Eisenhower Foundation

AI has become a disruptive technology in healthcare that is using the breakthroughs in computer science to improve the fields of diagnostics, treatment planning, patient monitoring, and medical research. In this review, the author discusses the historical development of AI starting with the early rule-based systems about modern machine learning, deep learning, and natural language processing applications. The most important areas of healthcare use are medical imaging, predictive analytics, personalized medicine and robotic-assisted surgery. Issues of data quality, interpretability, ethical issues, and regulation are mentioned, and novel trends like federated learning, generative AI, and human-AI collaboration are stated, and AI has potential to enhance patient outcomes and healthcare efficiency.

Much of the fake news and misinformation peddling can be attributed to this quick development in Internet of Things, and the web related social media platforms. In order to enable the detection of actual fake news, our present research suggests a robotic AI model through text and sensor data. The combination of the holistic model that is suggested consists of sensor-based cross-checking (confidence, location, time synchronization and anomaly detection) and RoBERTa transformer models to interpolate textual contents. Baselines were also used to compare model with a PolitiFact, LIAR and FakenewsNet datasets baselines of text only and sensor data only. Experimental results have demonstrated that the hybrid strategy has shown improved performance with all results, with much more accurate and reliable detection with the consideration of physical context. Findings indicate the potential of sensor-enhanced AI-based systems to reduce the risk of misinformation with regard to IoT-connecting ecosystems, which may inform the course of action with regard to the development of reliable, smart and context-sensitive digital media surveillance systems.

Artificial intelligence (AI) is taking the healthcare field by storm as healthcare providers adopt its use to inform data-based decisions, improve clinical decision-making, and make their operations more efficient. This review discusses the fundamentals of AI, including machine learning, deep learning, and natural language processing technologies and how they can be applied to diagnostics, individualized treatment, remote patient monitoring, hospital operations, and population health monitoring. The strengths of AI are the ability to identify early disease, custom care plans, and precognitive analysis to direct resources. Nevertheless, integration in healthcare systems is stalled by risk of having biased algorithms, data privacy, interoperability, and changing demands of regulatory guidelines. A solution to such barriers is interdisciplinary: combining multiple views to develop and validate the models legitimately, with transparency and trustworthiness. Future trends, such as explainable AI, federated learning and integration of the robots aim at a more flexible and patient-centered future. After all, the best role that AI can play is to augment human expertise by providing more precise, proactive and fair care but without losing that critical human touch in healthcare.

This study examines the cost-benefit dynamics of cybersecurity compliance across the financial, energy, and military intelligence sectors, utilising open-access datasets from the BLS, Statista, Verizon DBIR, ENISA, and IBM. A one-way ANOVA, chi-square test, cost-benefit analysis, and logistic regression were employed to analyse sectoral differences in compliance costs, breach reductions, return on compliance investment (ROCI), and institutional success factors. Results show that financial institutions incur the highest average compliance costs ($30.94 million), while military intelligence yields the highest Return on Capital Invested (ROCI) (1.67). Compliance reduces breach incidents by 57.14% in finance, 50% in energy, and 48.57% in intelligence. Regression findings highlight workforce capacity (β = 3.06, p = .019) and training investment (β = 0.00044, p < .001) as strong predictors of compliance success, while budget constraints significantly hinder outcomes (β = -4.11, p < .001). It is recommended that regulatory bodies develop sector-specific frameworks, expand budget flexibility, institutionalise role-specific training, and incentivise automation and intelligence sharing.

This study examined the effects of e-coaching on the implementation of a functional assessment-based intervention delivered by an early intervention provider in reducing challenging behaviors during home visits. A multiple baseline design across behavior support plan components was used with a provider-child dyad. The e-coaching intervention consisted of weekly training and support delivered via video conferencing software. Results demonstrated a functional relation between e-coaching and early intervention provider implementation of targeted behavior support plan strategies. Further, the child's challenging behaviors decreased over the course of the study. Contributions to the literature, implications for practice, and future directions are discussed.

Eighteen college freshmen were given a list of “arousal” or “relaxation” side effects after taking a placebo pill. All were then aroused by experiencing failure on a test supposedly predictive of college success. It was predicted that Ss who made an emotional appraisal of the situation (those who could not attribute