The Added Effect of Artificial Intelligence on Physicians’ Performance in Detecting Thoracic Pathologies on CT and Chest X-ray: A Systematic Review

Li, Dana; Pehrson, Lea Marie; Lauridsen, Carsten Ammitzbøl; Tøttrup, Lea; Fraccaro, Marco; Elliott, Desmond; Zając, Hubert Dariusz; Darkner, Sune; Carlsen, Jonathan Frederik; Nielsen, Michael Bachmann

doi:10.3390/diagnostics11122206

Cited by 21 publications

(13 citation statements)

References 59 publications

(192 reference statements)

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“…Several studies have compared diagnostic performance when reviewing images with and without AI support (see 12 for a systematic review in CT and chest X-rays). However, in a clinical setting, physicians often receive advice from colleagues, or are asked to re-review cases from someone else to give a second opinion 13 , 14 .…”

Section: Introductionmentioning

confidence: 99%

Non-task expert physicians benefit from correct explainable AI advice when reviewing X-rays

Gaube

Suresh

Raue

et al. 2023

Sci Rep

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Artificial intelligence (AI)-generated clinical advice is becoming more prevalent in healthcare. However, the impact of AI-generated advice on physicians’ decision-making is underexplored. In this study, physicians received X-rays with correct diagnostic advice and were asked to make a diagnosis, rate the advice’s quality, and judge their own confidence. We manipulated whether the advice came with or without a visual annotation on the X-rays, and whether it was labeled as coming from an AI or a human radiologist. Overall, receiving annotated advice from an AI resulted in the highest diagnostic accuracy. Physicians rated the quality of AI advice higher than human advice. We did not find a strong effect of either manipulation on participants’ confidence. The magnitude of the effects varied between task experts and non-task experts, with the latter benefiting considerably from correct explainable AI advice. These findings raise important considerations for the deployment of diagnostic advice in healthcare.

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Section: Introductionmentioning

confidence: 99%

Non-task expert physicians benefit from correct explainable AI advice when reviewing X-rays

Gaube

Suresh

Raue

et al. 2023

Sci Rep

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“…Medical students perceive radiologists to have “little or no patient interaction” and think that radiology “is best suited for introverted people” [ 30 ]. AI can automate repetitive tasks currently performed by radiologists, such as screenings and lesion or organ measurements [ 9 , 14 , 31 ]. The hope is that this automation will create time for radiologists to meet and speak to patients face-to-face, to discuss the need for imaging, and to review the results and consult on the next steps in care.…”

Section: The Patient-facing Radiologistmentioning

confidence: 99%

Changes in Radiology Due to Artificial Intelligence That Can Attract Medical Students to the Specialty

Liu¹,

Abu-Shaban²,

Halabi³

et al. 2023

JMIR Med Educ

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The role of artificial intelligence (AI) in radiology has grown exponentially in the recent years. One of the primary worries by medical students is that AI will cause the roles of a radiologist to become automated and thus obsolete. Therefore, there is a greater hesitancy by medical students to choose radiology as a specialty. However, it is in this time of change that the specialty needs new thinkers and leaders. In this succinct viewpoint, 2 medical students involved in AI and 2 radiologists specializing in AI or clinical informatics posit that not only are these fears false, but the field of radiology will be transformed in such a way due to AI that there will be novel reasons to choose radiology. These new factors include greater impact on patient care, new space for innovation, interdisciplinary collaboration, increased patient contact, becoming master diagnosticians, and greater opportunity for global health initiatives, among others. Finally, since medical students view mentorship as a critical resource when deciding their career path, medical educators must also be cognizant of these changes and not give much credence to the prevalent fearmongering. As the field and practice of radiology continue to undergo significant change due to AI, it is urgent and necessary for the conversation to expand from expert to expert to expert to student. Medical students should be encouraged to choose radiology specifically because of the changes brought on by AI rather than being deterred by it.

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“…Computational tools based on mathematical models for medical image analysis have been the earliest to be applied to assist the grading of a disease 7–9 . Lately, we have seen a surge of proof‐of‐concept uses of artificial intelligence (AI) in medicine, due to both the increase in data availability and the advances in AI algorithms, which allow their use under real‐life conditions to assist human decision makers 10 .…”

Section: Introductionmentioning

confidence: 99%

A decision support system based on artificial intelligence and systems biology for the simulation of pancreatic cancer patient status

Matos-Filipe

García-Illarramendi

Ramírez³

et al. 2023

CPT Pharmacom & Syst Pharma

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Oncology treatments require continuous individual adjustment based on the measurement of multiple clinical parameters. Prediction tools exploiting the patterns present in the clinical data could be used to assist decision making and ease the burden associated to the interpretation of all these parameters. The goal of this study was to predict the evolution of patients with pancreatic cancer at their next visit using information routinely recorded in health records, providing a decision-support system for clinicians. We selected hematological variables as the visit's clinical outcomes, under the assumption that they can be predictive of the evolution of the patient.Multivariate models based on regression trees were generated to predict next-visit values for each of the clinical outcomes selected, based on the longitudinal clinical data as well as on molecular data sets streaming from in silico simulations of individual patient status at each visit. The models predict, with a mean prediction score (balanced accuracy) of 0.79, the evolution trends of eosinophils, leukocytes, monocytes, and platelets. Time span between visits and neutropenia were among the most common factors contributing to the predicted evolution. The inclusion of molecular variables from the systems-biology in silico simulations provided a molecular background for the observed variations in the selected outcome variables, mostly in relation to the regulation of hematopoiesis. In spite of its limitations, this study serves as a proof of concept for the application of next-visit prediction tools in real-world settings, even when available data sets are small. Study Highlights WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC?Artificial intelligence (AI) and related technologies are increasingly prevalent in health care, as they can manage heterogeneous sources of data, identifying underlaying patterns, and predicting complex outcomes.

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The Added Effect of Artificial Intelligence on Physicians’ Performance in Detecting Thoracic Pathologies on CT and Chest X-ray: A Systematic Review

Cited by 21 publications

References 59 publications

Non-task expert physicians benefit from correct explainable AI advice when reviewing X-rays

Non-task expert physicians benefit from correct explainable AI advice when reviewing X-rays

Changes in Radiology Due to Artificial Intelligence That Can Attract Medical Students to the Specialty

A decision support system based on artificial intelligence and systems biology for the simulation of pancreatic cancer patient status

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