Research Paper: Artificial Intelligence in Medical: JEEV AI

Background: Artificial intelligence can help improve the quality of healthcare by analyzing vast amounts of data and providing more effective and personalized treatment plans. Researchers are working on developing AI-powered solutions that can help improve the outcomes of patients. Objective: To explore the potential of AI in improving healthcare outcomes and patient experience. Results: The study suggests that AI can improve healthcare efficiency and patient outcomes but cannot fully replace human healthcare professionals. AI can assist healthcare professionals in their work, leading to better resource utilization and improved patient care. However, there is still a need for human healthcare professionals to oversee AI systems and provide empathy and personalized care to patients. Conclusion: While there is immense potential for AI in healthcare, it is not yet feasible to replace human healthcare workers. Instead, it should be viewed as a tool that can help improve the efficiency a...

IRJET, 2020

Artificial Intelligence (AI) is rapidly being applied to a good range of healthcare medicine has been considering as an approach which will augment or substitute human professionals in primary health. Several sorts of AI are already use in companies. There are various automated systems and tools like Brain-computer interfaces (BCIs), arterial spin labeling (ASL), biomarkers, tongue processing (NLP) and various algorithm helps to attenuate errors and control disease progression. Although there are many instances during which AI can perform healthcare tasks also or better than humans, implementation factors will prevent large-scale automation of healthcare professional jobs for a substantial period. AI is to form computers are useful in solving problematic healthcare challenges and by using computers we will interpret data that's obtained by the diagnosis of varied chronic diseases like Alzheimer's, Diabetes, Cardiovascular diseases and various sorts of cancers like carcinoma , carcinoma , etc.

2023

Background: Artificial intelligence (AI) aims to create programs that reproduce human cognition and processes involved in interpreting complex data. Dermatology relies on morphological features and is ideal for applying AI image recognition for assisted diagnosis. Tibot is an AI app that analyzes skin conditions and works on the principle of a convolutional neural network. Appropriate research analyzing the accuracy of such apps is necessary. Objective: This study aims to analyze the predictability of the Tibot AI app in the identification of dermatological diseases as compared to a dermatologist. Methods: This is a cross-sectional study. After taking informed consent, photographs of lesions of patients with different skin conditions were uploaded to the app. In every condition, the AI predicted three diagnoses based on probability, and these were compared with that by a dermatologist. The ability of the AI app to predict the actual diagnosis in the top one and top three anticipated diagnoses (prediction accuracy) was used to evaluate the app's effectiveness. Sensitivity, specificity, and positive predictive value were also used to assess the app's performance. Chi-square test was used to contrast categorical variables. P<.05 was considered statistically significant. Results: A total of 600 patients were included. Clinical conditions included alopecia, acne, eczema, immunological disorders, pigmentary disorders, psoriasis, infestation, tumors, and infections. In the anticipated top three diagnoses, the app's mean prediction accuracy was 96.1% (95% CI 94.3%-97.5%), while for the exact diagnosis, it was 80.6% (95% CI 77.2%-83.7%). The prediction accuracy (top one) for alopecia, acne, pigmentary disorders, and fungal infections was 97.7%, 91.7%, 88.5%, and 82.9%, respectively. Prediction accuracy (top three) for alopecia, eczema, and tumors was 100%. The sensitivity and specificity of the app were 97% (95% CI 95%-98%) and 98% (95% CI 98%-99%), respectively. There is a statistically significant association between clinical and AI-predicted diagnoses in all conditions (P<.001). The AI app has shown promising results in diagnosing various dermatological conditions, and there is great potential for practical applicability.

Research Square (Research Square), 2023

Background Dermatological conditions are a relevant health problem. Machine learning (ML) models are increasingly being applied to dermatology as a diagnostic decision support tool using image analysis, especially for skin cancer detection and disease classi cation. Objective The objective of this study was to perform a prospective validation of an image analysis ML model, which is capable of screening 44 skin diseases, comparing its diagnostic accuracy with that of General Practitioners (GPs) and teledermatology (TD) dermatologists in a real-life setting. Methods Prospective, diagnostic accuracy study including 100 consecutive patients with a skin problem who visited a participating GP in central Catalonia, Spain, between June 2021 and October 2021. The skin issue was rst assessed by the GPs. Then an anonymised skin disease picture was taken and uploaded to the ML application, which returned a list with the Top-5 possible diagnosis in order of probability. The same image was then sent to a dermatologist via TD for diagnosis, as per clinical practice. The GPs Top-3, ML model's Top-5 and dermatologist's Top-3 assessments were compared to calculate the accuracy, sensitivity, speci city and diagnostic accuracy of the ML models. Results The overall Top-1 accuracy of the ML model (39%) was lower than that of GPs (64%) and dermatologists (72%). When the analysis was limited to the diagnoses on which the algorithm had been explicitly trained (n=82), the balanced Top-1 accuracy of the ML model increased (48%) and in the Top-3 (75%) was comparable to the GPs Top-3 accuracy (76%). The Top-5 accuracy of the ML model (89%) was comparable to the dermatologist Top-3 accuracy (90%). For the different diseases, the sensitivity of the model (Top-3 87% and Top-5 84%) is higher than that of the clinicians (Top-3 GPs 76% and Top-3 dermatologists 84%) only in the benign tumour pathology group, being on the other hand the most prevalent category (n=53). About the satisfaction of professionals, 92% of the GPs considered it as a useful diagnostic support tool (DST) for the differential diagnosis and in 60% of the cases as an aid in the nal diagnosis of the skin lesion. Conclusions The overall diagnostic accuracy of the model in this study under real conditions is lower than that of both GPs and dermatologists, a fact that is consistent with the few existing prospective studies under real conditions. These results highlight the potential of the ML models to assist GPs as a DST for skin conditions especially in the differential diagnosis. However, external testing in real conditions is essential for data validation and regulating these AI diagnostic models, in order to deploy ML models in a Primary Care setting. Autoderm® is a Class I CE marked DST in dermatology which uses ML to help diagnose skin lesions in a faster and more accurate way [40]. The current model can examine 44 different types of skin diseases, including in ammatory diseases, tumours, and genital skin problems, among others, representing 90% of the consultations made by the general population [1, 3, 4]. The model can be accessed through an Application Programming Interface (API) that can be integrated into any platform that is connected to the Internet. After examining a photograph, the model generates a ranking of the ve skin diseases that have the highest concordance with the lesion shown in the photo, sorted in order of probability. Autoderm® uses a set of 3 neural networks: resnet-18, resnet-34 [41] and squeezenet [42], provided by TorchVision (PyTorch)[43], which is used for applications such as computer vision and natural language processing. It was trained with an in-housedataset of 55,364 images in the training set and 13,841 for the test set. As for dermoscopic images, it was only trained with approximately 2000 images obtained from the HüD® dermatoscope and other Dermlite® dermatoscope models. These images were all taken by the layman or a healthcare worker using a smartphone. Data augmentation methods were used during algorithm training. This consists of modifying images in the training set (orientation, brightness, etc.) so that relevant information is not lost, but allowing the algorithm to be exposed to a more general distribution of data. After the data augmentation process, the number of images increased to approximately 120,000. The theoretical diagnostic accuracy of the model tested is 49.3% (Top-1), 70.1% (Top-3) and 81.7% (Top-5). Subsequently, two clinical studies were conducted with Autoderm® with earlier models in Sweden on Caucasian skin, and in Uganda on black skin (skin type 6 on the Fitzpatrick scale) [44, 45]. On these grounds, it is believed that ML dermatology models make PC more e cient, reducing the number of unnecessary referrals to dermatology, and leading to faster diagnosis, while maintaining accuracy and safety for individuals. Objectives The main objective of the study is the prospective validation of an ML model as a diagnostic decision support tool for skin diseases through a feasibility study in a real PC clinical practice setting in a region of Catalonia, Spain. The secondary objectives are: 1) evaluate the diagnostic accuracy and e cacy of the ML model in a clinical setting to determine the possibility of implementing it in a PC setting; 2) detect which skin lesions are missing in the study model; 3) estimate the rate of patients agreeing to participate in the study with the aim of using these data for future related research, 4) assess the PC professionals' degree of satisfaction with the use of the arti cial intelligence model. Methods The study protocol is described in detail in a separate publication (46); however, key elements are summarised below. Design: Prospective multicentre observational feasibility study with 100 consecutive patients who consulted PC for a skin lesion in the area of Central Catalonia. Anonymised photographs of the lesions were taken and entered into the Autoderm® model interface to obtain the diagnoses through

JMIR Dermatology

Background Artificial intelligence (AI) aims to create programs that reproduce human cognition and processes involved in interpreting complex data. Dermatology relies on morphological features and is ideal for applying AI image recognition for assisted diagnosis. Tibot is an AI app that analyzes skin conditions and works on the principle of a convolutional neural network. Appropriate research analyzing the accuracy of such apps is necessary. Objective This study aims to analyze the predictability of the Tibot AI app in the identification of dermatological diseases as compared to a dermatologist. Methods This is a cross-sectional study. After taking informed consent, photographs of lesions of patients with different skin conditions were uploaded to the app. In every condition, the AI predicted three diagnoses based on probability, and these were compared with that by a dermatologist. The ability of the AI app to predict the actual diagnosis in the top one and top three anticipated di...

JAMA Network Open, 2021

IMPORTANCE Most dermatologic cases are initially evaluated by nondermatologists such as primary care physicians (PCPs) or nurse practitioners (NPs). OBJECTIVE To evaluate an artificial intelligence (AI)-based tool that assists with diagnoses of dermatologic conditions. DESIGN, SETTING, AND PARTICIPANTS This multiple-reader, multiple-case diagnostic study developed an AI-based tool and evaluated its utility. Primary care physicians and NPs retrospectively reviewed an enriched set of cases representing 120 different skin conditions. Randomization was used to ensure each clinician reviewed each case either with or without AI assistance; each clinician alternated between batches of 50 cases in each modality. The reviews occurred from February 21 to

International Journal of Advanced Research in Engineering and Technology, 2018

Artificial intelligence is a branch of science concerned with building intelligent machines and parts that support performing multiple tasks using human intelligence. This is an approach towards replicating the existing human activities through machine learning and data analytics to enhance work and operational efficiency. AI is universally used in the healthcare industry for collecting, analysing, presenting, and managing critical healthcare and medical data. The practical methods of essential data analysis help healthcare workers make improved decisions, create personalised healthcare treatments and routine, and discover new drugs for enhanced treatment and patient's management. AI has completely revolutionised the healthcare industry and ensures improved safety and health standards. Computer devices and software help solve various challenges, detecting diseases such as diabetes and cardiovascular issues. It further provides practical solutions to these issues.

—Artificial Intelligence has significantly gained grounds in our daily livelihood in this age of information and technology. As with any field of study, evolution takes place in terms of breakthrough or developmental research leading to advancement and friendly usability of that specific technology. Problems from different areas have been successfully solved using Artificial Intelligence algorithms. In order to use AI algorithms in solving Personalized Medicine problems such as; disease detection or prediction, accurate disease diagnosis, and treatment optimization, the choice of the algorithm influenced by its ability and applicability matters. This paper reviews the application and ability of artificial neural network (ANN), support vector machines (SVM), Naï ve Bayes, and fuzzy logic in solving personalized medicine problems, and shows that the obtained results meet expectations. Also, the achievement from the previous studies encourages developers and researchers to use these algorithms in solving Medical and Personalized Medicine problems.

IJSREM, 2023

According to WHO Artificial Intelligence (AI) holds great promise for improving the delivery of healthcare and medicine worldwide, but only if ethics and human rights are put at the heart of its design, deployment, and use. AI continues to significantly outperform humans in terms of accuracy, efficiency and timely execution of medical and related administrative processes.This article briefly discusses the incorporation of AI based technologies for Medical assistance.The use of technology has aided in offering reliable and enhanced responses.This article aims to unfolds the importance of technology assisted services in Medical field and further through light upon recent advancements of AI in amalgamation with Healthcare sector.

Journal of Dermatology Research, 2023

Artificial intelligence is a branch of computer science that deals with the development of computer programs that aims to reproduce the human intelligence process [1]. Artificial intelligence has a crucial role to play in the field like dermatology in which visual data interpretations are required. Recent interest in AI had been driven by an evolution in machine learning resulting in the arrival of ‘deep learning.’ Given sufficient dataset size and processing power, deep learning utilizes Convolutional Neural Networks (CNNs). Deep learning technique is basically the modernized extended version of classical neural networks. The current neural network that is used is more superior in terms of the classical neural network as the current deep learning neural networks had multiple layers [2]. The deep learning method tends to deal with more complex and non-linear data. The deep learning in comparison with the classical neural networks can handle the larger volume and wide complex of data. As it learns directly from the dataset without human direction, deep learning is able to account for inter-data variability as well as process unstandardized data. AI algorithms have been currently used in the diagnosis of diabetic retinopathy, congenital cataracts, melanoma, and onychomycosis [3]. Outside clinical care, AI is being employed to support and potentially replace the roles of healthcare managers in resource, staffing, and financial management.