Artificial Intelligence (AI) is the ability and/or programming of a machine to solve complex problems by detecting patterns from mining the BIG data related to the problem. A branch of AI includes machine learning which uses an iterative learning model rather than fixed rules. This learning program can be the basis to build an intelligent knowledge base system (IKBS) which can be used as a key tool in clinical decision making. It can also be used for the identification of biomarkers which can further become an integral part of precision medicine practices.
Pulmonary diseases are highly heterogeneous and present various challenges in diagnosis, treatment, prognosis, and prediction of outcomes. Identification of mechanisms, underlying these types of diseases, provides the opportunity to develop new therapies and a personalized approach to management. The collection of multiple genetic and detailed biochemical data from small and large patient cohorts has led to an explosion of studies investigating biomarkers to achieve these aims. Although there have been some advances in these types of diagnostic tools, it is unlikely that they would be easily accessible to clinicians or clinical researchers in the future.
Moving toward better markers that could be used in clinical application in the screening and diagnosis of pulmonary diseases that could also provide prognostic information remains an important goal of research. Defining strict guidelines to carefully categorize the patient's phenotypes, endotypes and genotypes is fundamental in generating personalized patient data in any phase of clinical trials.
In this Research Topic, we will focus on the consensus between the experts in the field about what are the best evidence-based practice in clinical classifications of patients with pulmonary diseases. We will also discuss which biomarker in terms of laboratory, radiological or clinical exam can best define the accurate patient category to select appropriate representatives to be included in the research or clinical trials.
Major chapters in this special issue will focus on using pulmonary diseases Transomic data to:
• Define biomarkers in Pulmonary Diseases
• Define suitable biomarkers for Proper clinical trials in Pulmonary Diseases
• Explain Pulmonary Diseases etiology
• Guide Pulmonary Diseases treatment options
Artificial Intelligence (AI) is the ability and/or programming of a machine to solve complex problems by detecting patterns from mining the BIG data related to the problem. A branch of AI includes machine learning which uses an iterative learning model rather than fixed rules. This learning program can be the basis to build an intelligent knowledge base system (IKBS) which can be used as a key tool in clinical decision making. It can also be used for the identification of biomarkers which can further become an integral part of precision medicine practices.
Pulmonary diseases are highly heterogeneous and present various challenges in diagnosis, treatment, prognosis, and prediction of outcomes. Identification of mechanisms, underlying these types of diseases, provides the opportunity to develop new therapies and a personalized approach to management. The collection of multiple genetic and detailed biochemical data from small and large patient cohorts has led to an explosion of studies investigating biomarkers to achieve these aims. Although there have been some advances in these types of diagnostic tools, it is unlikely that they would be easily accessible to clinicians or clinical researchers in the future.
Moving toward better markers that could be used in clinical application in the screening and diagnosis of pulmonary diseases that could also provide prognostic information remains an important goal of research. Defining strict guidelines to carefully categorize the patient's phenotypes, endotypes and genotypes is fundamental in generating personalized patient data in any phase of clinical trials.
In this Research Topic, we will focus on the consensus between the experts in the field about what are the best evidence-based practice in clinical classifications of patients with pulmonary diseases. We will also discuss which biomarker in terms of laboratory, radiological or clinical exam can best define the accurate patient category to select appropriate representatives to be included in the research or clinical trials.
Major chapters in this special issue will focus on using pulmonary diseases Transomic data to:
• Define biomarkers in Pulmonary Diseases
• Define suitable biomarkers for Proper clinical trials in Pulmonary Diseases
• Explain Pulmonary Diseases etiology
• Guide Pulmonary Diseases treatment options
