Diagnosis Of Illness Research Articles

Investigating the association between metabolic syndrome conditions and perinatal mental illness: a national administrative claims study

BackgroundAlthough the association between mental disorder and metabolic syndrome as a bidirectional relationship has been demonstrated, there is little knowledge of the cumulative and individual effect of these conditions on peripartum mental health. This study aims to investigate the association between metabolic syndrome conditions (MetS-C) and maternal mental illness in the perinatal period, while exploring time to incident mental disorder diagnosis in postpartum women.MethodsThis observational study identified perinatal women continuously enrolled 1 year prior to and 1 year post-delivery using Optum’s de-identified Clinformatics® Data Mart Database (CDM) from 2014 to 2019 with MetS-C i.e. obesity, diabetes, high blood pressure, high triglycerides, or low HDL (1-year prior to delivery); perinatal comorbidities (9-months prior to and 4-month postpartum); and mental disorder (1-year prior to and 1-year post-delivery). Additionally, demographics and the number of days until mental disorder diagnosis were evaluated in this cohort. The analysis included descriptive statistics and multivariable logistic regression. MetS-C, perinatal comorbidities, and mental disorder were assessed using the International Classification of Diseases, Ninth, and Tenth Revision diagnosis codes.Results372,895 deliveries met inclusion/exclusion criteria. The prevalence of MetS-C was 13.43%. Multivariable logistic regression revealed prenatal prevalence (1.64, CI = 1.59–1.70) and postpartum incident (1.30, CI = 1.25–1.34) diagnosis of mental health disorder were significantly higher in those with at least one MetS-C. Further, the adjusted odds of having postpartum incident mental illness were 1.51 times higher (CI = 1.39–1.66) in those with 2 MetS-C and 2.12 times higher (CI = 1.21–4.01) in those with 3 or more MetS-C. Young women (under the age of 18 years) were more likely to have an incident mental health diagnosis as opposed to other age groups. Lastly, time from hospital discharge to incident mental disorder diagnosis revealed an average of 157 days (SD = 103 days).ConclusionThe risk of mental disorder (both prenatal and incident) has a significant association with MetS-C. An incremental relationship between incident mental illness diagnosis and the number of MetS-C, a significant association with younger mothers along with a relatively long period of diagnosis mental illness highlights the need for more screening and treatment during pregnancy and postpartum.

Delusion of pregnancy: Case series.

Delusion of pregnancy is a relatively uncommon manifestation in psychotic patients. Differentiating it from closely related clinical entities like pseudocyesis/pseudopregnancy can be a challenge in clinical setting. Diagnosis of a psychotic illness with delusional pregnancy as the presenting feature in the absence of other florid symptoms of psychosis is a different challenge altogether. Currently, there is no consensus on the pharmacotherapy and psychotherapy of delusional pregnancy. We present a case series on delusional pregnancy with the intent to answer some of these gaps in the literature.

Associations between violence, distress, and cancer in transgender and non-transgender people.

10566 Background: Due to transphobia, transgender people experience extreme rates of violence and distress. In other populations, violence and distress have been linked to increased cancer morbidity and mortality likely due to increased inflammation and higher rates of cancer risk factors. Methods: To evaluate associations between gender identity, violence, distress, cancer risk factors, and cancer, we developed an a priori structural equations model (SEM) and conducted a cross-sectional cohort study using electronic medical record data entered between 2006-2021 at a New York academic hospital. Participants were adults identified as transgender (N=894) by diagnosis codes, keyword searches, and structured gender identity information. We used age and follow-up time to match this cohort with participants not identified as transgender (N=1788) (2:1). We noted experiences of violence using keyword searches, diagnosis codes, and a screening question, “Are you safe at home?” We assessed distress as a latent factor based on mental illness diagnosis codes and keyword searches and diagnosis codes related to childhood violence. Cancer risk factors included ever smoking, body mass index ≥ 30, and history of HIV, HPV, or hepatitis B or C. We made unadjusted comparisons of violence, distress, cancer risk factors, and cancer in transgender and non-transgender people using two-sample chi-square tests. Our SEM included a direct path from gender identity to cancer or precancerous lesions as well as indirect paths through intermediaries of adult violence, distress, and cancer risk factors. We assessed direct paths for statistical significance using z-tests and indirect paths using bias-corrected bootstrap confidence intervals. Results: The association between gender identity and probability of cancer transmitted through the intermediate variables of violence, distress, and having multiple cancer risk factors was significant (p=0.05). Conclusions: In our model, transgender people experienced high rates of violence and distress, both of which were associated with cancer risk factors and cancer. Transphobia and violence experienced by transgender people must be addressed and mitigated. More work is needed to better understand associations between violence, distress, and cancer risk. [Table: see text]

Characterizing personal recovery in severe mental illness: French psychometric validation of the Questionnaire about the Process of Recovery (QPR-Fr).

The support of the personal recovery of people with lived experience of mental illness is a major issue in clinical practice. Thus, a valid instrument to assess personal recovery is needed. The present study aimed to validate the French translation of the 22-item Questionnaire about the Process of Recovery (QPR-Fr). A convenience sample of 222 participants reporting a severe mental illness diagnosis was recruited online. Psychometric properties of the QPR-Fr were evaluated. A confirmatory factor analysis was conducted for structural validity. Internal consistency and test-retest reliability were assessed. To test for convergent validity, we conducted multiple linear regression analysis to explore the QPR-Fr associations with psychological distress and the CHIME framework (with Connectedness, Hope and optimism about the future, Identity, Meaning in life, and Empowerment) proxy measures (perceived social support, hope, self-esteem, quality of life, and empowerment). An adequate fit was found for a 19-item unidimensional factor structure. Internal consistency was excellent. Test reliability was good. The QPR-Fr total score was significantly positively associated with quality of life, hope, self-esteem, and social support satisfaction and negatively associated with psychological distress. No significant association was found with social support availability nor with empowerment. This study provides additional data to support the cross-cultural validity of the Questionnaire about the Process of Recovery. The QPR-Fr is a valid and reliable tool to assess personal recovery. Practitioners could use the QPR-Fr to assess personal recovery in collaboration with people with lived experience. Convergent validity with CHIME proxy measures supports the validity of the CHIME framework in a French cultural context. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

An efficient healthcare system by cloud computing and clustering-based hybrid machine learning algorithm

Cloud computing, deep learning, clustering, genetic, and ensemble algorithms in healthcare are gaining popularity. This research highlights the relevance and complex repercussions of this integration. Cloud computing is transforming healthcare by providing scalable data storage and application access. It streamlines data exchange between hospitals, researchers, and institutions. Deep learning allows healthcare systems to use artificial intelligence for diagnostics, predictive analytics, and customized medication. Clustering algorithms segment patients, improving therapy and intervention customization. Genetic algorithms can optimize healthcare processes like treatment planning and resource allocation. Ensemble algorithms combine multiple models to improve predicted accuracy, enabling strong healthcare decision-making. This connection has several benefits. Healthcare systems become more efficient and scalable, resulting in cost-effective resource allocation. Access to patient data and apps promotes collaborative research and real-time healthcare. Deep learning algorithms can recognize complex medical data patterns, improving illness diagnosis and treatment results. Clustering algorithms streamline customized healthcare by stratifying individuals by clinical variables. Genetic algorithms optimize resource allocation, assuring healthcare resource efficiency. Ensemble algorithms improve predicted accuracy and clinical decision support system dependability. Its efficiency, accessibility, and prediction accuracy are positives, but security, resource constraints, interpretability, and ethical issues are obstacles.

Self-cross-linking synthesis of CuO for dual-mode sensing of hydrogen peroxide

Hydrogen peroxide (H2O2) detection is crucial for both illness diagnosis and environmental monitoring. This paper reports on a sustainable strategy that involves ion exchange using copper ions and sodium alginate as chelating agents to synthesize CuO nanoflakes. X-ray diffraction, transmission electron microscopy, scanning electron microscopy, X-ray photoelectron spectroscopy, and nitrogen adsorption-desorption curves were used to characterize the material. The catalytic oxidation of 3,3′,5,5′-tetramethylbenzidine (TMB) to a blue-colored ox TMB in the presence of H2O2 demonstrated the peroxidase-like activities of CuO. Hydrogen peroxide was detected using a dual-mode sensing platform built on the basis of colorimetry and electrochemistry. The dual-mode sensing platform created can be analytically applied widely, and this work offers a green and ecologically friendly synthesis technique.

Serum Soluble IL-2 Receptors Are Elevated in Febrile Illnesses and Useful for Differentiating Clinically Similar Malignant Lymphomas from Kikuchi Disease: A Cross-Sectional Study.

Background: The use of serum soluble interleukin 2 receptor (sIL-2R) for the diagnosis of febrile illnesses has not been examined. In this study, febrile patients were classified according to etiology and disease, and serum sIL-2R levels were evaluated. We determined whether serum sIL-2R is a useful marker for differentiating between malignant lymphoma (ML) and non-ML patients and between patients with ML and Kikuchi disease, which present similar clinical manifestations. Methods: This study was a cross-sectional study and included 344 patients with uncomplicated hemophagocytic syndrome, who had a fever of 38 °C or higher within 1 week of admission to our institution. Patient serum sIL-2R was measured, and the serum sIL-2R values are shown as median and IQR. Results: Serum sIL-2R increased above the upper reference limit in all disease groups with fever. The serum sIL-2R level in ML patients (n = 13) was 4760 (2120-6730) U/mL and significantly higher (p < 0.001) than the level of 998 (640-1625) U/mL in non-ML patients (n = 331). The serum sIL-2R level in ML patients (n = 13) was also significantly higher (p < 0.001) compared with that in patients with Kikuchi disease (n = 20; 705 (538-1091) U/mL). Conclusions: Serum sIL-2R tends to exceed the upper reference limit in patients with febrile illnesses. We conclude that the measurement of serum sIL-2R is useful for differentiating ML from non-ML and ML from Kikuchi disease.

Exploring Data Science: Methods, Models, and Applications

Extracting useful insights from data has become essential for businesses, researchers, and politicians alike in the digital age, as information is created at an unparalleled rate. In order to analyze and understand big information in order to find patterns, trends, and correlations, a wide range of approaches, techniques, and tools have come together to form the interdisciplinary subject of data science. Data science is essential for decision-making and innovation in a variety of fields, from supply chain optimization to illness diagnosis and consumer behavior prediction.

Trajectories of Purpose in Life Across Diagnosis of Alzheimer's Disease or Related Dementias in Older Adulthood.

Objectives: Sense of purpose serves as a psychological resource for late-life health. Limited research has examined how purpose changes across chronic illness diagnoses, including a diagnosis of Alzheimer's disease and related dementias (ADRD).Method: This study leverages data from the National Health and Aging Trends Study (NHATS; N = 831) to establish trajectories of purpose across ADRD diagnosis using three-year (pre-diagnosis, year of diagnosis, post-diagnosis) timespans and two comparison groups (heart disease diagnosis, no diagnosis). Results: We observed a longitudinal decrease in purpose in the ADRD diagnosis group, t = 3.85, p = .003, d = -0.258. This contrasts with heart disease diagnosis and no diagnosis groups, where purpose did not change over time. Discussion: Older adults who receive an ADRD diagnosis may be vulnerable to a decreasing sense of purpose across their diagnosis experience. We consider social, psychological, and clinical antecedents for this trend.

A Novel Secure and Robust Encryption Scheme for Medical Videos, Images and Reports

The use of medical imaging in telemedicine insurance and other applications has grown significantly in recent years. imaging techniques used in medicine, such as ultrasound, X-rays, computed tomography, and magnetic resonance imaging (MRI). plays a crucial part in the diagnosis of numerous illnesses. The internet has quickly advanced in terms of sharing and transferring large volumes of data. Medical image transmission can be impacted by a variety of attacks and medical image kinds. putting forth a medical data security strategy and outlining the various obstacles that it faces. This strategy attains a high degree of security and withstands many attacks over an extended period of time. A thorough analysis of security methods, including steganography, encryption, and compression, is presented along with a comprehensive assessment of current studies. Ensuring the security of medical images during transmission is crucial in safeguarding patient privacy. Steganography and cryptography are needed for secure transmission in order to maintain data integrity and confidentiality. An application to embed a video or audio content in another file is the project's development. Its focus is on securely and robustly embedding information under a seemingly innocent shroud. This system uses the ideas of cryptography and steganography to make the data more secure. Using an encryption technique that is resilient to various attacks over time is essential for improving the cryptography portion of the system. The paper's goal is to provide an overview and evaluation of each approach's various algorithms using several metrics, including PSNR, MSE, BER, and NC.

Teens with classmates with MI more likely to be diagnosed

Teens who have one or more classmates with a mental disorder are more likely to be diagnosed with one themselves later in life than those who do not have any classmates with a mental disorder, according to a report in JAMA Psychiatry. The risk of a mental illness diagnosis was greatest in the first year following the diagnosis of a classmate, according to an APA (American Psychiatric Association) Psychiatric News Alert.

"The Most Important Thing is That You Are Not Left Alone"-A Qualitative Study of Challenges and Needs of Adolescents and Young Adults with Cancer.

Purpose: We aimed to gain in-depth insights into the challenges and needs of adolescents and young adults with cancer (AYA) throughout their disease to detect increased needs for support. Methods: We conducted face-to-face interviews with 15 patients 18-39 years old at the time of diagnosis of malignancies who had completed treatment at the time of the interview. The interviews were analyzed using content analysis. Results: The unexpected diagnosis of a serious illness shocked the participants, especially since many were in a life phase of transition at that time and had little knowledge about cancer and its therapy. They reported feelings of helplessness, unfairness, and insecurity triggered by side effects, divergent information from health care professionals (HCPs), or lack of information. After the end of the therapy, many AYAs were challenged by prolonged changes in their lives since they gained strength from the hope of returning to "the life before." The primary needs were social and psychological support, connecting with peers, orientation, and guidance. Conclusion: AYAs face special challenges requiring support and guidance from HCPs that should extend beyond the end of the therapy. There is a need for exchange opportunities with other AYAs. Trial Registration Number: DRKS00030277 (German Clinical Trials Register); September 27, 2022.

Machine Learning for the Diagnosis and Prognosis of Chronic Illnesses

An essential part of healthcare is disease prediction, which seeks to identify people who are at risk of getting certain diseases. Because of their superior capacity to sift through massive datasets in search of intricate patterns, machine learning algorithms have recently become useful instruments in the fight against illness prediction. The goal of this project is to make it easier for people to diagnose their own health problems using just their symptoms and precise vital signs. Due to excessive medical expenditures, many people put off taking care of their health, which can result in worsening symptoms or even death. Medical expenses can be overwhelming for people without health insurance. Using machine learning methods like ExtRa Trees, the suggested system provides a general illness forecast based on patients' symptoms. The algorithm provides a possible diagnosis based on the user's age, gender, and symptoms, suggesting that the user may be experiencing a certain illness. The system also suggests healthy eating and exercise routines to help lessen the impact of the condition, depending on how bad it is. Lastly, this article has shown a comparison examination of the suggested system using several algorithms including logistic regression, decision tree, and Naïve Bayes. The efficiency and accuracy of illness prediction are both enhanced by the suggested model.

The relationship between uncertainty in illness and psychological adjustment to chronic illness.

Using Mishel's Uncertainty in illness theory, this integrative review sought to identify how uncertainty in illness and psychological adjustment to illness are associated with chronic illness to guide development of interventions to support psychological adjustment. A search was conducted via EBSCOHost to answer the question "What is the relationship between uncertainty in illness and psychological adjustment to chronic illness?" Two key factors were identified as having an impact on psychological adjustment to chronic illness in the presence of uncertainty in illness including uncertainty appraisal and the type of coping strategies used in response to the appraisal. Individuals with a chronic illness diagnosis are more likely to experience increased levels of uncertainty in illness and decreased psychological adjustment to illness. A better understanding of these two concepts is important to augment and or guide the development of interventions to support psychological adjustment.

Hyperhomocysteinemia is associated with the risk of venous thromboembolism in patients with mental illness: a case-control study.

The risk of venous thromboembolism in patients with mental illness has been insufficiently addressed. This study aimed to assess the correlation between hyperhomocysteinemia and venous thromboembolism prevalence among this population. Patients with a diagnosis of mental illness and concurrent venous thromboembolism, admitted to Sir Run Run Shaw Hospital at Zhejiang University School of Medicine between January 2014 and December 2021, were included in the venous thromboembolism group. The control group, approximately twice the size, comprised individuals with mental illness but without venous thromboembolism. Basic clinical data were gathered for both cohorts. In psychiatric patients, elevated D-dimer levels(OR=5.60,95% CI 3.28-10.00), hyperhomocysteinemia (OR=2.37,95% CI 1.10-5.14), and hyperprolactinemia(OR= 2.68,95% CI 1.12-6.42)were significant risk factors for venous thromboembolism. According to further subgroup analyses, hyperhomocysteinemia is a significant risk factor associated with pulmonary embolism, with an OR of 5.08 (95% CI 1.20-21.48). An interaction effect between gender and homocysteine level was found, with a p-interaction of 0.022.A subsequent analysis confirmed the association between hyperhomocysteinemia and venous thromboembolism in female psychiatric patients, with an OR of 3.34 (95% CI 1.68-6.65), indicating that hyperhomocysteinemia is a significant risk factor for venous thromboembolism in women. Patients with psychiatric disorders were found to have an elevated risk of venous thromboembolism, which was associated with increased levels of D-dimer, hyperprolactinemia, and hyperhomocysteinemia. A strong correlation between hyperhomocysteinemia and pulmonary embolism was identified in patients with mental illnesses. Furthermore, the study revealed that female psychiatric patients with hyperhomocysteinemia constituted a high-risk group for venous thromboembolism. This finding holds significant clinical implications, suggesting that early preventative measures could be implemented for this high-risk population to reduce the incidence of thromboembolic events during hospitalization for psychiatric patients.

Palliative care at the end of life and the role of the psychologist

Mallampati, Orotracheal intubation, Modifications.Palliative care (PC) is defined as care for patients diagnosed with progressive, incurable and/or chronic-degenerative diseases, and can cover any life-threatening illness that culminates in finitude. Therefore, PC aims to ease the pain and relieve the suffering of invasive treatments by providing dignity, comfort and welcome to improve the quality of life of patients diagnosed with incurable diseases; this care can also include the individual's family members and caregivers, providing them with welcome, assistance and support to deal with the issues arising from the progression of the disease. Thus, the aim of this study is to describe the participation of the multi-professional team in PC, the patient and their family, i.e. all those involved in this process of becoming ill until the end of life, and in particular to investigate how psychology works in caring for the patient, the family/caregiver and the healthcare team. This study used the literature review method. This bibliographic review selected 27 scientific articles, 02 books, 01 Resolution of the Federal Council of Psychology, 01 Manual of Palliative Care of the National Academy of Palliative Care, 01 Manual of Palliative Care of the Sírio Libanês Hospital, 01 Primer of the Brazilian Society of Geriatrics and Gerontology, 01 thesis. It will record how the patient copes during the journey from the diagnosis of illness to the end of life, as well as the participation and experience of the family, the multi-professional team and in particular the participation of Psychology within this scenario, as it is the scientific field that covers the holistic sphere of the individual, and its premise is to deal with issues involving human relationships, bringing the necessary comfort in the face of this new context of an established disease that will cause the end of existence. In this way, the psychologist is the professional trained to study mental processes, such as feelings, thoughts, behaviors, understanding their subjectivity and, in this context, helping the individual to understand their self in the face of the serious and irreversible clinical state that precedes the proximity of death, resolving outstanding issues with themselves and their families, providing support so that everyone can be at peace at the moment of farewell. Finally, it will discuss the importance of humanized care in PC through the inclusion of a specific subject in the undergraduate courses of health professionals, paying special attention to the field of psychological knowledge since it is the professional who is the protagonist in this scenario.

Enhancing multi-class lung disease classification in chest x-ray images: A hybrid manta-ray foraging volcano eruption algorithm boosted multilayer perceptron neural network approach

ABSTRACT One of the most used diagnostic imaging techniques for identifying a variety of lung and bone-related conditions is the chest X-ray. Recent developments in deep learning have demonstrated several successful cases of illness diagnosis from chest X-rays. However, issues of stability and class imbalance still need to be resolved. Hence in this manuscript, multi-class lung disease classification in chest x-ray images using a hybrid manta-ray foraging volcano eruption algorithm boosted multilayer perceptron neural network approach is proposed (MPNN-Hyb-MRF-VEA). Initially, the input chest X-ray images are taken from the Covid-Chest X-ray dataset. Anisotropic diffusion Kuwahara filtering (ADKF) is used to enhance the quality of these images and lower noise. To capture significant discriminative features, the Term frequency-inverse document frequency (TF-IDF) based feature extraction method is utilized in this case. The Multilayer Perceptron Neural Network (MPNN) serves as the classification model for multi-class lung disorders classification as COVID-19, pneumonia, tuberculosis (TB), and normal. A Hybrid Manta-Ray Foraging and Volcano Eruption Algorithm (Hyb-MRF-VEA) is introduced to further optimize and fine-tune the MPNN’s parameters. The Python platform is used to accurately evaluate the proposed methodology. The performance of the proposed method provides 23.21%, 12.09%, and 5.66% higher accuracy compared with existing methods like NFM, SVM, and CNN respectively.

Burden and outcome of respiratory morbidities among children and adolescents with sickle cell disease-A retrospective review of emergency presentations in some Nigerian tertiary institutions.

Despite the huge burden of sickle cell disease (SCD) among Nigerian children, the burden and outcome of respiratory illnesses remain undocumented. Thus, we aimed to describe the spectrum and outcome of respiratory illnesses among SCD childrenand adolescentadmissions in ten Nigerian tertiary hospitals. A retrospective review of the SCD admission records of children and adolescents with a confirmed diagnosis of respiratory illnesses from 2012 to 2021 in ten tertiary health facilities across five geopolitical zones in Nigeria was conducted. The data, collectedbetween March and June 2023, included the age, sex, diagnosis, complications, duration and outcome of hospitalization. Of the 72,333 paediatric admissions, 7,256 (10.0%) had SCD; the proportion of SCD from the total admission ranged from 2.1 to 16.3% in the facilities. Of the 7,256 children and adolescents with SCD, 1,213 (16.7%) had respiratory morbidities. Lower respiratory disease was the most common (70.0%) respiratory entity and the majority were pneumonia (40.1.0%), followed by acute chest syndrome (26.7%). Seventeen (1.4%) patients died; all had lower respiratory diseases [(acute chest syndrome ACS (11, 64.7%), pneumonia; 5, 29.4%, and asthma (1, 5.9%). Based on the proportion of deaths among overall SCD, the 17 death cases contributed 9.4% (95% CI 5.9 to 14.5). Factors associated with deaths included duration of hospitalization less than 72 hours and lower respiratory tract diseases. Sickle cell disease is a major contributor to hospitalization among Nigerian children and adolescents, with high respiratory morbidity and mortality. Pneumonia and acute chest syndrome were associated with mortality, andthe highest risk of death within the first 72 hours.

Speech under Stress: Analysis and Recognition using KNN

This work examines how speech characteristics might be analyzed to tailor automated conversation recognition to illness diagnosis. Voice analysis, which is now performed by a skilled physician using techniques based on auditory analysis, enables the diagnosis of illnesses that impact the vocal apparatus. The suggested work offers a cutting-edge way to monitor a patient's pathology. It is stress-free to use, quick, and affordable for the physician, and safe for the patient. This technique practices a parametric methodology (jitter, shimmer, harmonic to sound, etc.) to assess the sick vocal sound. Additionally, the technique for this work rest on on the feature extraction of Mel Frequency Cepstral Coefficient (MFCC) and the feature matching of Dynamic Time Warping (DTW). This article provides a step-by-step breakdown of the speech analysis procedure used on anxious individuals. The degree of classification accuracy attained for each of the retrieved characteristics confirms that our method is effective in differentiating between stressed individuals. Thus, the speech analysis feature on the PRAAT platform has produced positive results and shown to be an effective tool for differentiating between those who are under stress and those who are not. Using MATLAB 2018b, the work displays several 2D and 3D displays of teaching and anticipated matters. Keywords: Dynamic Time Warping, Mel Frequency Cepstral Coefficient, K nearest neighbor, Virtual Reality

Explainable AI-driven IoMT fusion: Unravelling techniques, opportunities, and challenges with Explainable AI in healthcare

Background and Objective:Artificial Intelligence (AI) has shown significant advancements across several industries, including healthcare, using better fusion methodologies, improved data accessibility, and heightened processing capabilities. Although deep learning algorithms excel in challenging scenarios, their widespread adoption presents a challenge due to their need for more transparency. The advent of Explainable AI (XAI) has addressed this limitation by offering AI-driven systems with attributes such as transparency, interpretability, and reliability. The need for transparency in critical sectors like healthcare has prompted an increased academic focus on investigating and understanding these frameworks. The present paper thoroughly examines the latest research and advancements in Explainable Artificial Intelligence (XAI), specifically focusing on its integration into the Internet of Medical Things (IoMT) in healthcare settings. The last section tackles outstanding issues concerning XAI and IoMT and outlines prospective directions for further research. Methods:A thorough investigation was carried out across many scholarly databases and online digital libraries, such as IEEE Xplore, ACM Digital Library, Wiley Interscience, Taylor and Francis, ScienceDirect, Scopus, Springer, Google Scholar, Citeseer Library, Semantic Scholar, and other relevant sources. An analysis was conducted on articles published from March 2004 to February 2024, specifically focusing on AI models that explain different healthcare issues. The search query included “Explainable AI” in conjunction with “Open Black Box, Healthcare, Transparent Models, Interpretable Deep Learning, Machine Learning, Medical Information System, Accountability, Smart Healthcare, and Responsible AI”, indicating the field’s dynamic nature. The authors also examined various techniques and datasets to elucidate healthcare difficulties, including incorporating the Internet of Medical Things (IoMT). Results:The evaluation included more than 105 published models that used clinical data input to diagnose diverse disorders, focusing on incorporating Internet of Medical Things (IoMT) components. In addition to illness diagnosis, these models also elucidate the decisions made. The models were classified according to the input data, used methodologies, and integration of the Internet of Medical Things (IoMT). The categorization included machine learning and deep learning methodologies, particularly on explainability models connected to the Internet of Medical Things (IoMT). These models were classified as model-agnostic, model-specific, global, local, ante-hoc, and post-hoc. Conclusion:This extensive study examines machine learning, and deep learning models developed based on clinical data and associated with the Internet of Medical Things (IoMT) for illness detection. These models improve efficiency and accuracy and provide essential assistance to medical personnel. Explaining each choice made by individuals contributes to the acceleration of sickness detection, resulting in decreased medical expenses for patients and healthcare systems. The integration of Explainable Artificial Intelligence (XAI) and the Internet of Medical Things (IoMT) highlights a fundamental change in healthcare towards more transparent and linked systems, leading to significant breakthroughs in medical decision-making.