The Scientific World Journal / 2013 / Article / Tab 1

Review Article

Biomedical Informatics for Computer-Aided Decision Support Systems: A Survey

Table 1

Stregnths and weaknesses of existing computer-aided decision support systems and research in different application areas.

Application areasStrengthsWeaknesses

Cancer(i) An abundance of molecular assays and data are available for many cancer cases; these can be used towards developing strong decision support systems(i) More should be done to integrate knowledge from molecular-based and image-based sources available for cancer detection
(ii) There is a need to develop better schemes and methods for validating the effectiveness of the existing and upcoming systems in this area

Radiology(i) A variety of effective computational techniques exists for many applications in radiology
(ii) It is one of the fastest growing fields using applications of computer-aided decision systems
(i) Most of the research in this area suffers from lack of comprehensive datasets
(ii) Most of these studies do not include knowledge of illness/injury/complication into the decision-making process

Emergency medicine(i) Although there are only a few systems that have been adopted into clinical practices, the existing systems have shown a positive impact on the cost and quality of healthcare
(ii) There is a significant potential for computer-aided systems in this area since emergency medicine and trauma are very time and resource critical aspects of healthcare
(i) Accuracies of existing systems may not be sufficient for clinical uses
(ii) A variety of illnesses and injuries have not yet been addressed by computer-aided decision support systems
(iii) There is a lack of comprehensive validation of the short-/long-term impacts on these systems using sufficiently large datasets

Cardiovascular medicine (i) Since heart disease is among the leading causes of death, computer-aided decision systems here have potentially very high impact on world health
(ii) While most cardiovascular-based intelligent decision support systems suffer from high false positives, they often help detect disease at early stages
(i) These systems usually incorporate only a portion of available patient information. More variety in information sources may be required in the decision-making process to reduce false positives
(ii) There is a lack of a comprehensive validation process. Existing research claims need to be tested in more real-world settings

Dental(i) Existing systems have shown capability for detecting dental complications at early stages
(ii) Such early detection facilitates better practice of preventive care
(i) Some of the technologies used for capturing the information for computer-aided decision support systems are relatively expensive and hence preventing them from being widely adopted in practice