Artificial Intelligence (AI) for Fracture Diagnosis: An Overview of Current Products and Considerations for Clinical Adoption, From the <i>AJR</i> Special Series on AI Applications

Zech, John R.; Santomartino, Samantha M; Yi, Paul H.

doi:10.2214/ajr.22.27873

Cited by 19 publications

(10 citation statements)

References 56 publications

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“… Comparative analysis with other deep learning models demonstrated that the DCNN-LSTM fusion achieved higher accuracy and exhibited potential for medical applications as a reliable second option in wrist fracture diagnosis, emphasizing its utility in reducing missed diagnoses. Diagnostics [ 23 ] Zech, Santomartino, Yi Artificial Intelligence (AI) for Fracture Diagnosis: An Overview of Current Products and Considerations for Clinical Adoption, From the AJR Special Series on AI Applications. Review US Artificial intelligence (AI) and deep learning have demonstrated strong potential in accurately detecting fractures, enhancing radiologists’ performance in research settings.…”

Section: Resultsmentioning

confidence: 99%

“… 18 Another model predicted cardiac arrest and respiratory failure 1–6 hours in advance using only vital signs and history, surpassing traditional early warning scores. 35 Considerations span collecting diverse and standardized validation data 23 to enhancing localization to avoid false negatives 19 and addressing ethical concerns related to black-box recommendations and over-reliance. 17 Partnership with emergency and trauma radiologists is critical for translating technical potential into clinical practice improvements.…”

Section: Resultsmentioning

confidence: 99%

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Artificial Intelligence in Emergency Trauma Care: A Preliminary Scoping Review

Ventura,

Denton,

David

2024

MDER

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This study aimed to analyze the use of generative artificial intelligence in the emergency trauma care setting through a brief scoping review of literature published between 2014 and 2024. An exploration of the NCBI repository was performed using a search string of selected keywords that returned N=87 results; articles that met the inclusion criteria (n=28) were reviewed and analyzed. Heterogeneity sources were explored and identified by a significance threshold of P < 0.10 or an I 2 value exceeding 50%. If applicable, articles were categorized within three primary domains: triage, diagnostics, or treatment. Findings suggest that CNNs demonstrate strong diagnostic performance for diverse traumatic injuries, but generalized integration requires expanded prospective multi-center validation. Injury scoring models currently experience calibration gaps in mortality quantification and lesion localization that can undermine clinical utility by permitting false negatives. Triage predictive models now confront transparency, explainability, and healthcare ecosystem integration barriers limiting real-world translation. The most significant literature gap centers on treatment-oriented generative AI applications that provide real-time guidance for urgent trauma interventions rather than just analytical support.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence in Emergency Trauma Care: A Preliminary Scoping Review

Ventura,

Denton,

David

2024

MDER

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“…There have been a number of studies with different products on fracture detection using AI on plain radiographs [ 28 , 29 , 30 , 31 , 32 , 33 , 34 ]. Fractures are the leading type of missed diagnosis [ 35 ].…”

Section: Methodsmentioning

confidence: 99%

Artificial Intelligence in Orthopedic Radiography Analysis: A Narrative Review

et al. 2022

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Artificial intelligence (AI) in medicine is a rapidly growing field. In orthopedics, the clinical implementations of AI have not yet reached their full potential. Deep learning algorithms have shown promising results in computed radiographs for fracture detection, classification of OA, bone age, as well as automated measurements of the lower extremities. Studies investigating the performance of AI compared to trained human readers often show equal or better results, although human validation is indispensable at the current standards. The objective of this narrative review is to give an overview of AI in medicine and summarize the current applications of AI in orthopedic radiography imaging. Due to the different AI software and study design, it is difficult to find a clear structure in this field. To produce more homogeneous studies, open-source access to AI software codes and a consensus on study design should be aimed for.

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“…Several AI algorithms have demonstrated promising performance in real-world clinical settings, particularly in applications like medical imaging and diagnostics [104][105][106][107][108][109]. They show potential for enhancing healthcare outcomes by rapidly analyzing extensive medical data for early disease detection.…”

Section: Diagnosis and Classificationmentioning

confidence: 99%

From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer

Tripathi,

Tabari,

Mansur

et al. 2024

Diagnostics

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Pancreatic cancer is a highly aggressive and difficult-to-detect cancer with a poor prognosis. Late diagnosis is common due to a lack of early symptoms, specific markers, and the challenging location of the pancreas. Imaging technologies have improved diagnosis, but there is still room for improvement in standardizing guidelines. Biopsies and histopathological analysis are challenging due to tumor heterogeneity. Artificial Intelligence (AI) revolutionizes healthcare by improving diagnosis, treatment, and patient care. AI algorithms can analyze medical images with precision, aiding in early disease detection. AI also plays a role in personalized medicine by analyzing patient data to tailor treatment plans. It streamlines administrative tasks, such as medical coding and documentation, and provides patient assistance through AI chatbots. However, challenges include data privacy, security, and ethical considerations. This review article focuses on the potential of AI in transforming pancreatic cancer care, offering improved diagnostics, personalized treatments, and operational efficiency, leading to better patient outcomes.

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Artificial Intelligence (AI) for Fracture Diagnosis: An Overview of Current Products and Considerations for Clinical Adoption, From the AJR Special Series on AI Applications

Cited by 19 publications

References 56 publications

Artificial Intelligence in Emergency Trauma Care: A Preliminary Scoping Review

Artificial Intelligence in Emergency Trauma Care: A Preliminary Scoping Review

Artificial Intelligence in Orthopedic Radiography Analysis: A Narrative Review

From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer

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