2.Machine learning in medicine: what clinicians should know.
Jordan Zheng TING SIM ; Qi Wei FONG ; Weimin HUANG ; Cher Heng TAN
Singapore medical journal 2023;64(2):91-97
With the advent of artificial intelligence (AI), machines are increasingly being used to complete complicated tasks, yielding remarkable results. Machine learning (ML) is the most relevant subset of AI in medicine, which will soon become an integral part of our everyday practice. Therefore, physicians should acquaint themselves with ML and AI, and their role as an enabler rather than a competitor. Herein, we introduce basic concepts and terms used in AI and ML, and aim to demystify commonly used AI/ML algorithms such as learning methods including neural networks/deep learning, decision tree and application domain in computer vision and natural language processing through specific examples. We discuss how machines are already being used to augment the physician's decision-making process, and postulate the potential impact of ML on medical practice and medical research based on its current capabilities and known limitations. Moreover, we discuss the feasibility of full machine autonomy in medicine.
Humans
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Artificial Intelligence
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Machine Learning
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Algorithms
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Neural Networks, Computer
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Medicine
3.Impact of fatty liver on long-term outcomes in chronic hepatitis B: a systematic review and matched analysis of individual patient data meta-analysis
Yu Jun WONG ; Vy H. NGUYEN ; Hwai-I YANG ; Jie LI ; Michael Huan LE ; Wan-Jung WU ; Nicole Xinrong HAN ; Khi Yung FONG ; Elizebeth CHEN ; Connie WONG ; Fajuan RUI ; Xiaoming XU ; Qi XUE ; Xin Yu HU ; Wei Qiang LEOW ; George Boon-Bee GOH ; Ramsey CHEUNG ; Grace WONG ; Vincent Wai-Sun WONG ; Ming-Whei YU ; Mindie H. NGUYEN
Clinical and Molecular Hepatology 2023;29(3):705-720
Background/Aims:
Chronic hepatitis B (CHB) and fatty liver (FL) often co-exist, but natural history data of this dual condition (CHB-FL) are sparse. Via a systematic review, conventional meta-analysis (MA) and individual patient-level data MA (IPDMA), we compared liver-related outcomes and mortality between CHB-FL and CHB-no FL patients.
Methods:
We searched 4 databases from inception to December 2021 and pooled study-level estimates using a random- effects model for conventional MA. For IPDMA, we evaluated outcomes after balancing the two study groups with inverse probability treatment weighting (IPTW) on age, sex, cirrhosis, diabetes, ALT, HBeAg, HBV DNA, and antiviral treatment.
Results:
We screened 2,157 articles and included 19 eligible studies (17,955 patients: 11,908 CHB-no FL; 6,047 CHB-FL) in conventional MA, which found severe heterogeneity (I2=88–95%) and no significant differences in HCC, cirrhosis, mortality, or HBsAg seroclearance incidence (P=0.27–0.93). IPDMA included 13,262 patients: 8,625 CHB-no FL and 4,637 CHB-FL patients who differed in several characteristics. The IPTW cohort included 6,955 CHB-no FL and 3,346 CHB-FL well-matched patients. CHB-FL patients (vs. CHB-no FL) had significantly lower HCC, cirrhosis, mortality and higher HBsAg seroclearance incidence (all p≤0.002), with consistent results in subgroups. CHB-FL diagnosed by liver biopsy had a higher 10-year cumulative HCC incidence than CHB-FL diagnosed with non-invasive methods (63.6% vs. 4.3%, p<0.0001).
Conclusions
IPDMA data with well-matched CHB patient groups showed that FL (vs. no FL) was associated with significantly lower HCC, cirrhosis, and mortality risk and higher HBsAg seroclearance probability.