Artificial Intelligence and Healthcare: A Journey through History, Present Innovations, and Future Possibilities.

Artificial intelligence (AI) has emerged as a powerful tool in healthcare significantly impacting practices from diagnostics to treatment delivery and patient management. This article examines the progress of AI in healthcare, starting from the field's inception in the 1960s to present-day innovative applications in areas such as precision medicine, robotic surgery, and drug development. In addition, the impact of the COVID-19 pandemic on the acceleration of the use of AI in technologies such as telemedicine and chatbots to enhance accessibility and improve medical education is also explored. Looking forward, the paper speculates on the promising future of AI in healthcare while critically addressing the ethical and societal considerations that accompany the integration of AI technologies. Furthermore, the potential to mitigate health disparities and the ethical implications surrounding data usage and patient privacy are discussed, emphasizing the need for evolving guidelines to govern AI's application in healthcare.

Predictive Accuracy of Infant Clinical Sign Algorithms for Mortality in Young Infants Aged 0 to 59 Days: A Systematic Review.

CONTEXT: Clinical sign algorithms are a key strategy to identify young infants at risk of mortality. OBJECTIVE: Synthesize the evidence on the accuracy of clinical sign algorithms to predict all-cause mortality in young infants 0-59 days. DATA SOURCES: MEDLINE, Embase, CINAHL, Global Index Medicus, and Cochrane CENTRAL Registry of Trials. STUDY SELECTION: Studies evaluating the accuracy of infant clinical sign algorithms to predict mortality. DATA EXTRACTION: We used Cochrane methods for study screening, data extraction, and risk of bias assessment. We determined certainty of evidence using Grading of Recommendations Assessment Development and Evaluation. RESULTS: We included 11 studies examining 26 algorithms. Three studies from non-hospital/community settings examined sign-based checklists (n = 13). Eight hospital-based studies validated regression models (n = 13), which were administered as weighted scores (n = 8), regression formulas (n = 4), and a nomogram (n = 1). One checklist from India had a sensitivity of 98% (95% CI: 88%-100%) and specificity of 94% (93%-95%) for predicting sepsis-related deaths. However, external validation in Bangladesh showed very low sensitivity of 3% (0%-10%) with specificity of 99% (99%-99%) for all-cause mortality (ages 0-9 days). For hospital-based prediction models, area under the curve (AUC) ranged from 0.76-0.93 (n = 13). The Score for Essential Neonatal Symptoms and Signs had an AUC of 0.89 (0.84-0.93) in the derivation cohort for mortality, and external validation showed an AUC of 0.83 (0.83-0.84). LIMITATIONS: Heterogeneity of algorithms and lack of external validation limited the evidence. CONCLUSIONS: Clinical sign algorithms may help identify at-risk young infants, particularly in hospital settings; however, overall certainty of evidence is low with limited external validation.

Diagnostic Accuracy of Clinical Sign Algorithms to Identify Sepsis in Young Infants Aged 0 to 59 Days: A Systematic Review and Meta-analysis.

CONTEXT: Accurate identification of possible sepsis in young infants is needed to effectively manage and reduce sepsis-related morbidity and mortality. OBJECTIVE: Synthesize evidence on the diagnostic accuracy of clinical sign algorithms to identify young infants (aged 0-59 days) with suspected sepsis. DATA SOURCES: MEDLINE, Embase, CINAHL, Global Index Medicus, and Cochrane CENTRAL Registry of Trials. STUDY SELECTION: Studies reporting diagnostic accuracy measures of algorithms including infant clinical signs to identify young infants with suspected sepsis. DATA EXTRACTION: We used Cochrane methods for study screening, data extraction, risk of bias assessment, and determining certainty of evidence using Grading of Recommendations Assessment Development and Evaluation. RESULTS: We included 19 studies (12 Integrated Management of Childhood Illness [IMCI] and 7 non-IMCI studies). The current World Health Organization (WHO) 7-sign IMCI algorithm had a sensitivity of 79% (95% CI 77%-82%) and specificity of 77% (95% CI 76%-78%) for identifying sick infants aged 0-59 days requiring hospitalization/antibiotics (1 study, N = 8889). Any IMCI algorithm had a pooled sensitivity of 84% (95% CI 75%-90%) and specificity of 80% (95% CI 64%-90%) for identifying suspected sepsis (11 studies, N = 15523). When restricting the reference standard to laboratory-supported sepsis, any IMCI algorithm had a pooled sensitivity of 86% (95% CI 82%-90%) and lower specificity of 61% (95% CI 49%-72%) (6 studies, N = 14278). LIMITATIONS: Heterogeneity of algorithms and reference standards limited the evidence. CONCLUSIONS: IMCI algorithms had acceptable sensitivity for identifying young infants with suspected sepsis. Specificity was lower using a reference standard of laboratory-supported sepsis diagnosis.

Safe motherhood: A hidden reality in Pakistan.

OBJECTIVE: To explore the perceptions and experiences of healthcare professionals, including female health visitors, female health workers, community midwives, and heads of departments of healthcare facilities, regarding the importance of the Safe Motherhood Initiative (SMI), its pillars, and its foundational principles. DESIGN: Qualitative descriptive exploratory study. SETTING: This study was conducted in healthcare centres in 10 districts in Pakistan: six in Gilgit Baltistan, two in Chitral, and two in Sindh. PARTICIPANTS: Healthcare professionals were recruited using a purposive sampling technique. In total, 14 in-depth interviews were conducted. FINDINGS: The following themes emerged through thematic analysis: (1) health and well-being of mothers and newborns; (2) strengthening the SMI pillars; (3) equity of health services; and (4) effective strategies for behaviour modification. Each theme had two categories. KEY CONCLUSIONS: The health and well-being of mothers and newborns are key indicators; as such, interventions should be made to promote their quality of life. Capacity building and refresher training on antenatal care, childbirth, postnatal care, postabortion care, and family planning can be considered effective to enhance the competencies of healthcare professionals. IMPLICATIONS FOR PRACTICE: The findings of this study suggest that healthcare professionals should receive training to enhance their competencies and provide safe care.

A Review of the Past, Present, and Future of the Monkeypox Virus: Challenges, Opportunities, and Lessons from COVID-19 for Global Health Security.

Monkeypox, a rare but significant zoonotic and orthopoxviral disease, has garnered increasing attention due to its potential for human-to-human transmission and its recent resurgence in multiple countries throughout Europe, North America, and Oceania. The disease has emerged as a novel threat to the global health systems that are still striving to recover from the major shocks of the COVID-19 pandemic. The unusual manifestation of the illness highlights a substantial knowledge deficit and necessitates the immediate development of a public health action strategy, considering the epidemiological differences observed in the ongoing outbreak and the appearance of cases in non-endemic nations. This literature review aims to synthesize existing knowledge on monkeypox, encompassing its historical context, etiology, epidemiology, surveillance, prevention, transmission, clinical presentation, diagnosis, treatments, and recent outbreak. Particular attention is given to both advances and gaps in our understanding of monkeypox, and we point toward future directions for research and intervention efforts as pertains to vaccine development and distribution. Lastly, we will also review the recent outbreak through a sociopolitical lens as relates to decision-making strategies, especially given the lessons learned from COVID-19.