Early detection of cancer
Survival improves when cancer is detected early. However,~ 50% of cancers are at an
advanced stage when diagnosed. Early detection of cancer or precancerous change allows …
advanced stage when diagnosed. Early detection of cancer or precancerous change allows …
[HTML][HTML] Bias in artificial intelligence algorithms and recommendations for mitigation
LH Nazer, R Zatarah, S Waldrip, JXC Ke… - PLOS Digital …, 2023 - journals.plos.org
The adoption of artificial intelligence (AI) algorithms is rapidly increasing in healthcare. Such
algorithms may be shaped by various factors such as social determinants of health that can …
algorithms may be shaped by various factors such as social determinants of health that can …
Steps to avoid overuse and misuse of machine learning in clinical research
Steps to avoid overuse and misuse of machine learning in clinical research | Nature Medicine
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Critical appraisal of artificial intelligence-based prediction models for cardiovascular disease
The medical field has seen a rapid increase in the development of artificial intelligence (AI)-
based prediction models. With the introduction of such AI-based prediction model tools and …
based prediction models. With the introduction of such AI-based prediction model tools and …
[HTML][HTML] Missing data is poorly handled and reported in prediction model studies using machine learning: a literature review
SWJ Nijman, AM Leeuwenberg, I Beekers… - Journal of clinical …, 2022 - Elsevier
Objectives Missing data is a common problem during the development, evaluation, and
implementation of prediction models. Although machine learning (ML) methods are often …
implementation of prediction models. Although machine learning (ML) methods are often …
Leveraging electronic health records for data science: common pitfalls and how to avoid them
Analysis of electronic health records (EHRs) is an increasingly common approach for
studying real-world patient data. Use of routinely collected data offers several advantages …
studying real-world patient data. Use of routinely collected data offers several advantages …
Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review
Background Describe and evaluate the methodological conduct of prognostic prediction
models developed using machine learning methods in oncology. Methods We conducted a …
models developed using machine learning methods in oncology. Methods We conducted a …
TRIPOD+ AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual
Prognosis Or Diagnosis) statement was published in 2015 to provide the minimum reporting …
Prognosis Or Diagnosis) statement was published in 2015 to provide the minimum reporting …
[HTML][HTML] Methodological and quality flaws in the use of artificial intelligence in mental health research: systematic review
R Tornero-Costa, A Martinez-Millana… - JMIR Mental …, 2023 - mental.jmir.org
Background: Artificial intelligence (AI) is giving rise to a revolution in medicine and health
care. Mental health conditions are highly prevalent in many countries, and the COVID-19 …
care. Mental health conditions are highly prevalent in many countries, and the COVID-19 …
Clinical AI tools must convey predictive uncertainty for each individual patient
Clinical AI tools must convey predictive uncertainty for each individual patient | Nature Medicine
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