Revolutionizing healthcare: the role of artificial intelligence in clinical practice

BMC Medical Education, Journal Year: 2023, Volume and Issue: 23(1)

Published: Sept. 22, 2023

Abstract Introduction Healthcare systems are complex and challenging for all stakeholders, but artificial intelligence (AI) has transformed various fields, including healthcare, with the potential to improve patient care quality of life. Rapid AI advancements can revolutionize healthcare by integrating it into clinical practice. Reporting AI’s role in practice is crucial successful implementation equipping providers essential knowledge tools. Research Significance This review article provides a comprehensive up-to-date overview current state practice, its applications disease diagnosis, treatment recommendations, engagement. It also discusses associated challenges, covering ethical legal considerations need human expertise. By doing so, enhances understanding significance supports organizations effectively adopting technologies. Materials Methods The investigation analyzed use system relevant indexed literature, such as PubMed/Medline, Scopus, EMBASE, no time constraints limited articles published English. focused question explores impact applying settings outcomes this application. Results Integrating holds excellent improving selection, laboratory testing. tools leverage large datasets identify patterns surpass performance several aspects. offers increased accuracy, reduced costs, savings while minimizing errors. personalized medicine, optimize medication dosages, enhance population health management, establish guidelines, provide virtual assistants, support mental care, education, influence patient-physician trust. Conclusion be used diagnose diseases, develop plans, assist clinicians decision-making. Rather than simply automating tasks, about developing technologies that across settings. However, challenges related data privacy, bias, expertise must addressed responsible effective healthcare.

Language: Английский

---

Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies

BMJ, Journal Year: 2020, Volume and Issue: unknown, P. m689 - m689

Published: March 25, 2020

Abstract Objective To systematically examine the design, reporting standards, risk of bias, and claims studies comparing performance diagnostic deep learning algorithms for medical imaging with that expert clinicians. Design Systematic review. Data sources Medline, Embase, Cochrane Central Register Controlled Trials, World Health Organization trial registry from 2010 to June 2019. Eligibility criteria selecting Randomised registrations non-randomised a algorithm in contemporary group one or more Medical has seen growing interest research. The main distinguishing feature convolutional neural networks (CNNs) is when CNNs are fed raw data, they develop their own representations needed pattern recognition. learns itself features an image important classification rather than being told by humans which use. selected aimed use predicting absolute existing disease into groups (eg, non-disease). For example, chest radiographs tagged label such as pneumothorax no CNN pixel patterns suggest pneumothorax. Review methods Adherence standards was assessed using CONSORT (consolidated trials) randomised TRIPOD (transparent multivariable prediction model individual prognosis diagnosis) studies. Risk bias tool PROBAST (prediction assessment tool) Results Only 10 records were found clinical trials, two have been published (with low except lack blinding, high adherence standards) eight ongoing. Of 81 trials identified, only nine prospective just six tested real world setting. median number experts comparator four (interquartile range 2-9). Full access all datasets code severely limited (unavailable 95% 93% studies, respectively). overall 58 suboptimal (<50% 12 29 items). 61 stated abstract artificial intelligence at least comparable (or better than) 31 (38%) further required. Conclusions Few exist imaging. Most not prospective, deviate standards. availability lacking most human often small. Future should diminish enhance relevance, improve transparency, appropriately temper conclusions. Study registration PROSPERO CRD42019123605.

Language: Английский

---

Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis

npj Digital Medicine, Journal Year: 2021, Volume and Issue: 4(1)

Published: April 7, 2021

Deep learning (DL) has the potential to transform medical diagnostics. However, diagnostic accuracy of DL is uncertain. Our aim was evaluate algorithms identify pathology in imaging. Searches were conducted Medline and EMBASE up January 2020. We identified 11,921 studies, which 503 included systematic review. Eighty-two studies ophthalmology, 82 breast disease 115 respiratory for meta-analysis. Two hundred twenty-four other specialities qualitative Peer-reviewed that reported on using imaging included. Primary outcomes measures accuracy, study design reporting standards literature. Estimates pooled random-effects In AUC's ranged between 0.933 1 diagnosing diabetic retinopathy, age-related macular degeneration glaucoma retinal fundus photographs optical coherence tomography. imaging, 0.864 0.937 lung nodules or cancer chest X-ray CT scan. For 0.868 0.909 mammogram, ultrasound, MRI digital tomosynthesis. Heterogeneity high extensive variation methodology, terminology outcome noted. This can lead an overestimation There immediate need development artificial intelligence-specific EQUATOR guidelines, particularly STARD, order provide guidance around key issues this field.

Language: Английский

---

Artificial intelligence-enhanced electrocardiography in cardiovascular disease management

Nature Reviews Cardiology, Journal Year: 2021, Volume and Issue: 18(7), P. 465 - 478

Published: Feb. 1, 2021

Language: Английский

---

Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review

npj Digital Medicine, Journal Year: 2022, Volume and Issue: 5(1)

Published: Jan. 10, 2022

While the opportunities of ML and AI in healthcare are promising, growth complex data-driven prediction models requires careful quality applicability assessment before they applied disseminated daily practice. This scoping review aimed to identify actionable guidance for those closely involved AI-based model (AIPM) development, evaluation implementation including software engineers, data scientists, professionals potential gaps this guidance. We performed a relevant literature providing or criteria regarding evaluation, AIPMs using comprehensive multi-stage screening strategy. PubMed, Web Science, ACM Digital Library were searched, experts consulted. Topics extracted from identified summarized across six phases at core review: (1) preparation, (2) AIPM (3) validation, (4) (5) impact assessment, (6) into From 2683 unique hits, 72 documents identified. Substantial was found development validation (phases 1-3), while later clearly have received less attention (software implementation) scientific literature. The cycle provide framework responsible introduction healthcare. Additional domain technology specific research may be necessary more practical experience with implementing is needed support further

Language: Английский

---

Machine learning based early warning system enables accurate mortality risk prediction for COVID-19

Nature Communications, Journal Year: 2020, Volume and Issue: 11(1)

Published: Oct. 6, 2020

Abstract Soaring cases of coronavirus disease (COVID-19) are pummeling the global health system. Overwhelmed facilities have endeavored to mitigate pandemic, but mortality COVID-19 continues increase. Here, we present a risk prediction model for (MRPMC) that uses patients’ clinical data on admission stratify patients by risk, which enables physiological deterioration and death up 20 days in advance. This ensemble is built using four machine learning methods including Logistic Regression, Support Vector Machine, Gradient Boosted Decision Tree, Neural Network. We validate MRPMC an internal validation cohort two external cohorts, where it achieves AUC 0.9621 (95% CI: 0.9464–0.9778), 0.9760 (0.9613–0.9906), 0.9246 (0.8763–0.9729), respectively. expeditious accurate stratification with COVID-19, potentially facilitates more responsive systems conducive high patients.

Language: Английский

---

Designing deep learning studies in cancer diagnostics

Nature reviews. Cancer, Journal Year: 2021, Volume and Issue: 21(3), P. 199 - 211

Published: Jan. 29, 2021

Language: Английский

---

Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

Nature Medicine, Journal Year: 2022, Volume and Issue: 28(5), P. 924 - 933

Published: May 1, 2022

Language: Английский

---

Artificial intelligence in dental research: Checklist for authors, reviewers, readers

Journal of Dentistry, Journal Year: 2021, Volume and Issue: 107, P. 103610 - 103610

Published: Feb. 22, 2021

Language: Английский

---

The need to separate the wheat from the chaff in medical informatics

International Journal of Medical Informatics, Journal Year: 2021, Volume and Issue: 153, P. 104510 - 104510

Published: June 2, 2021

Language: Английский

---