Background: Artificial intelligence (AI) is set to shape the future of medical practice. The perspective and understanding of medical students are critical for guiding the development of educational curricula and training. Objective: This study aims to assess and compare medical AI-related attitudes among medical students in general medicine and in one of the visually oriented fields (pathology), along with illuminating their anticipated role of AI in the rapidly evolving landscape of AI-enhanced health care. Methods: This was a cross-sectional study that used a web-based survey composed of a closed-ended questionnaire. The survey addressed medical students at all educational levels across the 5 public medical schools, along with pathology residents in 4 residency programs in Jordan. Results: A total of 394 respondents participated (328 medical students and 66 pathology residents). The majority of respondents (272/394, 69%) were already aware of AI and deep learning in medicine, mainly relying on websites for information on AI, while only 14% (56/394) were aware of AI through medical schools. There was a statistically significant difference in awareness among respondents who consider themselves tech experts compared with those who do not (P=.03). More than half of the respondents believed that AI could be used to diagnose diseases automatically (213/394, 54.1% agreement), with medical students agreeing more than pathology residents (P=.04). However, more than one-third expressed fear about recent AI developments (167/394, 42.4% agreed). Two-thirds of respondents disagreed that their medical schools had educated them about AI and its potential use (261/394, 66.2% disagreed), while 46.2% (182/394) expressed interest in learning about AI in medicine. In terms of pathology-specific questions, 75.4% (297/394) agreed that AI could be used to identify pathologies in slide examinations automatically. There was a significant difference between medical students and pathology residents in their agreement (P=.001). Overall, medical students and pathology trainees had similar responses. Conclusions: AI education should be introduced into medical school curricula to improve medical students’ understanding and attitudes. Students agreed that they need to learn about AI’s applications, potential hazards, and legal and ethical implications. This is the first study to analyze medical students’ views and awareness of AI in Jordan, as well as the first to include pathology residents’ perspectives. The findings are consistent with earlier research internationally. In comparison with prior research, these attitudes are similar in low-income and industrialized countries, highlighting the need for a global strategy to introduce AI instruction to medical students everywhere in this era of rapidly expanding technology.

Purpose: The current state of the art in process modeling of blood banking and transfusion services is not well grounded; methodological reviews are lacking to bridge the gap between such blood banking and transfusion processes (and their models) and their automation. This research aims to fill this gap with a methodological review. Methods: A systematic mapping study was adopted, driven by five key research questions. Identified research studies were accepted based on fulfilling the following inclusion criteria: 1) research studies should focus on blood banking and transfusion process modeling since the late 1970s; and 2) research studies should focus on process automation in relation to workflow-based systems, with papers classified into categories in line with the analysis undertaken to answer each of the research questions. Results: The search identified 22 papers related to modeling and automation of blood banking and transfusion, published in the period 1979– 2022. The findings revealed that only four process modeling languages were reported to visualize process workflows. The preparation of blood components, serologic testing, blood distribution, apheresis, preparation for emergencies, maintaining blood banking and transfusion safety, and documentation have not been reported to have been modeled in the literature. This review revealed the lack of use of Business Process Modeling Notation (BPMN) as the industry standard process modeling language in the domain. The review also indicated a deficiency in modeling specialized processes in blood banking and transfusion, with the majority of reported processes being described as high level, but lacking elaboration. Automation was reported to improve transfusion safety, and to reduce cost, time cycle, and human errors. Conclusion: The work highlights the non-existence of a developed process architectural framework for blood banking and transfusion processes, which is needed to lay the groundwork for identifying and modeling strategic, managerial, and operational processes to bridge the gap with their enactment in healthcare systems. This paves the way for the development of a data-harvesting platform for blood banking and transfusion services.

Timely diagnosis is a crucial factor in decreasing the death rate of patients with breast cancer. BI-RADS categories IVc and V indicate a strong suspicion of cancer. The categorisation of each group is determined by the characteristics of the lesion. Certain benign breast lesions might have radiological features indicative of malignancy; thus, biopsy is mandatory. This study aimed to identify the histopathological diagnosis of benign breast masses classified into BIRADS IVc and V subgroups, investigate the radiological characteristics of these masses, and identify ultrasound features that could lead to false positive results (benign lesions that mimic malignancy on imaging). This was a retrospective cross-sectional study at a single facility. Breast lesions reported as BIRADS IVc and V that underwent needle core/stereotactic vacuum-assisted biopsy were reviewed. Patients with benign pathologic diagnoses were analysed, delineating pathological diagnoses. Radiological descriptors were compared to those of a matched control of 50 malignant cases with BIRADS IVc. A total of 828 breast lesions classified as BIRADS IVc or V were detected during the period spanning from 2015 to 2022. Forty-four lesions (44/828, 5.3%) were benign at initial biopsy, while 784 lesions (784/828, 94.7%) were malignant. After histopathological testing and repeat biopsy, 26/828 (3.14%) patients had discordant benign diagnosis. Half of the repeated biopsies (10/20, 50%) showed malignant pathology. Compared to that in the control group, the presence of an oval shape of the mass was significantly more common in patients with benign pathology (p = 0.035). Conversely, the presence of posterior shadowing was significantly less common (p = 0.050) in benign lesions. No significant differences were observed for the other radiological characteristics. The most common histopathological diagnosis was fibrocystic change. This study highlights key findings regarding the sonographic imaging descriptors and histopathological diagnoses of benign breast lesions categorised as BIRADS IVc/V. The study recommends a correlation between clinical and radiological findings and encourages multidisciplinary decision-making among radiologists, pathologists, and clinicians to determine if a repeat biopsy is warranted. There is a need for continuous research to improve the diagnosis and treatment of breast lesions and reduce false-positive rates by incorporating other methodologies such as sonoelastography and incorporating deep learning and artificial intelligence in the decision-making to eliminate unnecessary procedures.

Background: Blood banks are an important part of healthcare systems. They embrace critical processes that start with donor recruitment and blood collection, followed by blood processing to produce different types of blood components used in transfusions, blood storage, blood distribution, and transfusion. Blood components must be generated at high quality, preserved safely, and transfused in a timely manner. This can be achieved by operating interrelated processes within a complex network. There is no comprehensive blueprint of Blood Banking and Transfusion (BB&T) processes and their relationships; therefore, this study aims to develop and evaluate a BB&T process architecture using the Riva method. Research design: This research adopts a design science research methodology process (DSRM) that aims to create artifacts for the purpose of serving humanity through six phases: identifying problems, identifying solutions and objectives, designing and developing artifacts, demonstrating and evaluating the artifacts, and communicating the work. The adapted DSRM process is used to build a process architecture in the BB&T unit to improve the quality and strategic planning of BB&T processes. Applying the adapted DSRM process generated four increments before the outcomes were communicated as a highly comprehensive BB&T process architecture (BB&TPA) blueprint for virtual organizations. Finally, the generated BB&TPA is tested and validated at a reference hospital. Results: A Riva-based process architecture diagram was successfully developed, acting as a reference model for virtual BB&T organizations. It is a novel output in the domain of BB&T and can also be considered as a reference model to evaluate the existing processes in BB&T real-world units. This assists domain experts in performing gap analysis in their BB&T units and paths for developing BB&T management information systems and can be incorporated in the inspection workflow of accreditation organizations.

This article reports a case of an ovarian collision tumour consisting of an ovarian fibroma and a serous cystadenoma. A 60-year-old woman exhibited symptoms of post-menopausal bleeding and abdominal pain persisting for three months. Computerized tomography identified a solid mass with a cystic component in the right adnexa, and the patient underwent staging laparotomy. Gross examination of the right ovary revealed a cystic tumour with adjacent solid mass. The histopathological analysis identified a cystic mass that matched the characteristics of a serous cystadenoma, with an adjacent solid mass that matched the characteristics of a sexcord stromal tumour, both located in the right ovary. Additionally, a small cyst that matched the characteristics of a serous cystadenoma was found in the left ovary. There have been only seven previously reported examples of this specific mix of ovarian tumours. Mostly affecting patients above 60 years of age, although tumour markers levels are normal, such cases may present with a complex clinical scenario, as in this case, and demand a comprehensive diagnostic and therapeutic approach.

To determine the attitude of the medical students towards the introduction of artificial intelligence (AI) in undergraduate medical students at Jordanian u