Mesopotamian Journal of Artificial Intelligence in Healthcare最新文献

{"title":"Benchmarking Generative AI: A Call for Establishing a Comprehensive Framework and a Generative AIQ Test","authors":"Malik Sallam, Roaa Khalil, Mohammed Sallam","doi":"10.58496/mjaih/2024/010","DOIUrl":"https://doi.org/10.58496/mjaih/2024/010","url":null,"abstract":"The introduction and rapid evolution of generative artificial intelligence (genAI) models necessitates a refined understanding for the concept of “intelligence”. The genAI tools are known for its capability to produce complex, creative, and contextually relevant output. Nevertheless, the deployment of genAI models in healthcare should be accompanied appropriate and rigorous performance evaluation tools. In this rapid communication, we emphasizes the urgent need to develop a “Generative AIQ Test” as a novel tailored tool for comprehensive benchmarking of genAI models against multiple human-like intelligence attributes. A preliminary framework is proposed in this communication. This framework incorporates miscellaneous performance metrics including accuracy, diversity, novelty, and consistency. These metrics were considered critical in the evaluation of genAI models that might be utilized to generate diagnostic recommendations, treatment plans, and patient interaction suggestions. This communication also highlights the importance of orchestrated collaboration to construct robust and well-annotated benchmarking datasets to capture the complexity of diverse medical scenarios and patient demographics. This communication suggests an approach aiming to ensure that genAI models are effective, equitable, and transparent. To maximize the potential of genAI models in healthcare, it is important to establish rigorous, dynamic standards for its benchmarking. Consequently, this approach can help to improve clinical decision-making with enhancement in patient care, which will enhance the reliability of genAI applications in healthcare.","PeriodicalId":424250,"journal":{"name":"Mesopotamian Journal of Artificial Intelligence in Healthcare","volume":"54 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141687076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating if Ghana's Health Institutions and Facilities Act 2011 (Act 829) Sufficiently Addresses Medical Negligence Risks from Integration of Artificial Intelligence Systems","authors":"George Benneh Mensah, P. Dutta","doi":"10.58496/mjaih/2024/006","DOIUrl":"https://doi.org/10.58496/mjaih/2024/006","url":null,"abstract":"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 probabilistic nature 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. \u0000This 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. The method and recommendations offer a model for modernizing medical negligence law and AI policy amidst ongoing digitization in healthcare worldwide.","PeriodicalId":424250,"journal":{"name":"Mesopotamian Journal of Artificial Intelligence in Healthcare","volume":"121 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140459248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital Physicians: Unleashing Artificial Intelligence in Transforming Healthcare and Exploring the Future of Modern Approaches","authors":"Ban Salman Shukur, Mohd Khanapi Abd Ghani, Burhanuddin Bin Mohd Aboobaider","doi":"10.58496/mjaih/2024/005","DOIUrl":"https://doi.org/10.58496/mjaih/2024/005","url":null,"abstract":"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.","PeriodicalId":424250,"journal":{"name":"Mesopotamian Journal of Artificial Intelligence in Healthcare","volume":"295 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140462429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Examining Ghana's Health Professions Regulatory Bodies Act, 2013 (Act 857) To Determine Its Adequacy in Governing the Use of Artificial Intelligence in Healthcare Delivery and Medical Negligence Issues","authors":"George Benneh Mensah","doi":"10.58496/mjaih/2024/004","DOIUrl":"https://doi.org/10.58496/mjaih/2024/004","url":null,"abstract":"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 to transform medicine, proactive reforms appear vital to enable innovation gains while upholding accountability. \u0000Through 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. Findings reveal 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 of care is warranted. \u0000Detailed 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.","PeriodicalId":424250,"journal":{"name":"Mesopotamian Journal of Artificial Intelligence in Healthcare","volume":"358 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140483079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning Helps in Quickly Diagnosis Cases of \"New Corona\"","authors":"Maad M. Mijwil, I. Adamopoulos, Pramila Pudasaini","doi":"10.58496/mjaih/2024/003","DOIUrl":"https://doi.org/10.58496/mjaih/2024/003","url":null,"abstract":"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 analysing medical data and identifying patterns and behaviours of 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 recognise patterns 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 analysing medical image data and helping physicians and healthcare workers provide distinguished and influential care for patients infected with this virus.","PeriodicalId":424250,"journal":{"name":"Mesopotamian Journal of Artificial Intelligence in Healthcare","volume":" 53","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139619366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measuring the Effectiveness of AI Tools in Clinical Research and Writing: A Case Study in Healthcare","authors":"S. Salisu, Osamah Mohammed Alyasiri, Hussain A. Younis, Thaeer Mueen Sahib, Ahmed Hussein Ali, Ameen A Noor, Israa M. Hayder","doi":"10.58496/mjaih/2024/002","DOIUrl":"https://doi.org/10.58496/mjaih/2024/002","url":null,"abstract":" This article investigates the capabilities and limitations of ChatGPT, a natural language processing (NLP) tool, and large language models (LLMs), developed from advanced artificial intelligence (AI). Designed to help computers understand and produce text understandable by humans, ChatGPT is particularly aimed at general scientific writing and healthcare research applications. Our methodology involved searching the Scopus database for ’type 2 diabetes’ and ’T2 diabetes’ articles from reputable journals. After eliminating duplicates, we used ChatGPT to formulate conclusions for each selected article by inputting their structured abstracts, excluding the original conclusions. Additionally, we tested ChatGPT’s response to simple misuse scenarios. Our findings show that ChatGPT can accurately grasp context and concisely summarize primary research findings. Additionally, it helps individuals who are not as experienced in mathematical analysis by providing coding guidelines for mathematical analyses in a variety of computer languages and by demystifying difficult model results. In conclusion, even if ChatGPT and other AI technologies are revolutionizing scientific publishing and healthcare, their use should be strictly controlled by authoritative laws.","PeriodicalId":424250,"journal":{"name":"Mesopotamian Journal of Artificial Intelligence in Healthcare","volume":"20 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140509022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating ChatGPT performance in Arabic dialects: A comparative study showing defects in responding to Jordanian and Tunisian general health prompts","authors":"Malik Sallam, Dhia Mousa","doi":"10.58496/mjaih/2024/001","DOIUrl":"https://doi.org/10.58496/mjaih/2024/001","url":null,"abstract":"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 Tunisian and Jordanian. \u0000Methods: 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. \u0000Results: 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). \u0000Conclusion: 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.","PeriodicalId":424250,"journal":{"name":"Mesopotamian Journal of Artificial Intelligence in Healthcare","volume":" 39","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139627384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diabetes at a Glance: Assessing AI Strategies for Early Diabetes Detection and Intervention","authors":"Ban Salman Shukur, Noorayisahbe Mohd Yaacob, Mohamed Doheir","doi":"10.58496/mjaih/2023/017","DOIUrl":"https://doi.org/10.58496/mjaih/2023/017","url":null,"abstract":"For the early identification of diabetes, a chronic illness that affects millions of people globally, artificial intelligence (AI) shows enormous promise. AI algorithms can recognize diabetes signs and give patients and healthcare providers early warnings by examining a variety of data sources, such as medical records, patient histories, and lifestyle factors. AI's capacity to evaluate sizable and intricate datasets is one of its main advantages in the diagnosis of diabetes. AI is capable of taking into account a broad range of variables that could lead to diabetes, such as age, BMI, food preferences, physical activity, and genetic predisposition. Artificial intelligence (AI) systems can recognize early indicators of diabetes and forecast the risk of developing the condition by analysing patterns and relationships among these variables. AI can also be applied to customize diabetic care and screening. Healthcare providers can improve patient outcomes and lessen the burden of disease by customizing suggestions and treatment options for each patient. AI can assess a patient's risk of diabetes by looking into their lifestyle choices and medical history. Healthcare providers can focus treatments and preventative actions to lower the chance of illness onset by identifying high-risk individuals can identify early indicators of diabetes by analysing blood glucose levels, demographic data, and lifestyle factors. Because of this, medical practitioners may be able to intervene early on, when the illness is most amenable to treatment systems that can monitor blood glucose levels and examine patient histories to create customized diabetic treatment programs. This can involve individualized prescription schedules, workout programs, and food advice. All things considered, AI has the power to completely transform the way people with diabetes are managed by giving patients and healthcare providers individualized data-driven insights. The use of AI in clinical practice is fraught with difficulties, such as privacy issues and a dearth of standardized data, yet the advantages in identifying and treating diabetes are substantial.","PeriodicalId":424250,"journal":{"name":"Mesopotamian Journal of Artificial Intelligence in Healthcare","volume":"168 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138982360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Review of the State of Cybersecurity in the Healthcare Industry and Propose Security Controls","authors":"A. Desai, Mbbs Mph Manal Desai","doi":"10.58496/mjaih/2023/016","DOIUrl":"https://doi.org/10.58496/mjaih/2023/016","url":null,"abstract":"Our study aims to identify the state of cybersecurity in the healthcare domain. Cyber incidents, includingransomware and similar cyber-attacks, impact healthcare entities. The review highlights thegovernment's efforts to protect citizens' health information by passing laws regulating the healthcareindustry. The review targeted healthcare-related laws in the United States, the European Union,Singapore, and India. The study identified that while developed countries like the United States, theEuropean Union, and Singapore have health data privacy laws, developing countries like India still needdata privacy laws. The nature, value, and sensitivity of data retained by healthcare entities make thehealthcare domain a rich target for cyber threat actors. Based on the study, the paper proposes securitypractices, including security monitoring, secure network architecture, information technologyvulnerability management, cyber policies, and user training, that can help prevent cyber-attacks onhealthcare entities.","PeriodicalId":424250,"journal":{"name":"Mesopotamian Journal of Artificial Intelligence in Healthcare","volume":"490 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138983261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Treating Psychological Depression Utilising Artificial Intelligence: AI for Precision Medicine- Focus on Procedures","authors":"Marwa M. Eid, Yundong Wang, George Benneh Mensah, Pramila Pudasaini","doi":"10.58496/mjaih/2023/015","DOIUrl":"https://doi.org/10.58496/mjaih/2023/015","url":null,"abstract":"Depression is a common and complex mental health condition that affects millions of people in the world. Medical advice, medications, and constant medical supervision by a specialist are common components of traditional treatment methods. Recently, there has been a growing interest in the potential of artificial intelligence to improve the diagnosis, monitoring, and treatment of depression. The potential of artificial intelligence algorithms has been demonstrated in the development of chatbots, or virtual agents, that can provide treatment, assistance, and support to individuals with depression. These artificial intelligence (AI) systems can simulate therapy sessions, offer strategies, monitor progress in treatment phases, and speak in natural language. Artificial intelligence has the potential to play an important role in the early diagnosis and prognosis of depression. By analysing multiple data sets and information such as genetic information, patient medical records, and social media posts using the Internet, artificial intelligence algorithms can identify individuals vulnerable to depression and distinguish them from normal humans. This facilitates the implementation of interventions and preventive measures at the right time and day. AI can also be used to improve depression treatment strategies. By analysing massive databases of patient data, AI systems can determine the ideal drug combinations, doses, amounts, and combinations for each patient. This personalized approach can lead to better treatment outcomes and reduces the trial-and-error process typically required to determine the best action. While AI has the potential to treat psychological depression, it is important to keep in mind that AI should never replace qualified and helpful medical professionals. Artificial intelligence in treating depression seeks to enhance and support the care provided by therapists, psychologists, and psychiatrists, rather than replace human communication and knowledge.","PeriodicalId":424250,"journal":{"name":"Mesopotamian Journal of Artificial Intelligence in Healthcare","volume":"93 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138586347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}