Physicians’ required competencies in AI-assisted clinical settings: a systematic review

British Medical Bulletin, Год журнала: 2025, Номер 153(1)

Опубликована: Янв. 9, 2025

Utilizing Artificial Intelligence (AI) in clinical settings may offer significant benefits. A roadblock to the responsible implementation of medical AI is remaining uncertainty regarding requirements for users at bedside. An overview academic literature on human adequate use therefore value. systematic review potential implications required competencies physicians as mentioned literature. Our findings emphasize importance physicians' critical skills, alongside growing demand technical and digital competencies. Concrete guidance AI-assisted remains ambiguous requires further clarification specification. Dissensus over whether are adequately equipped monitor terms competencies, skills expertise, issues ownership normative guidance, training skills. offers a basis subsequent research analysis settings. Future should clearly outline (i) how must be(come) competent working with settings, (ii) who or what take embedding these regulatory framework, (iii) investigate conditions achieving reasonable amount trust AI, (iv) assess connection between efficiency patient care.

Язык: Английский

---

A Personalized Multimodal BCI–Soft Robotics System for Rehabilitating Upper Limb Function in Chronic Stroke Patients

Biomimetics, Год журнала: 2025, Номер 10(2), С. 94 - 94

Опубликована: Фев. 7, 2025

Multimodal brain-computer interfaces (BCIs) that combine electrical features from electroencephalography (EEG) and hemodynamic functional near-infrared spectroscopy (fNIRS) have the potential to improve performance. In this paper, we propose a multimodal EEG- fNIRS-based BCI system with soft robotic (BCI-SR) components for personalized stroke rehabilitation. We novel method of personalizing rehabilitation by aligning each patient's specific abilities treatment options available. collected 160 single trials motor imagery using 10 healthy participants. identified confounding effect respiration in fNIRS signal data collected. Hence, incorporate breathing sensor synchronize (MI) cues participant's respiratory cycle. found implementing synchronization (RS) resulted less dispersed readings oxyhemoglobin (HbO). then conducted clinical trial on BCI-SR Four chronic patients were recruited undergo 6 weeks rehabilitation, three times per week, whereby primary outcome was measured upper-extremity Fugl-Meyer Motor Assessment (FMA) Action Research Arm Test (ARAT) scores 0, 6, 12. The results showed striking coherence activation patterns EEG across all patients. addition, FMA ARAT significantly improved week 12 relative pre-trial baseline, mean gains 8.75 ± 1.84 5.25 2.17, respectively (mean SEM). These improvements better than Standard Therapy group when retrospectively compared previous trials. suggest leads performance standard BCI-SR, synchronizing increased consistency HbO levels, leading proposed holds promise engage promote neuroplasticity improvements.

Язык: Английский

---

The Role of Artificial Intelligence in Obesity Risk Prediction and Management: Approaches, Insights, and Recommendations

Medicina, Год журнала: 2025, Номер 61(2), С. 358 - 358

Опубликована: Фев. 19, 2025

Greater than 650 million individuals worldwide are categorized as obese, which is associated with significant health, economic, and social challenges. Given its overlap leading comorbidities such heart disease, innovative solutions necessary to improve risk prediction management strategies. In recent years, artificial intelligence (AI) machine learning (ML) have emerged powerful tools in healthcare, offering novel approaches chronic disease prevention. This narrative review explores the role of AI/ML obesity management, a special focus on childhood obesity. We begin by examining multifactorial nature obesity, including genetic, behavioral, environmental factors, limitations traditional predict treat morbidity Next, we analyze techniques commonly used risk, particularly minimizing risk. shift application comparing perspectives from healthcare providers versus patients. From provider's perspective, offer real-time data electronic medical records, wearables, health apps stratify patient customize treatment plans, enhance clinical decision making. patient's AI/ML-driven interventions personalized coaching long-term engagement management. Finally, address key challenges, determinants embracing while our recommendations based literature review.

Язык: Английский

---

Evaluating AI adoption in healthcare: Insights from the information governance professionals in the United Kingdom

International Journal of Medical Informatics, Год журнала: 2025, Номер 199, С. 105909 - 105909

Опубликована: Апрель 6, 2025

Artificial Intelligence (AI) is increasingly being integrated into healthcare to improve diagnostics, treatment planning, and operational efficiency. However, its adoption raises significant concerns related data privacy, ethical integrity, regulatory compliance. While much of the existing literature focuses on clinical applications AI, limited attention has been given perspectives Information Governance (IG) professionals, who play a critical role in ensuring responsible compliant AI implementation within systems. This study aims explore perceptions IG professionals Kent, United Kingdom, use delivery research, with focus governance, considerations, implications. A qualitative exploratory design was employed. Six from NHS trusts Kent were purposively selected based their roles compliance, policy enforcement. Semi-structured interviews conducted thematically analysed using NVivo software, guided by Unified Theory Acceptance Use Technology (UTAUT). Thematic analysis revealed varying levels knowledge among professionals. participants acknowledged AI's potential efficiency, they raised about accuracy, algorithmic bias, cybersecurity risks, unclear frameworks. Participants also highlighted importance need for national oversight. offers promising opportunities healthcare, but must be underpinned robust governance structures. Enhancing literacy teams establishing clearer frameworks will key safe implementation.

Язык: Английский

---

Artificial intelligence is going to transform the field of endocrinology: an overview

Frontiers in Endocrinology, Год журнала: 2025, Номер 16

Опубликована: Янв. 14, 2025

1. IntroductionThe field of clinical endocrinology, as well healthcare in general, is facing a transformative change by new technologies, especially artificial intelligence (AI). AI holds the promise to dramatically improve way we screen, diagnose, treat, monitor, and coach patients (1, 2). Not only will tools make flow endocrine decision-making faster more reliable, use opens personalized treatment plans tailored individual patient characteristics (3, 4). within computer science that encompasses machine learning (ML). ML uses mathematical algorithms designed predictions or classifications. These models are typically trained on known, labeled datasets iteratively enhanced gain capability accurate unseen data (5). Deep (DL), subset ML, complex mimic human central nervous system. DL entails neural networks (ANNs). ANNs consist interconnected layers pass information optimize minimizing error (6). Once trained, can process vast perform tasks such predictions, classifications, even advanced applications like large language (LLMs), vision, multimedia generation from text inputs (7-9). We anticipating an unprecedented disruption endocrinology AI. Nevertheless, most clinicians lack proper understanding potential one hand, shortcomings caveats other hand. A balanced comprehension underpinnings imperative maximize its benefits. Hence, providers must familiarize themselves with this technology but also understand limitations. Table 1 gives overview differences between AI-based conventional methods endocrinology.The aim paper give future direction domain diabetes. 2. Improved Risk AssessmentThe importance timely risk assessment well-established, significantly enhance both speed efficiency. For instance, Wändell colleagues created tool evaluate having de novo diabetes using stochastic gradient boosting model. Area under curve (AUC) was 0.773 0.825, indicating good discriminatory power (10). The important factors were identified being arterial hypertension obesity. model, adults over 30 years old Stokholm, Sweden included. No given relation ethnicity. Yousef co-workers used interpretable model for prediction undiagnosed type 2 mellitus. subjects study rural screening clinic Albury, Australia. Ethnicity not recorded. They two different Isolated Forest (iForest) algorithms. first basis BMI (body mass index), blood glucose level, triglycerides. second iForest same parameters, supplemented biomarkers oxidative stress (8-isoprostane, 8-hydroxydeoxyguanosine, oxidized glutathione), inflammation (interleukin-6, interleukin-10, interleukin-1β, insulin-like growth factor-1), mitochondrial dysfunction (humanin, MOTS-c, P66Shc). latter outperformed former one; F1-score increased 0.61 0.81 (11). In another study, Nabrdalik et al. stratification MASLD (metabolic dysfunction-associated steatotic liver disease) Patients recruited diabetology ward hospital Zabrze, Poland. initially 80 parameters. To determine discriminative predictors, feature selection conducted chi-squared test. stability rendered variables assured repeating Monte-Carlo simulation 1,000 times. independent employed multiple logistic regression order predict occurrence (12). has been hypoglycemia Cichosz developed binary classification XGBoost (extreme boost) algorithm aim, CGM (continuous monitoring) 206 United States. More than 90% white, non-Hispanic. Their median age 68 years. validated cohorts. total 61,470 weeks included analysis. demonstrated strong performance, ROC-AUCs (area receiver operating characteristic curve) ranging 0.90 across validation cohorts (13). shows osteoporosis assessment. Hong argue could be very beneficial prone osteoporotic fractures. An individualized approach management believed reach help cutting-edge (14). This potentially reduce morbidity mortality, costs alleviate workload providers. Assessment thyroid nodules challenging at Distinguishing benign malignancy paramount care. Wildman-Tobriner therefore system ultrasound images nodules, imaging reporting (AI TI-RADS). 378 320 study. Subjects' collected electronic health records Duke University Medical Center, Durham, NC, demographics mentioned, besides sex. All underwent fine needle aspiration cytology. Results TI-RADS comparable ACR (American College Radiology Thyroid Imaging Reporting Data Systems) (15). AI-driven expected diagnostic performance near future. Still challenges remain, inconsistent ratings physicians, uncertainty cytopathological diagnosis difficulty discriminating follicular lesions (16). hold promising As availability increases, comprehensive emerge. 3. Better Faster DiagnosisThe prove great benefit diagnostics. diagnosis, endocrinologists rely presentation, history, lab results technical examinations, medical imaging. Usually quite straightforward. However, occasionally doctors confronted cases where might increase accuracy. Chia co-workers, example, diagnose retinopathy (DR) indigenous Australian patients. Aboriginal Community Controlled Health Service located metropolitan area Perth, Western retina specialist terms sensitivity; specificity (17). Joseph performed systematic review which 34 studies carried out Asia (57%), Europe (20%), North America (12%), Australia (7%), Africa (2%) South (2%). findings indicate fact acceptable DR. Fundus compared graders. software conjunction fundus camera indeed facilitate work ophthalmologists accuracy (18). Wu linear random forest (RF) laboratory 479 patients, mellitus, neuropathy lower limb disease Tongji Hospital, Shanghai, China. proved diagnosing comparison models. Coversely, RF revealed suitable detecting peripheral vascular (19). some crucial outcome. Here automatic interpretation provoke alarm, so physician swiftly attend patient. Tirado-Aguilar underscored gestational avoid adverse neonatal maternal outcomes (20). become normal daily practice stethoscope today. bound see staggering progression come. 4. Personalized treatmentsOne opportunities possibility forge distinctions. medicine thus coming reach. decades come, leave one-size-fits-all shift towards optimized therapies highest efficiency while limiting effects. Long models, 9 responders versus non-responders metformin Beijing Friendship Capital University, Beijing, F1 scores XGBoost, KNN (K-Nearest Neighbors) , NB (Naive Bayes), SVM (Support Vector Machine) 0.830, 0.517, 0.898, 0.864 0.475, respectively (21). strategy expanded drugs compose best possible each Popova performing trial app women mellitus control their glycemic levels. outpatient department Perinatal Center Almazov National Research antenatal clinics, all Saint Petersburg, Russia. Prognoses level hour postprandial every time they input meal (22). anticipate adjust current manner hyperglycemia. Closed loop pancreas systems exciting greatly quality life Several already (23). helpful pathologies assisting test prescriptions, guide them interpretation, (24). mainstream once AI-tools sufficiently accepted. 5. Monitoring distanceThe too overloaded, going problem worse future; AI-enhanced distance monitoring mitigate problem. part solution may lie wearable devices sensors monitor without need direct oversight professional. Promphet introduced sensing device smartphone levels applying regressor Subject mentioned empower take doctor (25). diverse Juyal detect subtle patterns real (26). Besides kind sophisticated increasing wearables nowadays. provide interesting development forthcoming distinguishing immediate attention, those withstand delay. However technologies are, problems remain solved. Privacy concerns necessitate high encryption. And cost subject discussion. It conceivable, however, savings would offset investment long run. 6. Ethical considerations limitations AIDespite enormous there hinder widespread adoption technology. limitation dependent they're on. Missing data, incorrectly errors, mistakes rise inaccurate (27). constraint lies generalizability problematic certain ethnic groups, geographical locations, social strata, gender, category, apply aligned training operates (28). third hurdle privacy regarding sensitive (29). Certain regulations have adhered to. complicate inhibit application situations. last impediment relates issue liability. remains unclear whether responsible outcomes, if company providing final responsibility (30). There still ethical questions answered before fully embraced endocrinology. 7. ConclusionsIn conclusion, revolutionize enhancing offering treatments, allowing remote monitoring. transformation realized, professionals proactively embrace AI, benefits Without adequate preparation comprehension, miss pivotal opportunity care through groundbreaking essential engage responsibly, ensuring equipped navigate promises practical challenges.

Язык: Английский

---

A roadmap to implementing machine learning in healthcare: from concept to practice

Frontiers in Digital Health, Год журнала: 2025, Номер 7

Опубликована: Янв. 20, 2025

Background The adoption of machine learning (ML) has been slow within the healthcare setting. We launched Pediatric Real-world Evaluative Data sciences for Clinical Transformation (PREDICT) at a pediatric hospital. Its goal was to develop, deploy, evaluate and maintain clinical ML models improve patient outcomes using electronic health records data. Objective To provide examples from PREDICT experience illustrating how common challenges with deployment were addressed. Materials methods present in developing deploying related following: identify scenarios, establish data infrastructure utilization, create operations integrate into workflows. Results show these overcome suggestions pragmatic solutions while maintaining best practices. Discussion These approaches will require refinement over time as number deployments increase.

Язык: Английский

---

Leveraging AI to optimize vaccines supply chain and logistics in Africa: opportunities and challenges

Frontiers in Pharmacology, Год журнала: 2025, Номер 16

Опубликована: Фев. 10, 2025

Examining the current situation of vaccine supply chain in Africa, article highlights importance AI technologies while outlining prospects and problems management Africa. Despite significance vaccinations, many African children are unable to receive them due logistical challenges a lack infrastructure. has potential increase productivity by streamlining logistics inventory management, but it is hampered issues with data privacy technology This perspectiveoffers ways for utilizing enhance chains citing successful experiences Nigeria, Malawi, Rwanda, Ghana as examples AI’s advantages. In order improve healthcare outcomes immunization coverage cooperation among stakeholders stressed.

Язык: Английский

---

Benefits and Project Management to Improve Success of IS/IT Projects in Healthcare

IGI Global eBooks, Год журнала: 2025, Номер unknown, С. 313 - 354

Опубликована: Март 7, 2025

The rapidly changing business environment is putting growing pressure on organizations to deliver successful projects that align with their strategic objectives. As a result, the use of emerging information systems and technology (IS/IT) has expanded significantly across various sectors, healthcare being major area focus. Two critical factors have driven surge in Health IS/IT investments. First, rising burden chronic diseases led costs increasing at much faster pace. Second, there recognized need greatly improve quality safety health delivery. These strong investments enhance speed accuracy sharing, which crucial for supporting clinical decision-making. However, many implementations faced low success rates. authors suggest by integrating Project Management approach Benefits approach, can these outcomes, ensuring effective realization benefits from project success.

Язык: Английский

---

Artificial Intelligence for Medication Management in Discordant Chronic Comorbidities: An Analysis from Healthcare Provider and Patient Perspectives

Information, Год журнала: 2025, Номер 16(3), С. 237 - 237

Опубликована: Март 17, 2025

Recent advances in artificial intelligence (AI) have created opportunities to enhance medical decision-making for patients with discordant chronic conditions (DCCs), where a patient has multiple, often unrelated, conflicting treatment plans. This paper explores the perspectives of healthcare providers (n = 10) and 6) regarding AI tools medication management. Participants were recruited through two centers, interviews conducted via Zoom. The semi-structured (60–90 min) explored their views on AI, including its potential role limitations decision making management DCCs. Data analyzed using mixed-methods approach, semantic analysis grounded theory, yielding an inter-rater reliability 0.9. Three themes emerged: empathy AI–patient interactions, support AI-assisted administrative tasks, challenges complex diseases. Our findings suggest that while can decision-making, effectiveness depends complementing human judgment, particularly empathetic communication. also highlights importance clear AI-generated information need future research embedding ethical standards systems.

Язык: Английский

---

AI-IoT integration in Tanzania’s primary healthcare system: a narrative review

Journal of Health Organization and Management, Год журнала: 2025, Номер unknown

Опубликована: Март 24, 2025

Purpose This narrative review explores the integration of artificial intelligence (AI) and Internet Things (IoT) technologies in Tanzania’s primary healthcare system. It aims to identify barriers adoption, propose strategies for effective implementation align these insights with digital health transformation goals. Design/methodology/approach A methodology was employed, synthesising evidence from 21 peer-reviewed studies reports published between 2015 2024. The thematic analysis examined barriers, research gaps, focusing on technical, socio-cultural organisational factors specific context. Findings highlights several challenges, including infrastructural limitations, low literacy, resistance lack robust policy frameworks. Strategies such as participatory system design, capacity building investments resilient infrastructure emerged critical enablers. Insights also underscore importance addressing ethical considerations customising solutions unique socio-economic cultural realities. Originality/value study uniquely focuses Tanzanian context, providing actionable recommendations bridge gap AI-IoT technological potential practical low-resource settings. Integrating global local offers a comprehensive framework guide policymakers, practitioners stakeholders advancing innovations personalised needs systems.

Язык: Английский

---