Applications of artificial intelligence in the thorax: a narrative review focusing on thoracic radiology

Background: Suicide poses a significant health burden worldwide. In many cases, people at risk of suicide do not engage with their doctor or community due to concerns about stigmatisation and forced medical treatment; worse still, people with mental illness (who form a majority of people who die from suicide) may have poor insight into their mental state, and not self-identify as being at risk. These issues are exacerbated by the fact that doctors have difficulty in identifying those at risk of suicide when they do present to medical services. Advances in artificial intelligence (AI) present opportunities for the development of novel tools for predicting suicide.Method: We searched Google Scholar and PubMed for articles relating to suicide prediction using artificial intelligence from 2017 onwards.Conclusions: This paper presents a qualitative narrative review of research focusing on two categories of suicide prediction tools: medical suicide prediction and social suicide prediction. Initial evidence is promising: AI-driven suicide prediction could improve our capacity to identify those at risk of suicide, and, potentially, save lives. Medical suicide prediction may be relatively uncontroversial when it pays respect to ethical and legal principles; however, further research is required to determine the validity of these tools in different contexts. Social suicide prediction offers an exciting opportunity to help identify suicide risk among those who do not engage with traditional health services. Yet, efforts by private companies such as Facebook to use online data for suicide prediction should be the subject of independent review and oversight to confirm safety, effectiveness and ethical permissibility.

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Rise of the Machines: The Inevitable Evolution of Medicine and Medical Laboratories Intertwining with Artificial Intelligence - A Narrative Review

10.20944/preprints202106.0295.v1 ◽

2021 ◽

Author(s):

Janne Cadamuro

Keyword(s):

Artificial Intelligence ◽

Laboratory Medicine ◽

Narrative Review ◽

Test Results ◽

Diagnostic Data ◽

Medical Laboratories ◽

Making Sense ◽

Medical Discipline ◽

The Masses ◽

Near Future

Laboratory medicine has evolved from a mainly manual profession, providing few selected test results to a highly automated and standardized medical discipline, generating millions of test results per year. As next inevitable evolutional step, artificial intelligence (AI) algorithms will need to assist us in structuring and making sense of the masses of diagnostic data collected today. Such systems will be able to connect clinical and diagnostic data and to provide valuable suggestions in diagnosis, prognosis or therapeutic options. They will merge the often so separated worlds of the laboratory and the clinics. When used correctly, it will be a tool, capable of freeing the physicians time so that he/she can refocus on the patient. In this narrative review I therefore aim to provide an overview of what AI is, what applications currently are available in healthcare and in laboratory medicine in particular. I will discuss the challenges and pitfalls of applying AI algorithms and I will elaborate on the question if healthcare workers will be replaced by such systems in the near future.

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Artificial intelligence in gastric cancer: a translational narrative review

Annals of Translational Medicine ◽

10.21037/atm-20-6337 ◽

2021 ◽

Vol 9 (3) ◽

pp. 269-269

Author(s):

Chaoran Yu ◽

Ernest Johann Helwig

Keyword(s):

Artificial Intelligence ◽

Gastric Cancer ◽

Narrative Review

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Artificial Intelligence and Robotics in Spine Surgery

Global Spine Journal ◽

10.1177/2192568220915718 ◽

2020 ◽

pp. 219256822091571 ◽

Cited By ~ 1

Author(s):

Jonathan J. Rasouli ◽

Jianning Shao ◽

Sean Neifert ◽

Wende N. Gibbs ◽

Ghaith Habboub ◽

...

Keyword(s):

Artificial Intelligence ◽

Spine Surgery ◽

Patient Selection ◽

Clinical Decision Making ◽

Predictive Analytics ◽

Narrative Review ◽

Robotic Assisted ◽

Spine Care ◽

And Robotics ◽

Preoperative Patient

Study Design: Narrative review. Objectives: Artificial intelligence (AI) and machine learning (ML) have emerged as disruptive technologies with the potential to drastically affect clinical decision making in spine surgery. AI can enhance the delivery of spine care in several arenas: (1) preoperative patient workup, patient selection, and outcome prediction; (2) quality and reproducibility of spine research; (3) perioperative surgical assistance and data tracking optimization; and (4) intraoperative surgical performance. The purpose of this narrative review is to concisely assemble, analyze, and discuss current trends and applications of AI and ML in conventional and robotic-assisted spine surgery. Methods: We conducted a comprehensive PubMed search of peer-reviewed articles that were published between 2006 and 2019 examining AI, ML, and robotics in spine surgery. Key findings were then compiled and summarized in this review. Results: The majority of the published AI literature in spine surgery has focused on predictive analytics and supervised image recognition for radiographic diagnosis. Several investigators have studied the use of AI/ML in the perioperative setting in small patient cohorts; pivotal trials are still pending. Conclusions: Artificial intelligence has tremendous potential in revolutionizing comprehensive spine care. Evidence-based, predictive analytics can help surgeons improve preoperative patient selection, surgical indications, and individualized postoperative care. Robotic-assisted surgery, while still in early stages of development, has the potential to reduce surgeon fatigue and improve technical precision.

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