepts.

Machine learning (ML) is becoming an integral aspect of several domains in medicine. Yet, most pathologists and laboratory professionals remain unfamiliar with such tools and are unprepared for their inevitable integration. To bridge this knowledge gap, we present an overview of key elements within this emerging data science discipline. First, we will cover general, well-established concepts within ML, such as data type concepts, data preprocessing methods, and ML study design. We will describe common supervised and unsupervised learning algorithms and their associated common machine learning terms (provided within a comprehensive glossary of terms that are discussed within this review). Overall, this review will offer a broad overview of the key concepts and algorithms in machine learning, with a focus on pathology and laboratory medicine. The objective is to provide an updated useful reference for those new to this field or those who require a refresher.

Albahra S ，Gorbett T ，Robertson S ，D'Aleo G ，Kumar SVS ，Ockunzzi S ，Lallo D ，Hu B ，Rashidi HH ... -  《SEMINARS IN DIAGNOSTIC PATHOLOGY》

Peering Into the Black Box of Artificial Intelligence: Evaluation Metrics of Machine Learning Methods.

Handelman GS ，Kok HK ，Chandra RV ，Razavi AH ，Huang S ，Brooks M ，Lee MJ ，Asadi H ... -  《-》