Motivation: The price of medical treatment continues to rise due to (i) an increasing population; (ii) an aging human growth; (iii) disease prevalence; (iv) a rise in the frequency of patients that utilize health care services; and (v) increase in the price. Objective: Artificial Intelligence (AI) is already well-known for its superiority in various healthcare applications, including the segmentation of lesions in images, speech recognition, smartphone personal assistants, navigation, ride-sharing apps, and many more. Our study is based on two hypotheses: (i) AI offers more economic solutions compared to conventional methods; (ii) AI treatment offers stronger economics compared to AI diagnosis. This novel study aims to evaluate AI technology in the context of healthcare costs, namely in the areas of diagnosis and treatment, and then compare it to the traditional or non-AI-based approaches. Methodology: PRISMA was used to select the best 200 studies for AI in healthcare with a primary focus on cost reduction, especially towards diagnosis and treatment. We defined the diagnosis and treatment architectures, investigated their characteristics, and categorized the roles that AI plays in the diagnostic and therapeutic paradigms. We experimented with various combinations of different assumptions by integrating AI and then comparing it against conventional costs. Lastly, we dwell on three powerful future concepts of AI, namely, pruning, bias, explainability, and regulatory approvals of AI systems. Conclusions: The model shows tremendous cost savings using AI tools in diagnosis and treatment. The economics of AI can be improved by incorporating pruning, reduction in AI bias, explainability, and regulatory approvals.

Background and Purpose— Many processes involved in the pathogenesis of atherosclerosis result in modifications of the extracellular matrix. These changes not only determine the mechanical stability of atherosclerotic lesions but can directly or indirectly influence further development of the lesions. The purpose of the present study was to compare the matrix composition of human carotid plaques from symptomatic patients with those obtained from patients without symptoms. Furthermore, matrix changes related to age were studied. Methods— Thirty atherosclerotic carotid plaques were removed by endarterectomy from 27 patients and divided into 2 groups on the basis of the presence of ipsilateral symptoms. The plaques were homogenized, and the total levels of the major components of the extracellular matrix were determined. Results— Plaques associated with symptoms were characterized by increased levels of elastin (1.58±0.46 versus 1.24±0.40 mg/g wet wt; P =0.03) and decreased levels of hydroxyapatite (45.1±46.3 versus 131.4±111.7 mg/g wet wt; P =0.02) compared with asymptomatic plaques. The increase in elastin in plaques from symptomatic patients was due to elevated levels of an intermediate-size fraction, as determined by liquid chromatography. Collagen and sulfated glycosaminoglycans were present in equal amounts in both groups. Elastin content in carotid plaques decreased with age. Conclusions— Carotid plaques from symptomatic patients have lower levels of hydroxyapatite than those from asymptomatic patients. The present study also raises the possibility that non–cross-linked forms of elastin, increased in plaques associated with symptoms, could be a marker of plaque vulnerability and/or directly induce harmful cellular activities or increase lipoprotein retention in the vascular wall.

Background and Purpose— Echolucent carotid plaques have been associated with increased risk for stroke. Histological studies suggested that echolucent plaques are hemorrhage- and lipid-rich, whereas echogenic plaques are characterized by fibrosis and calcification. This is the first study to relate echogenicity to plaque composition analyzed biochemically. Methods— Echogenicity of human carotid plaques was analyzed by standardized high-definition ultrasound and classified into echolucent, with gray-scale median (GSM) <32 and echogenic with GSM ≥32. The biochemical composition of the plaques was assessed by fast-performance liquid chromotography and high-performance thin-layer chromotography. Results— As assessed biochemically (milligrams per gram [mg/g]), echolucent plaques contained less hydroxyapatite (43.8 [SD 41.2] mg/g versus 121.6 [SD 106.2] mg/g; P =0.018), more total elastin (1.7 [SD 0.4] mg/g versus 1.2 [SD 0.4] mg/g; P =0.008), and more intermediate-size elastin forms (1.2 [SD 0.3] mg/g versus 0.8 [SD 0.4] mg/g; P =0.018). There was no difference in collagen amount between echogenic and echolucent plaques, neither biochemically (15.3 [SD 3.7] mg/g versus 14.4 [SD 3.4] mg/g) nor histologically (13.4 [SD 4.9] % versus 13.0 [SD 5.6] %). Cholesterol esters, unesterified cholesterol, and triglycerides were increased in plaques associated with symptoms (22.5 [SD 23.3] mg/g versus 13.3 [SD 3.2]; P =0.04), but no differences were detected between echolucent and echogenic plaques (13.5 [SD 4.0] versus 20.2 [SD 21.5] mg/g). Similar results were obtained by Oil Red O staining (symptomatic 7.6 [SD 4.7] % versus asymptomatic 4.2 [SD 3.6] %; P =0.03; echolucent 5.9 [SD 4.1] % versus echogenic 5.0 [SD 4.0] % of area). Conclusions— Echogenicity of carotid plaques is mainly determined by their elastin and calcium but not collagen or lipid content. In addition, echolucency is associated to higher elastin content.