Medical students need artificial intelligence and machine learning training

Pucchio, Aidan; Eisenhauer, Elizabeth; Moraes, Fábio Ynoe de

doi:10.1038/s41587-021-00846-2

Cited by 41 publications

(33 citation statements)

References 7 publications

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“…Hence, the clinical validation will become a combination of the biomarker and automated scoring method providing a combined computational biomarker, and not just a digital tool add-on after years of manually scoring the biomarker. With the current pace of advancement in ML for healthcare, it will also become instrumental that existing clinicians and future generations of physicians obtain formal training in computational approaches so they can better assess the clinical needs, advice on how it is best integrated into their workflow, and perform the critical appraisal of the performance of ML-based systems [ 58 ]. All this to ensure the added value in day-to-day clinical decision making.…”

Section: Discussionmentioning

confidence: 99%

Automated Quantification of sTIL Density with H&E-Based Digital Image Analysis Has Prognostic Potential in Triple-Negative Breast Cancers

Thagaard

Stovgaard

Vognsen

et al. 2021

Cancers

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Triple-negative breast cancer (TNBC) is an aggressive and difficult-to-treat cancer type that represents approximately 15% of all breast cancers. Recently, stromal tumor-infiltrating lymphocytes (sTIL) resurfaced as a strong prognostic biomarker for overall survival (OS) for TNBC patients. Manual assessment has innate limitations that hinder clinical adoption, and the International Immuno-Oncology Biomarker Working Group (TIL-WG) has therefore envisioned that computational assessment of sTIL could overcome these limitations and recommended that any algorithm should follow the manual guidelines where appropriate. However, no existing studies capture all the concepts of the guideline or have shown the same prognostic evidence as manual assessment. In this study, we present a fully automated digital image analysis pipeline and demonstrate that our hematoxylin and eosin (H&E)-based pipeline can provide a quantitative and interpretable score that correlates with the manual pathologist-derived sTIL status, and importantly, can stratify a retrospective cohort into two significant distinct prognostic groups. We found our score to be prognostic for OS (HR: 0.81 CI: 0.72–0.92 p = 0.001) independent of age, tumor size, nodal status, and tumor type in statistical modeling. While prior studies have followed fragments of the TIL-WG guideline, our approach is the first to follow all complex aspects, where appropriate, supporting the TIL-WG vision of computational assessment of sTIL in the future clinical setting.

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Section: Discussionmentioning

confidence: 99%

Automated Quantification of sTIL Density with H&E-Based Digital Image Analysis Has Prognostic Potential in Triple-Negative Breast Cancers

Thagaard

Stovgaard

Vognsen

et al. 2021

Cancers

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“…Further development, refinement and application of AI to real-world data is warranted. Such goal can only be achieved with a trained workforce which underscores the urgency of the education of the next generation of physician-scientists in AI and oncology [ 40 ].…”

Section: Artificial Intelligence For Cancer Imagingmentioning

confidence: 99%

An Overview of Artificial Intelligence in Oncology

et al. 2022

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Cancer is associated with significant morbimortality globally. Advances in screening, diagnosis, management and survivorship were substantial in the last decades, however, challenges in providing personalized and data-oriented care remain. Artificial intelligence (AI), a branch of computer science used for predictions and automation, has emerged as potential solution to improve the healthcare journey and to promote precision in healthcare. AI applications in oncology include, but are not limited to, optimization of cancer research, improvement of clinical practice (eg., prediction of the association of multiple parameters and outcomes – prognosis and response) and better understanding of tumor molecular biology. In this review, we examine the current state of AI in oncology, including fundamentals, current applications, limitations and future perspectives.

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“…He introduced a test called the "Turing Test". This test evaluates a machine based on intelligence [37,38]. Today, there are various definitions of machine learning.…”

Section: Machine Learningmentioning

confidence: 99%

“…They create the ML-based models to perform their actions independently based on pre-defined goals [11]. For example, one of the future applications in the healthcare field is to build a hospital without physicians [37,38]. As a result, we must prepare ourselves for a robotic future based on machine learning and artificial intelligence.…”

mentioning

confidence: 99%

Machine Learning (ML) in Medicine: Review, Applications, and Challenges

Rahmani

Yousefpoor

et al. 2021

Mathematics

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Today, artificial intelligence (AI) and machine learning (ML) have dramatically advanced in various industries, especially medicine. AI describes computational programs that mimic and simulate human intelligence, for example, a person’s behavior in solving problems or his ability for learning. Furthermore, ML is a subset of artificial intelligence. It extracts patterns from raw data automatically. The purpose of this paper is to help researchers gain a proper understanding of machine learning and its applications in healthcare. In this paper, we first present a classification of machine learning-based schemes in healthcare. According to our proposed taxonomy, machine learning-based schemes in healthcare are categorized based on data pre-processing methods (data cleaning methods, data reduction methods), learning methods (unsupervised learning, supervised learning, semi-supervised learning, and reinforcement learning), evaluation methods (simulation-based evaluation and practical implementation-based evaluation in real environment) and applications (diagnosis, treatment). According to our proposed classification, we review some studies presented in machine learning applications for healthcare. We believe that this review paper helps researchers to familiarize themselves with the newest research on ML applications in medicine, recognize their challenges and limitations in this area, and identify future research directions.

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Medical students need artificial intelligence and machine learning training

Cited by 41 publications

References 7 publications

Automated Quantification of sTIL Density with H&E-Based Digital Image Analysis Has Prognostic Potential in Triple-Negative Breast Cancers

Automated Quantification of sTIL Density with H&E-Based Digital Image Analysis Has Prognostic Potential in Triple-Negative Breast Cancers

An Overview of Artificial Intelligence in Oncology

Machine Learning (ML) in Medicine: Review, Applications, and Challenges

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