scholarly journals Using artificial intelligence on dermatology conditions in Uganda: A case for diversity in training data sets for machine learning

2019 ◽  
Author(s):  
Louis Henry Kamulegeya ◽  
Mark Okello ◽  
John Mark Bwanika ◽  
Davis Musinguzi ◽  
William Lubega ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Diagnostic Accuracy ◽  
Body Part ◽  
Clinical Decision ◽  
Disease Diagnosis ◽  
Training Data ◽  
Correct Prediction ◽  
Data Sets ◽  
Body Parts ◽  
Correct Treatment

AbstractIntroductionArtificial intelligence (AI) in healthcare has gained momentum with advances in affordable technology that has potential to help in diagnostics, predictive healthcare and personalized medicine. In pursuit of applying universal non-biased AI in healthcare, it is essential that data from different settings (gender, age and ethnicity) is represented. We present findings from beta-testing an AI-powered dermatological algorithm called Skin Image Search, by online dermatology company First Derm on Fitzpatrick 6 skin type (dark skin) dermatological conditions.Methods123 dermatological images selected from a total of 173 images retrospectively extracted from the electronic database of a Ugandan telehealth company, The Medical Concierge Group (TMCG) after getting their consent. Details of age, gender and dermatological clinical diagnosis were analyzed using R on R studio software to assess the diagnostic accuracy of the AI app along disease diagnosis and body part. Predictability levels of the AI app was graded on a scale of 0 to 5, where 0-no prediction made and 1-5 demonstrating reducing correct prediction.Results76 (62%) of the dermatological images were from females and 47 (38%) from males. The 5 most reported body parts were; genitals (20%), trunk (20%), lower limb (14.6%), face (12%) and upper limb (12%) with the AI app predicting a diagnosis in 62% of image body parts uploaded. Overall diagnostic accuracy of the AI app was low at 17% (21 out of 123 predictable images) with varying predictability levels correctness i.e. 1-8.9%, 2-2.4%, 3-2.4%, 4-1.6%, 5-1.6% with performance along individual diagnosis highest with dermatitis (80%).ConclusionThere is a need for diversity in the image datasets used when training dermatology algorithms for AI applications in clinical decision support as a means to increase accuracy and thus offer correct treatment across skin types and geographies.

scholarly journals Artificial Intelligence Is a Promising Prospect for the Detection of Prostate Cancer Extracapsular Extension With Mp-mri: A Two-center Comparative Study

2021 ◽  
Author(s):  
Ying Hou ◽  
Yi-Hong Zhang ◽  
Jie Bao ◽  
Mei-Ling Bao ◽  
Guang Yang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Artificial Intelligence ◽  
Paired Comparison ◽  
Characteristic Curve ◽  
Multiparametric Mri ◽  
Training Data ◽  
Urinary Continence ◽  
Data Sets ◽  
Extracapsular Extension ◽  
Promising Alternative

Abstract Purpose: A balance between preserving urinary continence and achievement of negative margins is of clinical relevance while implementary difficulty. Preoperatively accurate detection of prostate cancer (PCa) extracapsular extension (ECE) is thus crucial for determining appropriate treatment options. We aimed to develop and clinically validate an artificial intelligence (AI)-assisted tool for the detection of ECE in patients with PCa using multiparametric MRI. Methods: 849 patients with localized PCa underwent multiparametric MRI before radical prostatectomy were retrospectively included from two medical centers. The AI tool was built on a ResNeXt network embedded with a spatial attention map of experts’ prior knowledges (PAGNet) from 596 training data sets. The tool was validated in 150 internal and 103 external data sets, respectively; and its clinical applicability was compared with expert-based interpretation and AI-expert interaction.Results: An index PAGNet model using a single-slice image yielded the highest areas under the receiver operating characteristic curve (AUC) of 0.857 (95% confidence interval [CI], 0.827-0.884), 0.807 (95% CI, 0.735-0.867) and 0.728 (95% CI, 0.631-0.811) in the training, internal test and external test cohorts, compared to the conventional ResNeXt networks. For experts, the inter-reader agreement was observed in only 437/849 (51.5%) patients with a Kappa value 0.343. And the performance of two experts (AUC, 0.632 to 0.741 vs 0.715 to 0.857) was lower (paired comparison, all p values < 0.05) than that of AI assessment. When expert’ interpretations were adjusted by the AI assessments, the performance of both two experts was improved.Conclusion: Our AI tool, showing improved accuracy, offers a promising alternative to human experts for imaging staging of PCa ECE using multiparametric MRI.

scholarly journals Alternative personal data governance models

2019 ◽  
Author(s):  
Bastian Greshake Tzovaras ◽  
Mad Price Ball
Keyword(s):  
Artificial Intelligence ◽  
Data Exchange ◽  
Personal Data ◽  
Training Data ◽  
Data Sets ◽  
Data Governance ◽  
Forthcoming Book ◽  
Governance Strategies ◽  
Governance Models ◽  
Self Driving Cars

The not-so-secret ingredient that underlies all successful Artificial Intelligence / Machine Learning (AI/ML) methods is training data. There would be no facial recognition, no targeted advertisements and no self-driving cars if it was not for large enough data sets with which those algorithms have been trained to perform their tasks. Given how central these data sets are, important ethics questions arise: How is data collection performed? And how do we govern its' use? This chapter – part of a forthcoming book – looks at why new data governance strategies are needed; investigates the relation of different data governance models to historic consent approaches; and compares different implementations of personal data exchange models.

Liquid Pipeline Rupture Detection Using Multiple Artificial Intelligence Classifiers During Steady-State and Transient Operations

2020 ◽  
Author(s):  
Christopher MacDonald ◽  
Michael Yang ◽  
Shawn Learn ◽  
Ron Hugo ◽  
Simon Park
Keyword(s):  
Artificial Intelligence ◽  
Training Data ◽  
Sensor Data ◽  
Data Sets ◽  
Data Set ◽  
Transient Model ◽  
Rule Based ◽  
Detection Systems ◽  
Pump Station ◽  
Neuro Fuzzy

Abstract There are several challenges associated with existing rupture detection systems such as their inability to accurately detect during transient (such as pump dynamics) conditions, delayed responses and their inability to transfer models to different pipeline configurations easily. To address these challenges, we employ multiple Artificial Intelligence (AI) classifiers that rely on pattern recognitions instead of traditional operator-set thresholds. AI techniques, consisting of two-dimensional (2D) Convolutional Neural Networks (CNN) and Adaptive Neuro Fuzzy Interface Systems (ANFIS), are used to mimic processes performed by operators during a rupture event. This includes both visualization (using CNN) and rule-based decision making (using ANFIS). The system provides a level of reasoning to an operator through the use of the rule-based AI system. Pump station sensor data is non-dimensionalized prior to AI processing, enabling application to pipeline configurations outside of the training data set. AI algorithms undergo testing and training using two data sets: laboratory-collected data that mimics transient pump-station operations and real operator data that includes Real Time Transient Model (RTTM) simulated ruptures. The use of non-dimensional sensor data enables the system to detect ruptures from pipeline data not used in the training process.

Humor, Ethics, and Dignity: Being Human in the Age of Artificial Intelligence

2019 ◽  
Vol 33 (1) ◽  
pp. 3-12 ◽  
Author(s):  
Sean Kanuck
Keyword(s):  
Artificial Intelligence ◽  
New Technologies ◽  
Ethical Reasoning ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Data Sets ◽  
Human Beings ◽  
Human In The Loop ◽  
Software Algorithms ◽  
Decision Making Processes

AbstractThe growing adoption of artificial intelligence (AI) raises questions about what comparative advantage, if any, human beings will have over machines in the future. This essay explores what it means to be human and how those unique characteristics relate to the digital age. Humor and ethics both rely upon higher-level cognition that accounts for unstructured and unrelated data. That capability is also vital to decision-making processes—such as jurisprudence and voting systems. Since machine learning algorithms lack the ability to understand context or nuance, reliance on them could lead to undesired results for society. By way of example, two case studies are used to illustrate the legal and moral considerations regarding the software algorithms used by driverless cars and lethal autonomous weapons systems. Social values must be encoded or introduced into training data sets if AI applications are to be expected to produce results similar to a “human in the loop.” There is a choice to be made, then, about whether we impose limitations on these new technologies in favor of maintaining human control, or whether we seek to replicate ethical reasoning and lateral thinking in the systems we create. The answer will have profound effects not only on how we interact with AI but also on how we interact with one another and perceive ourselves.

scholarly journals Facial Recognition Intensity in Disease Diagnosis Using Automatic Facial Recognition

2021 ◽  
Vol 11 (11) ◽  
pp. 1172
Author(s):  
Danning Wu ◽  
Shi Chen ◽  
Yuelun Zhang ◽  
Huabing Zhang ◽  
Qing Wang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Diagnostic Accuracy ◽  
Facial Recognition ◽  
Disease Diagnosis ◽  
Facial Features ◽  
Random Effects Models ◽  
Pooled Data ◽  
Quantitative Synthesis ◽  
Meta Regression ◽  
First Time

Artificial intelligence (AI) technology is widely applied in different medical fields, including the diagnosis of various diseases on the basis of facial phenotypes, but there is no evaluation or quantitative synthesis regarding the performance of artificial intelligence. Here, for the first time, we summarized and quantitatively analyzed studies on the diagnosis of heterogeneous diseases on the basis on facial features. In pooled data from 20 systematically identified studies involving 7 single diseases and 12,557 subjects, quantitative random-effects models revealed a pooled sensitivity of 89% (95% CI 82% to 93%) and a pooled specificity of 92% (95% CI 87% to 95%). A new index, the facial recognition intensity (FRI), was established to describe the complexity of the association of diseases with facial phenotypes. Meta-regression revealed the important contribution of FRI to heterogeneous diagnostic accuracy (p = 0.021), and a similar result was found in subgroup analyses (p = 0.003). An appropriate increase in the training size and the use of deep learning models helped to improve the diagnostic accuracy for diseases with low FRI, although no statistically significant association was found between accuracy and photographic resolution, training size, AI architecture, and number of diseases. In addition, a novel hypothesis is proposed for universal rules in AI performance, providing a new idea that could be explored in other AI applications.

scholarly journals Artificial Intelligence in Cardiac MRI: Is Clinical Adoption Forthcoming?

2022 ◽  
Vol 8 ◽  
Author(s):  
Anastasia Fotaki ◽  
Esther Puyol-Antón ◽  
Amedeo Chiribiri ◽  
René Botnar ◽  
Kuberan Pushparajah ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Magnetic Resonance ◽  
Clinical Decision Making ◽  
Prediction Models ◽  
Wide Spectrum ◽  
Clinical Decision ◽  
Training Data ◽  
Response To Treatment ◽  
Cine Imaging ◽  
Post Processing

Artificial intelligence (AI) refers to the area of knowledge that develops computerised models to perform tasks that typically require human intelligence. These algorithms are programmed to learn and identify patterns from “training data,” that can be subsequently applied to new datasets, without being explicitly programmed to do so. AI is revolutionising the field of medical imaging and in particular of Cardiovascular Magnetic Resonance (CMR) by providing deep learning solutions for image acquisition, reconstruction and analysis, ultimately supporting the clinical decision making. Numerous methods have been developed over recent years to enhance and expedite CMR data acquisition, image reconstruction, post-processing and analysis; along with the development of promising AI-based biomarkers for a wide spectrum of cardiac conditions. The exponential rise in the availability and complexity of CMR data has fostered the development of different AI models. Integration in clinical routine in a meaningful way remains a challenge. Currently, innovations in this field are still mostly presented in proof-of-concept studies with emphasis on the engineering solutions; often recruiting small patient cohorts or relying on standardised databases such as Multi-ethnic Study on atherosclerosis (MESA), UK Biobank and others. The wider incorporation of clinically valid endpoints such as symptoms, survival, need and response to treatment remains to be seen. This review briefly summarises the current principles of AI employed in CMR and explores the relevant prospective observational studies in cardiology patient cohorts. It provides an overview of clinical studies employing undersampled reconstruction techniques to speed up the scan encompassing cine imaging, whole-heart imaging, multi-parametric mapping and magnetic resonance fingerprinting along with the clinical utility of AI applications in image post-processing, and analysis. Specific focus is given to studies that have incorporated CMR-derived prediction models for prognostication in cardiac disease. It also discusses current limitations and proposes potential developments to enable multi-disciplinary collaboration for improved evidence-based medicine. AI is an extremely promising field and the timely integration of clinician's input in the ingenious technical investigator's paradigm holds promise for a bright future in the medical field.

scholarly journals Evaluation of the Diagnostic Accuracy of an Online Artificial Intelligence Application for Skin Disease Diagnosis

2020 ◽  
Vol 100 (16) ◽  
pp. adv00260
Author(s):  
O Zaar ◽  
A Larson ◽  
S Polesie ◽  
K Saleh ◽  
M Tarstedt ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Diagnostic Accuracy ◽  
Skin Disease ◽  
Disease Diagnosis ◽  
Artificial Intelligence Application ◽  
Intelligence Application

scholarly journals Artificial Intelligence and Healthcare Regulatory and Legal Concerns

2021 ◽  
Author(s):  
Krishnan Ganapathy
Keyword(s):  
Artificial Intelligence ◽  
Product Liability ◽  
Diagnostic Errors ◽  
Training Data ◽  
Data Sets ◽  
Digital Information ◽  
Regulatory Requirements ◽  
The Law ◽  
User Friendly ◽  
Clinical Problems

We are in a stage of transition as artificial intelligence (AI) is increasingly being used in healthcare across the world. Transitions offer opportunities compounded with difficulties. It is universally accepted that regulations and the law can never keep up with the exponential growth of technology. This paper discusses liability issues when AI is deployed in healthcare. Ever-changing, futuristic, user friendly, uncomplicated regulatory requirements promoting compliance and adherence are needed. Regulators have to understand that software itself could be a software as a medical device (SaMD). Benefits of AI could be delayed if slow, expensive clinical trials are mandated. Regulations should distinguish between diagnostic errors, malfunction of technology, or errors due to initial use of inaccurate/inappropriate data as training data sets. The sharing of responsibility and accountability when implementation of an AI-based recommendation causes clinical problems is not clear. Legislation is necessary to allow apportionment of damages consequent to malfunction of an AI-enabled system. Product liability is ascribed to defective equipment and medical devices. However, Watson, the AI-enabled supercomputer, is treated as a consulting physician and not categorised as a product. In India, algorithms cannot be patented. There are no specific laws enacted to deal with AI in healthcare. DISHA or the Digital Information Security in Healthcare Act when implemented in India would hopefully cover some issues. Ultimately, the law is interpreted contextually and perceptions could be different among patients, clinicians and the legal system. This communication is to create the necessary awareness among all stakeholders.

Increasing the Accuracy of Predictive Algorithms

2011 ◽  
pp. 1906-1910
Author(s):  
Sotiris Kotsiantis ◽  
Dimitris Kanellopoulos ◽  
Panayotis Pintelas
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Bayesian Networks ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Data Sets ◽  
Combining Classifiers ◽  
Predictive Algorithms

In classification learning, the learning scheme is presented with a set of classified examples from which it is expected tone can learn a way of classifying unseen examples (see Table 1). Formally, the problem can be stated as follows: Given training data {(x1, y1)…(xn, yn)}, produce a classifier h: X- >Y that maps an object x ? X to its classification label y ? Y. A large number of classification techniques have been developed based on artificial intelligence (logic-based techniques, perception-based techniques) and statistics (Bayesian networks, instance-based techniques). No single learning algorithm can uniformly outperform other algorithms over all data sets. The concept of combining classifiers is proposed as a new direction for the improvement of the performance of individual machine learning algorithms. Numerous methods have been suggested for the creation of ensembles of classi- fiers (Dietterich, 2000). Although, or perhaps because, many methods of ensemble creation have been proposed, there is as yet no clear picture of which method is best.

scholarly journals Accessing Artificial Intelligence for Clinical Decision-Making

2021 ◽  
Vol 3 ◽  
Author(s):  
Chris Giordano ◽  
Meghan Brennan ◽  
Basma Mohamed ◽  
Parisa Rashidi ◽  
François Modave ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Patient Care ◽  
Clinical Decision Making ◽  
Clinical Medicine ◽  
Clinical Decision ◽  
Disruptive Innovation ◽  
Decision Makers ◽  
Data Sets ◽  
Sequential Decision

Advancements in computing and data from the near universal acceptance and implementation of electronic health records has been formative for the growth of personalized, automated, and immediate patient care models that were not previously possible. Artificial intelligence (AI) and its subfields of machine learning, reinforcement learning, and deep learning are well-suited to deal with such data. The authors in this paper review current applications of AI in clinical medicine and discuss the most likely future contributions that AI will provide to the healthcare industry. For instance, in response to the need to risk stratify patients, appropriately cultivated and curated data can assist decision-makers in stratifying preoperative patients into risk categories, as well as categorizing the severity of ailments and health for non-operative patients admitted to hospitals. Previous overt, traditional vital signs and laboratory values that are used to signal alarms for an acutely decompensating patient may be replaced by continuously monitoring and updating AI tools that can pick up early imperceptible patterns predicting subtle health deterioration. Furthermore, AI may help overcome challenges with multiple outcome optimization limitations or sequential decision-making protocols that limit individualized patient care. Despite these tremendously helpful advancements, the data sets that AI models train on and develop have the potential for misapplication and thereby create concerns for application bias. Subsequently, the mechanisms governing this disruptive innovation must be understood by clinical decision-makers to prevent unnecessary harm. This need will force physicians to change their educational infrastructure to facilitate understanding AI platforms, modeling, and limitations to best acclimate practice in the age of AI. By performing a thorough narrative review, this paper examines these specific AI applications, limitations, and requisites while reviewing a few examples of major data sets that are being cultivated and curated in the US.

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