Computer-aided diagnosis (CAD) has become a major research area in medical imaging and diagnostic radiology. This article reviews the historical development, current status, and future potential of CAD in the PACS environment. CAD is a concept that integrates the roles of physicians and computers, whereas automated computer diagnosis is based solely on computer algorithms. CAD systems are designed to complement radiologists' performance rather than compete with it. CAD has been used to assist in the early detection of breast cancer on mammograms and to improve the detection of lung nodules when combined with other CAD schemes for PA chest images. CAD has also been developed for detecting vertebral fractures on lateral chest radiographs and intracranial aneurysms in MRA. In addition, CAD systems have been developed for detecting interval changes in successive bone scan images. These systems use temporal subtraction images to enhance the detection of subtle changes between images. CAD systems are expected to be integrated into PACS in the future, and they may be assembled as packages for detecting various lesions and assisting in differential diagnosis. CAD systems can be used as a "second opinion" to improve the accuracy of radiologists' diagnoses. The performance of CAD systems is evaluated based on their ability to improve the diagnostic accuracy of radiologists. CAD systems have shown promising results in improving the detection of various lesions, including clustered microcalcifications, lung nodules, and interstitial opacities. CAD systems are also being developed for differential diagnosis, where they can assist radiologists in distinguishing between benign and malignant lesions. These systems use the likelihood of malignancy to improve the accuracy of radiologists' decisions. CAD systems can also be used to search for and retrieve images similar to an unknown case, which can help radiologists make more accurate diagnoses. In conclusion, CAD has become an important tool in medical imaging and diagnostic radiology. It has the potential to improve the accuracy of radiologists' diagnoses and to assist in the early detection of various lesions. CAD systems are expected to be integrated into PACS in the future, and they may be used as a valuable tool in clinical practice.Computer-aided diagnosis (CAD) has become a major research area in medical imaging and diagnostic radiology. This article reviews the historical development, current status, and future potential of CAD in the PACS environment. CAD is a concept that integrates the roles of physicians and computers, whereas automated computer diagnosis is based solely on computer algorithms. CAD systems are designed to complement radiologists' performance rather than compete with it. CAD has been used to assist in the early detection of breast cancer on mammograms and to improve the detection of lung nodules when combined with other CAD schemes for PA chest images. CAD has also been developed for detecting vertebral fractures on lateral chest radiographs and intracranial aneurysms in MRA. In addition, CAD systems have been developed for detecting interval changes in successive bone scan images. These systems use temporal subtraction images to enhance the detection of subtle changes between images. CAD systems are expected to be integrated into PACS in the future, and they may be assembled as packages for detecting various lesions and assisting in differential diagnosis. CAD systems can be used as a "second opinion" to improve the accuracy of radiologists' diagnoses. The performance of CAD systems is evaluated based on their ability to improve the diagnostic accuracy of radiologists. CAD systems have shown promising results in improving the detection of various lesions, including clustered microcalcifications, lung nodules, and interstitial opacities. CAD systems are also being developed for differential diagnosis, where they can assist radiologists in distinguishing between benign and malignant lesions. These systems use the likelihood of malignancy to improve the accuracy of radiologists' decisions. CAD systems can also be used to search for and retrieve images similar to an unknown case, which can help radiologists make more accurate diagnoses. In conclusion, CAD has become an important tool in medical imaging and diagnostic radiology. It has the potential to improve the accuracy of radiologists' diagnoses and to assist in the early detection of various lesions. CAD systems are expected to be integrated into PACS in the future, and they may be used as a valuable tool in clinical practice.