Mesopotamian Journal of Artificial Intelligence in Healthcare

This article investigates the capabilities and limitations of ChatGPT, a naturallanguage processing (NLP) tool, and large language models (LLMs), developed fromadvanced artificial intelligence (AI). Designed to help computers understand andproduce text understandable by humans, ChatGPT is particularly aimed at generalscientific writing and healthcare research applications. Our methodology involvedsearching the Scopus database for ’type 2 diabetes’ and ’T2 diabetes’ articles fromreputable journals. After eliminating duplicates, we used ChatGPT to formulateconclusions for each selected article by inputting their structured abstracts, excludingthe original conclusions. Additionally, we tested ChatGPT’s response to simplemisuse scenarios. Our findings show that ChatGPT can accurately grasp contextand concisely summarize primary research findings. Additionally, it helps individualswho are not as experienced in mathematical analysis by providing coding guidelinesfor mathematical analyses in a variety of computer languages and by demystifyingdifficult model results. In conclusion, even if ChatGPT and other AI technologiesare revolutionizing scientific publishing and healthcare, their use should be strictlycontrolled by authoritative laws.

Machine learning is considered one of the most significant techniques that play a vital role in diagnosing the Coronavirus. It is a set of advanced algorithms capable of analyzingmedical data and identifying patterns and behaviorsof diseases. It is used to interpret medical images, giving details of each image with high accuracy and efficiency, such as chest X-ray images. These algorithms are trained on a large set of images to recognizepatterns that indicate the presence of infection with the Coronavirus (COVID-19). This article will provide a brief overview of the importance of machine learning in diagnosing COVID-19 by processing and analyzingmedical image data and helping physiciansand healthcare workers provide distinguished and influential care for patients infected with this virus

Background: The role of artificial intelligence (AI) is increasingly recognized to enhance digital health literacy. There is of particular importance with widespread availability and popularity of AI chatbots such as ChatGPT and its possible impact on health literacy.The involves the need to understand AI models’ performance across different languages, dialects, and cultural contexts. This study aimed to evaluate ChatGPT performance in response to prompting in two different Arabic dialects, namely Tunisianand Jordanian.Methods:This descriptive study followed the METRICS checklist for the design and reporting of AI based studies in healthcare. Ten general health queries were translated into Tunisian and Jordanian dialects of Arabic by bilingual native speakers. The performance of two AI models, ChatGPT-3.5 and ChatGPT-4 in response to Tunisian, Jordanian, and English were evaluated using the CLEAR tool tailored for assessment of health information generated by AI models.Results:ChatGPT-3.5 performance was categorized as average in Tunisian Arabic, with an overall CLEAR score of 2.83, compared to above average score of 3.40 in Jordanian Arabic. ChatGPT-4 showed a similar pattern with marginally better outcomes with a CLEAR score of 3.20 in Tunisian rated as average and above average performance in Jordanian with a CLEAR score of 3.53. The CLEAR components consistently showed superior performance in the Jordanian dialect for both models despite the lack of statistical significance. Using English content as a reference, the responses to both Tunisian and Jordanian dialects were significantly inferior (P<.001).Conclusion:The findings highlight a critical dialectical performance gap in ChatGPT, underlining the need to enhance linguistic and cultural diversity in AI models’ development, particularly for health-related content. Collaborative efforts among AI developers, linguists, and healthcare professionals are needed to improve the performance of AI models across different languages, dialects, and cultural contexts. Future studies are recommended to broaden the scope across an extensive range of languages and dialects, which would help in achieving equitable access to health information across various communities

Background:With artificial intelligence (AI) integrated increasingly to enhance personalized diagnosis and data-driven treatment recommendations, this analysis examines the legal sufficiency of Ghana’s Health Institutions and Facilities Act 2011 (Act 829) to address medical negligence risks from reliance on AI systems in clinical settings. The CREAC framework structures evaluating gaps where existing health regulations may lack clarity for emerging issues of accountability. Explanation contextualizes the probabilisticnature of AI inferences and how general principles of medical negligence could have ambiguous application currently if erroneous AI contributions result in patient harm. Application to a hypothetical scenario assesses if adequate protections for appropriate integration exist across developers, systems, healthcare facilities, and practitioners under applicable interpretations of existing laws. Finding liability rules insufficient absent targeted AI governance, conclusions recommend amending Act 829 in key areas to codify expectations for responsible innovation and prevent ambiguity in liability. Objective:This work carries scientific novelty as one of the first structured jurisdictional analyses internationally of healthcare AI accountability gaps through a legal lens. Practical significance lies in setting the stage for strengthening protections in Ghana through proposed statutory reforms that reduce uncertainty around this crucial area for quality care. Result:The method and recommendations offer a model for modernizing medical negligence law and AI policy amidst ongoing digitization in healthcare worldwide.

This analysis examines Ghana’s Health Professions Regulatory Bodies Act, 2013 (Act 857) to assess its fitness to govern the ascent of artificial intelligence (AI) in reshaping healthcare delivery. As advanced algorithms supplement or replace human judgments, dated laws centered on individual practitioner liability struggle to contemplate emerging negligence complexities. Act 857 lacks bespoke provisions for governing this new era beyond outdated assumptions of human-centric care models. With AI projected totransform medicine, proactive reforms appear vital to enable innovation gains while upholding accountability. Through an IRAC legal analysis lens supplemented by case law spanning from the United States to Ghana, this paper demonstrates how judiciaries globally are elucidating risks from legal uncertainty given increasingly autonomous health technologies. Findingsreveal governance gaps impeding equitable access to remedy where algorithmic activities contribute to patient harm. Calls for stringent training, validation and monitoring prerequisites before deploying higher-risk AI systems signal a reframed standard ofcare is warranted. Detailed recommendations to modernize Act 857 and adjacent regulation are provided, covering practitioner codes, product safety, ongoing evaluation duties, and crucially, updated liability rules on apportioning fault between disparate enterprises enabling flawed AI. Beyond protecting patients and practitioners, enhanced governance can boost investor confidence in Ghana’s AI healthcare ecosystem. Ultimately astute reforms today can reinforce innovation gains tomorrow across a more ethical, accountable industry.

Growing global awareness that attention to health care is the basis for maintaining citizens\' quality of life. Health institutions seek to increase interest in electronic care services and enhance patient results by integrating artificial intelligence techniques. Artificial intelligence tools are indispensable to diagnosis, treatment, and patient care. Integrating artificial intelligence techniques into the development of the electronic healthcare environment works to enhance public health and disease prevention and provide free services to all citizens. Designing electronic platforms raises health awareness in society, provides health programs and initiatives, and reaches homes, gardens, schools, and universities through applications based on artificial intelligence. The primary purpose of this article is to challenge the extent to which artificial intelligence is related to medicine and its contribution to the positive and negative effects of revolutionizing healthcare services.

The healthcare sector is witnessing significant development in many aspects thanks to the effects of artificial intelligence or software, which has turned out to be the centre of attraction all over the world. This is evidence of a simple development in acquiring deep knowledge of the methods and areas in which they are used. Face detection, voice recognition, autonomous use, the defence industry, the security industry, and other fields may be displayed as examples that help complete tasks. This article surveys the impact of deep learning methods and practices in the medical device industry, and we also examine the distribution of multi-year data. It is divided into six categories: healthcare, big data and wearable technologies, biomedical code, image processing, diagnostics, and the Internet of Medical Things. As a result, the medical device industry has grown in recent years through deep learning techniques and the use of most research related to diagnosis and image processing

Lung disease prediction is a critical issue in today\'s world. However, in the past two years, the corona virus disease 2019 (COVID-19) has a broad range and, in a limited percentage of people, a notable effect on the lungs. In the past, the fuzzy logic method has been used to classify lung disease prediction, but it has faced challenges such as difficulties in identifying segmented regions and output inaccuracies. To solve the issue Convolutional neural networks are used in machine learning to predict lung condition. The preprocessing based weighted average filter gives greater weight to the central value, making its contribution more significant than that of other values and can regulate the degree of image blurring. The process of segmenting based on region split and merge techniques involves separating one or more areas or entities in an image according to a size of m by n at one level of a threshold value. This segmented in multiple sub-regions of the same size, indicating a fundamental representational structure, from that Image classification using convolutional neural networks (CNNs) is a type of neural network specifically designed to extract distinct characteristics from segmented data. They are often used in tasks such as lung disease prediction and recognition due to their ability to identify intricate details in clustered data. The approach was evaluated using the MATLAB tool, a novel CNN with multiple image processing technique in our experiment to efficiently classify lung illnesses under typical circumstances, the average accuracy increased up to 97%.The results of this study show significant improvement in the prognosis of lung prediction in medical filed

Breast cancer is a common type of cancer in women, denoted by the uncontrolled growth of cells in breast tissue. Thus, manually detecting breast cancer is time-consuming and necessitates automated systems. Existing breast cancer screening methods often have limited efficacy and may delay detection and complicate the individual treatment planning process. However, early detection of breast cancer can be costly and impact the accuracy of diagnosis. To address this issue, we introduce a Darknet-53 Convolutional Neural Network (darknet-53CNN) approach for classifying breast cancer images and improving precision. Furthermore, we utilise the Contrast-Limited Adaptive Histogram Equalization (CLAHE) technique to pre-process breast cancer images to enhance image quality.Furthermore, we evaluate the intensity level of pixel images by feature extraction using the Haralick Grey-Level Co-Occurrence Matrix(HGLCM) technique. Finally, the DarkNet-53 CNN method improves the accuracy of detecting breast cancer and classifying images as benign or malignant. The proposed algorithm evaluates the specificity, sensitivity, accuracy and precision of predictive test results based on the classification of breast cancer images. Moreover, the accuracy of the proposed method has increased to 95.6% compared to the methods obtained from previous approaches.Mesopotamian Journal of Artificial Intelligence in HealthcareVol.2024, pp. 59–68DOI: https://doi.org/10.58496/MJAIH/2024/009;ISSN: 3005-365Xhttps://mesopotamian.press/journals/index.php/MJAIH

The introduction and rapid evolution of generative artificial intelligence (genAI) models necessitates a refined understanding for the concept of “intelligence”. The genAItools are known for its capability to produce complex, creative, and contextually relevant output. Nevertheless, the deployment of genAI modelsin healthcare should beaccompanied appropriate and rigorous performance evaluation tools. In this rapid communication, weemphasizesthe urgentneed to developa “Generative AIQ Test”as a novel tailored tool forcomprehensive benchmarking ofgenAI models against multiple human-like intelligenceattributes. A preliminary framework is proposed in this communication. This framework incorporatesmiscellaneousperformance metricsincludingaccuracy, diversity, novelty, and consistency. These metrics were consideredcritical in the evaluation of genAI models thatmight be utilizedto generatediagnostic recommendations, treatment plans, and patient interactionsuggestions. This communication also highlights the importance of orchestrated collaboration to constructrobustand well-annotated benchmarkingdatasets tocapture the complexity of diverse medical scenarios and patient demographics. Thiscommunicationsuggests an approach aimingto ensure that genAI models are effective, equitable,and transparent. To maximize the potential of genAI modelsin healthcare, it is important to establish rigorous, dynamic standards for its benchmarking. Consequently, this approach can help to improve clinical decision-making withenhancement inpatient care, which will enhancethe reliability of genAI applicationsin healthcare

Artificial intelligence is increasingly present in many applications that help humans accomplish many tasks. It can support improved results, increased productivity, and high efficiency in providing services. Systems developers seek to integrate improved artificial intelligence models into the development of healthcare services and make a significant qualitative shift in the medical field. One recently invented tool is ChatGPT, which deserves extensive advertising space. This tool provides sample answers that help both healthcare workers and patients answer all their questions. It is an important tool in medical education, increasing knowledge in making decisions that can improve the performance of medical professionals. The impact of this tool must be addressed because it is in a developed stage, and there are a good number of articles widely circulating that speak to and explain its importance at present. In this article, the importance and role of the ChatGPT tool in developing healthcare services and the tangible and informational information that is provided will be described, as well as the possibility of predicting diseases and diagnosing and treating patients

The utilisation of deep learning techniques has witnessed a surge in popularity within the realm of medical image analysis, particularly in the context of identifying COVID-19. Following the occurrence of the COVID-19 pandemic, extensive investigations have been conducted to identify the existence of Sars-Cov-2 through the utilisation of several deep learning algorithms. The objective of this study is to conduct a comprehensive review of deep learning techniques utilised for the detection of COVID-19. \"Can deep learning methodologies serve as a viable substitute for radiologists in the diagnostic process of COVID-19?\" is the research inquiry. In order to compile research articles for the purpose of conducting a systematic review, two scientific databases were employed as primary sources. Databases such as PubMed and IEEE Xplore have been utilised for this purpose till January 2022. The published studies were examined in accordance with the PRISMA guidelines. The study established predetermined criteria for exclusion and inclusion, and subsequently identified relevant works based on these criteria. The findings indicated that a total of 543 out of the 634 articles that were initially retrieved were excluded due to their lack of conformity with the predetermined criteria. Conversely, 87 articles met the inclusion criteria and were retained for further analysis. The research articlespresented in this compilation are categorised into three distinct groups: the types of visual representations utilised, the methods employed for applying deep learning techniques, and the programming languages that are most frequently utilised. The exclusive reliance on deep learning algorithms is insufficient for substituting the visual diagnostic performed by physicians and radiologists in the detection of COVID-19. Due to the lack of substantiation by the medical establishment. CT and x-ray imaging modalities are commonly utilised in various fields. However, alternative imaging techniques, such as Optical Coherence Tomography (OCT) and Ultrasonic imaging, are either overlooked or not given due consideration. The predominant focus of study is on retrospective (theoretical) rather than prospective (pragmatic) investigations. Consequently, there exists a significant need for researchers to enhance the practicality of their investigations.1.INTRODUCTIONThe SARS-CoV-2 outbreak in late 2019 affected 2.81 million people in a short period of time [1]. The first Covid-19 case was reported in China\'s Wuhan province. The virus\'s transmission then shifted from epidemic to endemic. Covid-19 is more likely to be an endemic illness and will not go extinct anytime soon due to a number of influencing variables [1]. Despite the absence of sufficient healthcare facilities, all countries throughout the world are fighting this pandemic. Fever, cough, dyspnea, sweating, and myalgia are frequent symptoms with SARS-Cov-2 infection. However, symptoms are not limited to covid. Real-time reverse transcription-polymerase chain reaction (RT-PCR) is the gold standard and most often used method for detecting coronavirus. This approach, however, has significant disadvantages, including a scarcity of test kits, a 24-hour turnaround time, and a 30% false-negative rate. This means that it incorrectly identifies positive Covid-19 cases as negative. As a result of the insufficient number of healthcare facilities, practitioners in the healthcare sector confront numerous challenges, and many places have been closed down to prevent the spread of the virus [2]. Another way for diagnosing Covid-19 is to use chest radiography images such as X-rays and CT scans, which show particular characteristics such as ground-glass opacities and pleural thickening [3]. This medical imaging must be visually analysed by specialists, which is a tiresome and time-consuming task. As a result, researchers have created numerous algorithms to analyse radiologic pictures of the chest in order to distinguish between healthy and sick instances. Skin cancer detection [4][5], tumour classification [5, and lung segmentation [6] are among the challenges that must be addressed by artificial intelligence, machine learning, and, Mesopotamian Journal of Artificial Intelligence in HealthcareVol.2024, pp. 84–109DOI: https://doi.org/10.58496/MJAIH/2024/012;ISSN: 3005-365Xhttps://mesopotamian.press/journals/index.php/MJAIH

This paper sets itself in a context of examining how IoT technology is disrupting and revolutionizing the monitoring and control of relative humidity in different spheres of smart cities with special reference to the enhancement of well-being in the information society. The authors discuss the current emergent research topicsin IoT-based humidity sensors, wireless communication systems, and big data analytics for monitoring and controlling the humidity in real time. The paper also presents an outlook into various fields such as agriculture, healthcare, intelligent houses, industries, as well as the environment, and presents an indication of how managing humidity accurately enhances crop productivity, disease prevention, internal air quality, and the general wellbeing of the public. This also covers on the use of artificial intelligence and machine learning for IoT on analysis in terms of prediction and control. Admittedly, there are limitations associated with IoT including data security, compatibility, and power supply issues of the devices, etc. The future development of IoT as described in the paper includes improved sensors, edge computing, and the use of block-chain technology. Therefore, the authors find that IoT techniques for humidity measurement and management are effectively used in the development of healthier, comfortable, and sustainable environments in smart cities to enhance the quality of life

In recent years, chronic kidney disease (CKD) has become a crucial public health concern and issue, resulting in serious mortality rates. Therefore, we introduce a powerful system for kidney disease diagnosis that develops up-to-date approaches and applications. Our powerful system is meant for early detection, individual patient monitoring, and optimal CKD patient outcomes. The study introduces a CKD detection ecosystem primarily intended for optimized early detection techniques to be able to manage significant weaknesses in current systems and also for proactive treatment. Thisecosystem also provides medical practitioners with up-to-date diagnostic technology, incorporating advanced Convolutional Neural Networks (CNN) that guarantee accuracy and efficiency of the diagnosis phase. Furthermore, it facilitates patient empowerment via an individualized mobile application meant for passive surveillance and active engagement in the treatment cycle. Our study introduces this ecosystem with two components, including (1) a deep sequential model for CKD diagnosis and (2) a CNN-based diagnosis for kidney conditions. We have developed the system in order to manage the shortcomings of CKD treatment, deal with the challenges faced by CKD patients by empowering them, and support the global attempts intended to reduce the CKD mortality rates. Inaddition, we anticipate it to significantly transform the kidney disease treatment field all over the world, demonstrating an effective performance with an accuracy of 98.75%

The accurate prediction and classification of medical data, such as Hepatitis C, play a significant role in upgrading symptomatic accuracy and treatment planning. This study utilized progressed machine learning algorithms, including RF, SVR, and Gradient Boosting, to analyze features and predict outcomes viably. By coordination robust preprocessing strategies and feature engineering, the models tended to missing values and categorical transformations, enabling exact predictions from complex datasets. The models accomplished high accuracy and unwavering quality in execution, as prove by comparative measurements and validation results. These discoveries highlight the potential of machine learning in increasing clinical decision-making and emphasize the requirefor advance research to optimize these methods for broader healthcare applications

As healthcare becomes increasingly dependent on the Internet of Medical Things (IoMT) infrastructure, it is essential to establish a secure system that guarantees the confidentiality and privacy of patient data. This system must also facilitate the secure sharing of healthcare information with other parties within the healthcare ecosystem. However, this increased connectivity also introduces cybersecurity attacks and vulnerabilities. This comprehensive review explores the state-of-the-art in the IoMT, security requirements in the IoMT, cryptographic techniques in the IoMT, application of cryptographic techniques in securing the IoMT, security attacks on cryptographic techniques, mitigation strategies, and future research directions. The study adopts a comprehensive review approach, synthesizing findings from peer-reviewed journals, conference proceedings, book chapters, Books, and websitespublished between 2020 and 2024 to assess their relevance to cryptographic applications in IoMT systems. Despite advancements, cryptographic algorithms in IoMT remain susceptible to security attacks, such as man-in-the-middle attacks, replay attacks, ransomware attacks, cryptanalysis attacks, key management attacks, chosen plaintext/chosen ciphertext attacks, and side-channel attacks. While techniques like homomorphic encryption enhance security, their high computational and power demands pose challenges for resource-constrained IoMT devices. The rise of quantum computing threatens the efficacy of current cryptographic protocols, highlighting the need for research into quantum-resistant cryptography. The review identifies critical gaps in existing cryptographic research and emphasizes future directions, including lightweight cryptography, quantum-resistant methods, and artificial intelligence-driven security mechanisms. These innovations are vital for meeting the growing security requirements of IoMT systems and protecting against increasingly sophisticated threats

This study compares the performance of ARIMA, LSTM, and hybrid models in predicting COVID-19 cases and analyzing forecast errors. Utilizing real-world data, the models were assessed for accuracy and trend prediction over numerous months. Heatmaps of prediction errors revealed that the hybrid model reliably outperformed ARIMA and LSTM by accomplishing lower error extents. The findings highlight the preferences of combining statistical and deep learning approaches for time series predicting. The results contribute to progressing predicting accuracy, which is basic for pandemic response arranging and public health administration

Skin diseases are the most common than other diseases. Skin diseases may be caused by fungal infection, allergy, bacteria, viruses, etc. Fungal disease affects more than billions people worldwide. The accurately of diagnosing skin fungal infections can be challenging in their early stages or present with symptoms, which mimic other dermatological disorders. The identification and categorization of skin fungal infections could be improved by recent developments in deep learning and artificial intelligence (AI). It offers a more effective and dependable substitute for conventional diagnostic techniques. Here we tried to focus on various methods for identifying fungalskin that rely on deep learning (such as transformers, convolutional neural networks, and hybrid models), the difficulties related to data diversity and availability, going over the shortcomings of current datasets, how data augmentation and synthetic datacreation which might help close these gaps. We also investigate how improving interpretability and usability can help clinical uptake of AI-based diagnostic systems. Finally, the study concludes with suggestions for further research, highlighting the revolutionary potential of deep learning in dermatology and stressing the necessity of sophisticated model architectures, a wide range of high-quality datasets, and thorough clinical validation

Background:The potential of generative artificial intelligence (genAI) tools, such as ChatGPT, is being increasingly exploredin healthcare settings. However, the same toolsalso introduce significant cybersecurity risks that could compromise patient safety, data integrity, and institutional trust.This study aimed to examine real-world security breaches involving genAI and extrapolate their potential implications forhealthcare settings.Methods: Using a systematic Google News search and a consensus-based approach among the authors, five high-profile genAI breaches were identified and analyzed. These cases included: (1) Data exposure in ChatGPT (OpenAI) due to an open-source library bug (March 2023); (2) Unauthorized data disclosure via Samsung’s (Samsung Group) use of ChatGPT (2023); (3) Logical vulnerabilities in Chevrolet(General Motors)AI-powered chatbot resulting in pricing errors (December 2023); (4) Prompt injection vulnerability in Vanna AI (Vanna AI, Inc.) which enabledremote code execution (2024); and (5) the deepfake technology used in a scam targeting the engineering firm Arup(Arup Group Limited), leading to fraudulent transactions (February 2024). Hypothetical healthcare scenarios were constructed based on thefivecases, mapping their mechanisms to vulnerabilities in electronic health records (EHRs), clinical decision support systems (CDSS), and patient engagement platforms. Each case was analyzed using the Confidentiality, Integrity, and Availability (CIA) triad of information security to systematically identify vulnerabilities and propose actionable safeguards.Results:The analyzed cases of AI security breaches revealed significant risks to healthcare systems. Confidentiality violations included the potential exposure of sensitive patient records and billing information, extrapolated from incidents such as the ChatGPT data exposure and Samsung’s cases. These identified security breaches raisedconcerns about privacy violations, identity theft, and non-compliance with regulations such as Health Insurance Portability and Accountability Act (HIPAA). Integrity vulnerabilities were highlighted in Vanna AI's prompt injection flawincident, withrisks of altering patient records, compromising diagnostic algorithms, and misleading CDSSwith erroneous recommendations. Similarly, logic errors identified in the Chevrolet caseexposed potential risks of inaccurate billing, double-booked appointments, and flawed treatment plans within healthcare contexts.Availability disruptions, observed through system outages and operational suspensions following breaches like the ChatGPT and deepfake cases, can delay access to EHRsystems or AI-driven CDSS. Such interruptions would directly impact patient care and create inefficiencies in administrative workflows.Conclusions:Generative AI presents a double-edged sword in healthcare, with transformative potential accompanied by substantial risks. Extrapolation of security breach cases in this study highlightedthe urgent need for robust safeguardsif genAI is implemented in healthcare settings. To address these vulnerabilities, healthcare institutions must implement strong security protocols, enforce strict data governance, and create AI-specific incident response plans. The balance betweengenAI-enabled innovation andprotection of patient safetyanddata integrity trust requires proactive safety measures.Mesopotamian Journal of Artificial Intelligence in HealthcareVol.2024, pp. 184–203DOI: https://doi.org/10.58496/MJAIH/2024/019;ISSN: 3005-365Xhttps://mesopotamian.press/journals/index.php/MJAIH