Artificial Intelligence and Machine Learning Based Intervention in Medical Infrastructure: A Review and Future Trends

Kumar, Kamlesh; Kumar, Prince; Deb, Dipankar; Ungureşan, Mihaela-Ligia; Mureşan, Vlad

doi:10.3390/healthcare11020207

Cited by 38 publications

(14 citation statements)

References 109 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Artificial intelligence and machine learning are other essential facets inseparable from today’s medicine and the facilitation of decision-making. Its applications found their way into clinical routine, significantly improving patient care [ 21 , 23 ]. Multiple studies have explored the potential use of artificial intelligence in many areas of medicine, such as cardiology [ 21 ], neurology [ 20 ], oncology [ 22 ], haematology [ 42 ], nephrology [ 43 ], gastroenterology, hepatology, orthopaedics and rheumatology [ 21 ].…”

Section: Discussionmentioning

confidence: 99%

“…It is, therefore, necessary to consider how clinicians working with this technology can be supported. One of the solutions for significantly improving patient care is the application of artificial intelligence and machine learning [ 20 , 21 , 22 , 23 ]. Situation awareness and user-centred design principles also play an essential role in facilitating decision-making in complex clinical situations [ 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

User Perceptions of ROTEM-Guided Haemostatic Resuscitation: A Mixed Qualitative–Quantitative Study

et al. 2023

View full text Add to dashboard Cite

Viscoelastic point-of-care haemostatic resuscitation methods, such as ROTEM or TEG, are crucial in deciding on time-efficient personalised coagulation interventions. International transfusion guidelines emphasise increased patient safety and reduced treatment costs. We analysed care providers’ perceptions of ROTEM to identify perceived strengths and areas for improvement. We conducted a single-centre, mixed qualitative–quantitative study consisting of interviews followed by an online survey. Using a template approach, we first identified themes in the responses given by care providers about ROTEM. Later, the participants rated six statements based on the identified themes on five-point Likert scales in an online questionnaire. Seventy-seven participants were interviewed, and 52 completed the online survey. By analysing user perceptions, we identified ten themes. The most common positive theme was “high accuracy”. The most common negative theme was “need for training”. In the online survey, 94% of participants agreed that monitoring the real-time ROTEM temograms helps to initiate targeted treatment more quickly and 81% agreed that recurrent ROTEM training would be beneficial. Anaesthesia care providers found ROTEM to be accurate and quickly available to support decision-making in dynamic and complex haemostatic situations. However, clinicians identified that interpreting ROTEM is a complex and cognitively demanding task that requires significant training needs.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

User Perceptions of ROTEM-Guided Haemostatic Resuscitation: A Mixed Qualitative–Quantitative Study

et al. 2023

View full text Add to dashboard Cite

show abstract

“…The UNOS, INTERMACS, and the ISHLT registry databases are the largest databases available. There is a critical need for data sharing infrastructure that is inclusive of multiple biodomains (imaging, clinical text, electronic heart care system entries, and vital outcomes) to enable generation of accurate ML models that can be validated, meet user's expectations, and continuously updated to remain current with the clinical practice (52).…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence guidance of advanced heart failure therapies: A systematic scoping review

Al‐Ani

Chen

Hashky

et al. 2023

Front. Cardiovasc. Med.

View full text Add to dashboard Cite

IntroductionArtificial intelligence can recognize complex patterns in large datasets. It is a promising technology to advance heart failure practice, as many decisions rely on expert opinions in the absence of high-quality data-driven evidence.MethodsWe searched Embase, Web of Science, and PubMed databases for articles containing “artificial intelligence,” “machine learning,” or “deep learning” and any of the phrases “heart transplantation,” “ventricular assist device,” or “cardiogenic shock” from inception until August 2022. We only included original research addressing post heart transplantation (HTx) or mechanical circulatory support (MCS) clinical care. Review and data extraction were performed in accordance with PRISMA-Scr guidelines.ResultsOf 584 unique publications detected, 31 met the inclusion criteria. The majority focused on outcome prediction post HTx (n = 13) and post durable MCS (n = 7), as well as post HTx and MCS management (n = 7, n = 3, respectively). One study addressed temporary mechanical circulatory support. Most studies advocated for rapid integration of AI into clinical practice, acknowledging potential improvements in management guidance and reliability of outcomes prediction. There was a notable paucity of external data validation and integration of multiple data modalities.ConclusionOur review showed mounting innovation in AI application in management of MCS and HTx, with the largest evidence showing improved mortality outcome prediction.

show abstract

“…Another noteworthy application of AI involves enhancing ongoing psychological interventions for real-time emotional issues, leading to enhanced treatment outcomes [ 40 ]. Additionally, AI has been instrumental in the development of efficient tools for personalized medicine, including predictive models that forecast illness, treatment responses, and preventive measures [ 36 , 41 ]. While these case studies underscore the considerable potential of AI in healthcare, further research is imperative to comprehensively understand the broader impact of AI-driven treatment interventions on patient outcomes.…”

Section: Reviewmentioning

confidence: 99%

Navigating the Future: A Comprehensive Review of Artificial Intelligence Applications in Gastrointestinal Cancer

Mukherjee,

Vagha,

Gadkari

2024

Cureus

View full text Add to dashboard Cite

This comprehensive review explores the transformative role of artificial intelligence (AI) in the realm of gastrointestinal cancer. Gastrointestinal cancers present unique challenges, necessitating precise diagnostic tools and personalized treatment strategies. Leveraging AI, particularly machine learning and deep learning algorithms, has demonstrated remarkable potential in revolutionizing early detection, treatment planning, prognosis, and drug development. The analysis of current research and technological advancements underscores the capacity of AI to unravel intricate patterns within extensive datasets, providing actionable insights that enhance diagnostic accuracy and treatment efficacy. The transformative impact of AI on the landscape of gastrointestinal cancer is emphasized, signaling a paradigm shift towards more precise and targeted cancer care. The conclusion emphasizes the need for sustained research efforts and collaborative initiatives among AI researchers, healthcare professionals, and policymakers. By fostering interdisciplinary collaboration, we can navigate the evolving field of gastrointestinal cancer care, embracing the potential of AI to improve patient outcomes and contribute to a more effective and personalized approach to cancer management.

show abstract

Artificial Intelligence and Machine Learning Based Intervention in Medical Infrastructure: A Review and Future Trends

Cited by 38 publications

References 109 publications

User Perceptions of ROTEM-Guided Haemostatic Resuscitation: A Mixed Qualitative–Quantitative Study

User Perceptions of ROTEM-Guided Haemostatic Resuscitation: A Mixed Qualitative–Quantitative Study

Artificial intelligence guidance of advanced heart failure therapies: A systematic scoping review

Navigating the Future: A Comprehensive Review of Artificial Intelligence Applications in Gastrointestinal Cancer

Contact Info

Product

Resources

About