Artificial intelligence in medicine: current trends and future possibilities.

By V. Buch, Irfan Ahmed, M. Maruthappu. 2018

310 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Epidemiology, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Bo Han
2. Gang Niu
3. Masashi Sugiyama
4. Li-zhen Cui
5. Zeeshan Ahmed
6. Shreshth Tuli
7. Nipam Basumatary
8. Xilie Xu

In Epidemiology

1. Francesco Amenta
2. Masashi Sugiyama

In Radiology, Nuclear Medicine and Imaging

1. Zeeshan Ahmed
2. Mohan Kankanhalli
3. Khalid Mohamed
4. Xinqi Dong
5. Saman Zeeshan
6. Masashi Sugiyama

Citations in the corpus

• HealthFog: An Ensemble Deep Learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in Integrated IoT and Fog Computing Environments (2019, background): “This is difficult to do practically due to shortage of doctors as most countries still do not trust computer systems to be able to detect heart problems with the required accuracy and explain-ability [51, 52].”
• Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine (2020, background): “AI has been in business for almost over 50 years, and its applications are heavily demanded in numerous fields of life, science, technology and medicine (68,69).”
• Physician Confidence in Artificial Intelligence: An Online Mobile Survey (2018, background): “However, AI machine learning saves time and is less likely to make errors [37].”
• Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis (2020, background): “In 2016, AI projects coupled with medicine drew in more speculation from the global economy than other projects [4].”
• Challenges to implementing artificial intelligence in healthcare: a qualitative interview study with healthcare leaders in Sweden (2022, background): “The use of artificial intelligence (AI) in healthcare can potentially enable solutions to some of the challenges faced by healthcare systems around the world [1–3].”
• Attacks Which Do Not Kill Training Make Adversarial Learning Stronger (2020, methodology): “Safety-critical nature of some areas such as medicine (Buch et al., 2018) and automatic driving (Litman, 2017), necesEqual contribution †Preliminary work was done during an internship at RIKEN AIP.”
• Revolutionizing healthcare: the role of artificial intelligence in clinical practice (2023, background): “AI virtual healthcare assistance With continuously increasing demands of health care services and limited resources worldwide, finding solutions to overcome these challenges is essential [82].”