Artificial Intelligence in Medicine: Diabetes as a Model

Sridhar, G. R.; Lakshmi, Gumpeny

doi:10.1007/978-981-16-0415-7_14

Cited by 5 publications

(5 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As these systems gather, handle, and evaluate enormous volumes of private health information, it becomes critical to have strong cybersecurity safeguards. Finding the right balance between using patient data to provide individualized care and protecting individual privacy is a difficult undertaking that calls for strict laws, open procedures, and safe infrastructure [32].…”

Section: Challenges and Ethical Considerations In The Integration Of ...mentioning

confidence: 99%

How Artificial Intelligence Technology can be Used to Treat Diabetes

Jawaid,

Qureshi

2024

Preprint

View full text Add to dashboard Cite

Background: The research is conducted against the backdrop of the rising incidence of diabetes and the demand for novel approaches to improve patient outcomes. With the goal of revolutionizing conventional methods and addressing the changing healthcare landscape, the study concentrated on the application of AI technology in diabetes treatment. Objective: Examining the various ways AI can be used in diabetes care and assessing how it can enhance automated screenings, tailored treatment recommendations, predictive modeling, and patient self-management were the main goals of the study. Result: The findings demonstrated how well AI works to anticipate a patient's unique reaction to a therapy, assist medical personnel in making decisions, and give patients more control over their own care. Artificial intelligence (AI)-powered solutions showed promise in improving real-time monitoring and encouraging patient adherence, supporting a comprehensive strategy for managing diabetes. Conclusion: The research indicates that artificial intelligence (AI) has great potential to change the way diabetes is treated. However, it also highlights the need for more research, ethical issues, and validation of AI. The results advocate for joint efforts to ensure responsible and patient-centered technology innovation in diabetes care and encourage the integration of AI with new technologies.

show abstract

Section: Challenges and Ethical Considerations In The Integration Of ...mentioning

confidence: 99%

How Artificial Intelligence Technology can be Used to Treat Diabetes

Jawaid,

Qureshi

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…These records provide a comprehensive view of a patient’s health history, including diagnoses, medications, treatment plans, immunization dates, allergies, radiology images, and laboratory and test results ( 11 ). The use of EHR has been shown to improve patient care by providing accurate and legible notes that can track the individual’s lifetime of health status, medicine use, laboratory results, images from many sources, and transferable across locations ( 12 ). They also provide clinical guidelines, flag abnormal results, remind tests to be done, and reduce medication errors ( 12 ).…”

Section: Introductionmentioning

confidence: 99%

“…The use of EHR has been shown to improve patient care by providing accurate and legible notes that can track the individual’s lifetime of health status, medicine use, laboratory results, images from many sources, and transferable across locations ( 12 ). They also provide clinical guidelines, flag abnormal results, remind tests to be done, and reduce medication errors ( 12 ). Furthermore, the integration of artificial intelligence and machine learning techniques with EHR has the potential to enhance patient profiling.…”

Section: Introductionmentioning

confidence: 99%

Profiling of patients with type 2 diabetes based on medication adherence data

Markovič

Grubelnik

Završnik

et al. 2023

Front. Public Health

View full text Add to dashboard Cite

IntroductionType 2 diabetes mellitus (T2DM) is a complex, chronic disease affecting multiple organs with varying symptoms and comorbidities. Profiling patients helps identify those with unfavorable disease progression, allowing for tailored therapy and addressing special needs. This study aims to uncover different T2DM profiles based on medication intake records and laboratory measurements, with a focus on how individuals with diabetes move through disease phases.MethodsWe use medical records from databases of the last 20 years from the Department of Endocrinology and Diabetology of the University Medical Center in Maribor. Using the standard ATC medication classification system, we created a patient-specific drug profile, created using advanced natural language processing methods combined with data mining and hierarchical clustering.ResultsOur results show a well-structured profile distribution characterizing different age groups of individuals with diabetes. Interestingly, only two main profiles characterize the early 40–50 age group, and the same is true for the last 80+ age group. One of these profiles includes individuals with diabetes with very low use of various medications, while the other profile includes individuals with diabetes with much higher use. The number in both groups is reciprocal. Conversely, the middle-aged groups are characterized by several distinct profiles with a wide range of medications that are associated with the distinct concomitant complications of T2DM. It is intuitive that the number of profiles increases in the later age groups, but it is not obvious why it is reduced later in the 80+ age group. In this context, further studies are needed to evaluate the contributions of a range of factors, such as drug development, drug adoption, and the impact of mortality associated with all T2DM-related diseases, which characterize these middle-aged groups, particularly those aged 55–75.ConclusionOur approach aligns with existing studies and can be widely implemented without complex or expensive analyses. Treatment and drug use data are readily available in healthcare facilities worldwide, allowing for profiling insights into individuals with diabetes. Integrating data from other departments, such as cardiology and renal disease, may provide a more sophisticated understanding of T2DM patient profiles.

show abstract

“…Digitization is a promising path which makes artificial intelligence (AI) possible. Digital data is captured from a variety of sources including electronic medical records (EMRs), and wearable sensors [5] . This has led to questions regarding ethics and legality in the use of AI in healthcare sector.…”

Section: Introductionmentioning

confidence: 99%

“…AI has been defined as the 'use of computerprocessing capabilities of symbols to find generic methods for automatic perceptual, cognitive and manipulating activities via algorithms' [5] . Due to the burgeoning availability of data which cannot be meaningfully interpreted by individuals or even by traditional statistical methods, there is need for more complex algorithmic computer analysis [6] .…”

Section: Introductionmentioning

confidence: 99%

Ethical Issues of Artificial Intelligence in Diabetes Mellitus

Sridhar,

Lakshmi

2023

MRAJ

View full text Add to dashboard Cite

Artificial intelligence (AI) has permeated various branches of clinical medicine, with promising applications in predictive, diagnostic and therapeutic areas. Digital innovations are increasingly useful in the management of non communicable diseases in the form of tracking applications, data collection systems (EMRs) and wearable sensors. Globally, diabetes mellitus is the most common and serious non communicable disease. There is a mismatch between the people with diabetes and the number of healthcare professionals needed to manage them. Therefore, artificial intelligence has the potential to play a significant role in addressing the unmet need. Majority of AI applications have been developed for the diabetes population. Ethical issues arising from the application can be carried over to its application in other areas of clinical medicine. Among diabetic population, artificial intelligence has been prominently employed in screening for diabetic retinopathy. Continuous glucose monitoring and insulin pumps are other areas of application. Data collection and sharing through AI media can ease the burden of poor doctor-patient ratio, and improve efficacy of treatment. Despite its advantages, and the fact that citizen juries have been found to be favourable towards the use of AI in research and treatment, certain drawbacks continue to exist. With the threat of data theft and breach of privacy, due diligence must be given to ethical and legal aspects to protect the patient. It is acknowledged that AI can facilitate the decision making process but not entirely replace a physician's role. With able governing laws, systems to protect safety, minimize bias and improve transparency, AI and precision medicine could help control the burden of disease.

show abstract

Artificial Intelligence in Medicine: Diabetes as a Model

Cited by 5 publications

References 65 publications

How Artificial Intelligence Technology can be Used to Treat Diabetes

How Artificial Intelligence Technology can be Used to Treat Diabetes

Profiling of patients with type 2 diabetes based on medication adherence data

Ethical Issues of Artificial Intelligence in Diabetes Mellitus

Contact Info

Product

Resources

About