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Clinical Guidelines are being developed as a tool to promote Best Practice in Medicine. They are usually defined as “systematically developed statements to assist practitioner and patient decisions about appropriate Healthcare for specific clinical circumstances” (). The Institute’s Committee on Practice Guidelines further clarified this definition by specifying as: “the expected health benefit exceeds the expected negative consequences by a sufficient margin that the care is worth providing”.

The “applied” nature distinguishes applied sciences from theoretical sciences. To emphasize this distinction, we begin with a general, meta-level overview of the scientific endeavor. We introduce the notion of and four interconnected modalities of knowledge. In addition to the traditional differentiation between implicit and explicit knowledge, we outline the concepts of general and individual knowledge. We connect with the “,” a fundamental issue of artificial intelligence, and with another important paradigm of artificial intelligence, reasoning, a method of individual knowledge processing that aims at solving new problems based on the solutions to similar past problems.

Healthcare executives are grappling with a climate of great change in the healthcare industry. This is coming from a number of sources. First, there is increased activism among consumers and their employers who want a greater say in healthcare service delivery and reimbursement options; consumers want access to services, while employers and other payers want affordability. Second, in the clinical provider organizations, there is increased pressure to make operations more efficient in response to the need to hold down spiraling costs, to better manage utilization of health care resources among populations, and to more effectively compete in healthcare markets. (It should be noted that the market-oriented context for the application discussed in this paper is considered from the standpoint of the predominantly private healthcare sector, which is how the system is organized in the United States. However, the primary clinical application domain in which we focus our attention is one where the U.S. Government provides reimbursement for services under its Medicare program.) Finally, there is increased regulatory scrutiny across the spectrum of service providers, service payers and life science companies.

A virtual community is a social entity involving several individuals who relate to one another by the use of a specific communication technology that bridges geographic distance. Traditional communities are determined by factors such as geographic proximity, organizational structures or activities shared by the members of the community. The concept “virtual” implies properties that unlike these of a traditional community are based on the utilization of advanced technologies enabling interactions and exchange of information between members who may not physically meet at any point in time.

This chapter focuses on evidence based telemedicine and its various applications. Evidence based medicine is the integration of best research evidence with clinical expertise and patient values, for the best possible patient management. It is the explicit and judicious use of current best evidence in making decisions about the treatment and care of individual patients. In practice, it means integrating the individual clinical skills of the doctor with the best available clinical evidence from systematic research.

Breast cancer is one of the most devastating and deadly diseases for women today. Despite advances in cancer treatment, early mammographic detection remains the first line of defense in the battle against breast cancer. Patients with early-detected malignancies have a significantly lower mortality rate. Nevertheless, it is reported that up to 30% of breast lesions go undetected in screening mammograms and up to 2/3 of those lesions are visible in retrospect. The clinical significance of early diagnosis and the difficulty of the diagnostic task have generated a tremendous interest on developing computerized decision support systems in mammography. Their main goal is to offer radiologists a reliable and fast “second opinion”. Several systems have been developed over the past decade and some have successfully entered the clinical arena. Although several studies have indicated a positive impact on early breast cancer detection, the results are mixed and the decision support systems are under ongoing development and evaluation. In addition, there are still several unresolved issues such as their true impact on breast cancer mortality, the overall impact on the recall rate of mammograms and thus the radiologists’ workload, the reproducibility of the computerized second opinions, the ability of a knowledgeable radiologist to effectively process these opinions, and ultimately clinical acceptance. Furthermore, the medical and legal implications of storing and/or dismissing computerized second opinions are currently unknown. Given the number of unresolved issues, the clinical role of the decision support systems in mammography continues to evolve. The purpose of this article is to review the present state of computer-assisted detection (CAD) and diagnosis (CADx) in mammography. Specifically, the article will describe the principles of CAD/CADx, how it is currently applied in mammography, examine reported limitations, and identify future research directions. Research work is presented towards the application of knowledge-based systems in mammography to address some of the current CAD limitations. Finally, the natural extension of CAD to telemammography is discussed.

Immense genomic data have been accumulated through various research activities such as the Human Genome Project. A genome is the entire collection of information on the DNA molecules of each organism. The application of information technology to data mining analyses of genomic data is known as bioinformatics, which could be a most rewarding field for a computer scientist. This chapter describes DNA microarray technology, one of the hot topics in bioinformatics.

The miniaturization of electrical and electronic equipment is certainly not a new phenomenon, and its effects have long been evident in the healthcare sector. Nevertheless, reducing the size of medical devices is one thing, wearing them is quite another. This transition imposes a new set of design requirements, challenges and restrictions and has further implications on their use, as they are often intended for operation by non medical professionals in uncontrolled environments. The purpose of this chapter is to introduce the use of wearable devices in healthcare along with the key enabling technologies behind their design, with emphasis on information technologies. Furthermore, it aims to present the current state of development along with the potential public benefits in both technological and healthcare terms. The devices described are those involving some degree of digital information handling, thus excluding conventional wearable devices such as eyeglasses, hearing aids and prosthetic devices from the discussion.