Transformative Potential of AI in Healthcare: Definitions, Applications, and Navigating the Ethical Landscape and Public Perspectives

Bekbolatova, Molly; Mayer, Jonathan; Ong, Chi Wei; Toma, Milan

doi:10.3390/healthcare12020125

Cited by 15 publications

(7 citation statements)

References 149 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Diabetes is a multifaceted disease, influenced by genetics, lifestyles, and socioeconomic factors, thus demanding personalized treatment approaches. Machine learning models stand at the forefront of enabling such individualized care by leveraging vast amounts of health data to uncover complex, nonlinear relationships that may go undetected by traditional methods [42,43]. The importance of these models lies in their ability to digest diverse datasets, including genetic profiles, dietary habits, exercise routines, and even environmental and social influences, to predict diabetes-related outcomes with precision.…”

Section: Discussionmentioning

confidence: 99%

Cardiovascular Health Management in Diabetic Patients with Machine-Learning-Driven Predictions and Interventions

Jose,

Syed,

Thomas

et al. 2024

Applied Sciences

Self Cite

View full text Add to dashboard Cite

The advancement of machine learning in healthcare offers significant potential for enhancing disease prediction and management. This study harnesses the PyCaret library—a Python-based machine learning toolkit—to construct and refine predictive models for diagnosing diabetes mellitus and forecasting hospital readmission rates. By analyzing a rich dataset featuring a variety of clinical and demographic variables, we endeavored to identify patients at heightened risk for diabetes complications leading to readmissions. Our methodology incorporates an evaluation of numerous machine learning algorithms, emphasizing their predictive accuracy and generalizability to improve patient care. We scrutinized the predictive strength of each model concerning crucial metrics like accuracy, precision, recall, and the area under the curve, underlining the imperative to eliminate false diagnostics in the field. Special attention is given to the use of the light gradient boosting machine classifier among other advanced modeling techniques, which emerge as particularly effective in terms of the Kappa statistic and Matthews correlation coefficient, suggesting robustness in prediction. The paper discusses the implications of diabetes management, underscoring interventions like lifestyle changes and pharmacological treatments to avert long-term complications. Through exploring the intersection of machine learning and health informatics, the study reveals pivotal insights into algorithmic predictions of diabetes readmission. It also emphasizes the necessity for further research and development to fully incorporate machine learning into modern diabetes care to prompt timely interventions and achieve better overall health outcomes. The outcome of this research is a testament to the transformative impact of automated machine learning in the realm of healthcare analytics.

show abstract

Section: Discussionmentioning

confidence: 99%

Cardiovascular Health Management in Diabetic Patients with Machine-Learning-Driven Predictions and Interventions

Jose,

Syed,

Thomas

et al. 2024

Applied Sciences

Self Cite

View full text Add to dashboard Cite

show abstract

“…Our group has a history of exploring the applications of machine learning in various healthcare and medical contexts, ranging from patient safety enhancement through integrated sensor technology [ 19 ], to the prediction of coronary artery disease [ 20 ], cardiovascular health management in diabetic patients [ 21 ], and image detection of colonic polyps [ 22 ]. We have also contributed to the academic discourse on the transformative potential of AI in healthcare, navigating the ethical landscape, and public perspectives through our review paper [ 23 ]. Furthermore, our entry paper on predictive modeling in medicine underscores our commitment to harnessing the power of machine learning for improved healthcare outcomes [ 24 ].…”

Section: Discussionmentioning

confidence: 99%

The Role of ArtificiaI Intelligence in Brain Tumor Diagnosis: An Evaluation of a Machine Learning Model

Abraham,

Jose,

Farooqui

et al. 2024

Cureus

Self Cite

View full text Add to dashboard Cite

This research study explores of the effectiveness of a machine learning image classification model in the accurate identification of various types of brain tumors. The types of tumors under consideration in this study are gliomas, meningiomas, and pituitary tumors. These are some of the most common types of brain tumors and pose significant challenges in terms of accurate diagnosis and treatment.The machine learning model that is the focus of this study is built on the Google Teachable Machine platform (Alphabet Inc., Mountain View, CA). The Google Teachable Machine is a machine learning image classification platform that is built from Tensorflow, a popular open-source platform for machine learning. The Google Teachable Machine model was specifically evaluated for its ability to differentiate between normal brains and the aforementioned types of tumors in MRI images. MRI images are a common tool in the diagnosis of brain tumors, but the challenge lies in the accurate classification of the tumors. This is where the machine learning model comes into play. The model is trained to recognize patterns in the MRI images that correspond to the different types of tumors. The performance of the machine learning model was assessed using several metrics. These include precision, recall, and F1 score. These metrics were generated from a confusion matrix analysis and performance graphs. A confusion matrix is a table that is often used to describe the performance of a classification model. Precision is a measure of the model's ability to correctly identify positive instances among all instances it identified as positive. Recall, on the other hand, measures the model's ability to correctly identify positive instances among all actual positive instances. The F1 score is a measure that combines precision and recall providing a single metric for model performance. The results of the study were promising.The Google Teachable Machine model demonstrated high performance, with accuracy, precision, recall, and F1 scores ranging between 0.84 and 1.00. This suggests that the model is highly effective in accurately classifying the different types of brain tumors. This study provides insights into the potential of machine learning models in the accurate classification of brain tumors. The findings of this study lay the groundwork for further research in this area and have implications for the diagnosis and treatment of brain tumors. The study also highlights the potential of machine learning in enhancing the field of medical imaging and diagnosis. With the increasing complexity and volume of medical data, machine learning models like the one evaluated in this study could play a crucial role in improving the accuracy and efficiency of diagnoses. Furthermore, the study underscores the importance of continued research and development in this field to further refine these models and overcome any potential limitations or challenges. Overall, the study contributes to the field of medical imaging and machine learning and sets the stage for future ...

show abstract

“…Through the utilization of machine learning algorithms, natural language processing, and computer vision, AI facilitates the analysis of intricate medical data. Its integration into healthcare systems seeks to aid clinicians, customize patient treatments, and improve overall population health, all while tackling the obstacles posed by escalating expenses and constrained resources [ 22 ]. Likewise, nursing AI tools encompass clinical decision support, mobile health, sensor-based technologies, voice assistants, and robotics.…”

Section: Conceptual Frameworkmentioning

confidence: 99%

Digital proficiency: assessing knowledge, attitudes, and skills in digital transformation, health literacy, and artificial intelligence among university nursing students

Abou Hashish,

Alnajjar

2024

BMC Med Educ

View full text Add to dashboard Cite

Background Implementing digital transformation and artificial intelligence (AI) in education and practice necessitates understanding nursing students’ attitudes and behaviors as end-users toward current and future digital and AI applications. Purpose This study aimed to assess the perceived knowledge, attitudes, and skills of nursing students regarding digital transformation, as well as their digital health literacy (DHL) and attitudes toward AI. Furthermore, we investigated the potential correlations among these variables. Methods A descriptive correlational design was employed in a Saudi nursing college utilizing a convenience sample of 266 nursing students. A structured questionnaire consisting of six sections was used, covering personal information, knowledge, skills and attitudes toward digital transformation, digital skills, DHL, and attitudes toward AI. Descriptive statistics and Pearson correlation were employed for data analysis. Results Nursing students exhibited good knowledge of and positive attitudes toward digital transformation services. They possessed strong digital skills, and their DHL and positive attitude toward AI were commendable. Overall, the findings indicated significant positive correlations between knowledge of digital transformation services and all the digital variables measured (p = < 0.05). Senior students reported greater digital knowledge and a positive attitude toward AI. Conclusion The study recommends an innovative undergraduate curriculum that integrates opportunities for hands-on experience with digital healthcare technologies to enhance their digital literacy and skills.

show abstract

Transformative Potential of AI in Healthcare: Definitions, Applications, and Navigating the Ethical Landscape and Public Perspectives

Cited by 15 publications

References 149 publications

Cardiovascular Health Management in Diabetic Patients with Machine-Learning-Driven Predictions and Interventions

Cardiovascular Health Management in Diabetic Patients with Machine-Learning-Driven Predictions and Interventions

The Role of ArtificiaI Intelligence in Brain Tumor Diagnosis: An Evaluation of a Machine Learning Model

Digital proficiency: assessing knowledge, attitudes, and skills in digital transformation, health literacy, and artificial intelligence among university nursing students

Contact Info

Product

Resources

About