Search

Research on the applications of artificial intelligence (AI) tools in medicine has increased exponentially over the last few years but its implementation in clinical practice has not seen a commensurate increase with a lack of consensus on implementing and maintaining such tools. This systematic review aims to summarize frameworks focusing on procuring, implementing, monitoring, and evaluating AI tools in clinical practice. A comprehensive literature search, following PRSIMA guidelines was performed on MEDLINE, Wiley Cochrane, Scopus, and EBSCO databases, to identify and include articles recommending practices, frameworks or guidelines for AI procurement, integration, monitoring, and evaluation. From the included articles, data regarding study aim, use of a framework, rationale of the framework, details regarding AI implementation involving procurement, integration, monitoring, and evaluation were extracted. The extracted details were then mapped on to the Donabedian Plan, Do, Study, Act cycle domains. The search yielded 17,537 unique articles, out of which 47 were evaluated for inclusion based on their full texts and 25 articles were included in the review. Common themes extracted included transparency, feasibility of operation within existing workflows, integrating into existing workflows, validation of the tool using predefined performance indicators and improving the algorithm and/or adjusting the tool to improve performance. Among the four domains (Plan, Do, Study, Act) the most common domain was Plan (84%, n = 21), followed by Study (60%, n = 15), Do (52%, n = 13), & Act (24%, n = 6). Among 172 authors, only 1 (0.6%) was from a low-income country (LIC) and 2 (1.2%) were from lower-middle-income countries (LMICs). Healthcare professionals cite the implementation of AI tools within clinical settings as challenging owing to low levels of evidence focusing on integration in the Do and Act domains. The current healthcare AI landscape calls for increased data sharing and knowledge translation to facilitate common goals and reap maximum clinical benefit., Competing Interests: MPS is a co-inventor of intellectual property licensed by Duke University to Clinetic, Inc., KelaHealth, Inc, and Cohere-Med, Inc. MPS holds equity in Clinetic, Inc. MPS has received honorarium for a conference presentation from Roche. MPS is a board member of Machine Learning for Health Care, a non-profit that convenes an annual research conference. SB is a co-inventor of intellectual property licensed by Duke University to Clinetic, Inc. and Cohere-Med, Inc. SB holds equity in Clinetic, Inc., (Copyright: © 2024 Khan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The advent of artificial intelligence (AI) technologies has emerged as a promising solution to enhance healthcare efficiency and improve patient outcomes. The objective of this study is to analyse the knowledge, attitudes, and perceptions of healthcare professionals in Pakistan about AI in healthcare. We conducted a cross-sectional study using a questionnaire distributed via Google Forms. This was distributed to healthcare professionals (e.g., doctors, nurses, medical students, and allied healthcare workers) working or studying in Pakistan. Consent was taken from all participants before initiating the questionnaire. The questions were related to participant demographics, basic understanding of AI, AI in education and practice, AI applications in healthcare systems, AI's impact on healthcare professions and the socio-ethical consequences of the use of AI. We analyzed the data using Statistical Package for Social Sciences (SPSS) statistical software, version 26.0. Overall, 616 individuals responded to the survey while n = 610 (99.0%) of respondents consented to participate. The mean age of participants was 32.2 ± 12.5 years. Most of the participants (78.7%, n = 480) had never received any formal sessions or training in AI during their studies/employment. A majority of participants, 70.3% (n = 429), believed that AI would raise more ethical challenges in healthcare. In all, 66.4% (n = 405) of participants believed that AI should be taught at the undergraduate level. The survey suggests that there is insufficient training about AI in healthcare in Pakistan despite the interest of many in this area. Future work in developing a tailored curriculum regarding AI in healthcare will help bridge the gap between the interest in use of AI and training., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Habib et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Low- middle-income countries, including Pakistan, are facing significant obstacles in their efforts to achieve the global targets for maternal, newborn, and child health (MNCH) defined by the Sustainable Development Goals. Barriers at the individual, health system, and contextual levels undermine healthcare access for pregnant women and children, disproportionately affecting those in low-resource settings. To address these challenges in the high-mortality, peri-urban areas of Karachi, VITAL Pakistan Trust and Aga Khan University launched a digital health intervention (DHI) to stimulate demand for health services and streamline care management for health workers at the primary care level. In this case study, we present a narrative review of the design, development, and deployment of the DHI, an Android-based application, in accordance with the Principles for Digital Development. We draw on the initial experience with implementation to reflect on how each of the nine Principles was considered during different phases of the project lifecycle, focusing on the lessons learned and challenges encountered during this process. By engaging with end-users and understanding the community, we were able to map existing relationships and workflows onto a digital platform to address major challenges hindering service delivery. Leveraging insights from field observations and user feedback, we collaborated with experts in healthcare and technology to develop the DHI, which has now scaled to 44 peri-urban settlements in Karachi. Our experience underscores the value of substantiated frameworks like the Principles. However, on-ground challenges reveal important caveats requiring further assessment. These include building community trust in new digital systems and ensuring the ethical use of health data, particularly in low digital and data literacy contexts. Based on this understanding, we share recommendations for conditions central to the effective integration and uptake of technology in healthcare, specifically within the context of digital health for MNCH., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Ahmer et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)