Artificial Intelligence and Healthcare

A state-of-the-art newsfeed on Artificial Intelligence-based medical research

Artificial Intelligence transforms the future of health care

Noorbakhsh-Sabet N, Zand R, (...), Abedi V.

Am J Med. 2019. DOI: 10.1016/j.amjmed.2019.01.017.

AI transforms healthcare
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Machine Learning can reveal previously unknown associations, generate novel hypotheses and drive researchers and resources towards more productive channels of enquiry.

Healthcare providers should be prepared for the age of AI and embrace the added capabilities that will lead to more efficient and effective care.

  1. Early diagnosis can now be achieved for many conditions by improving the extraction of clinical insight and feeding such insight into a well-trained and validated system.
  2. Integrating machine learning-based modeling can facilitate the detection of potential complications, improve healthcare resource utilization and outcomes at a personalized level.
  3. Artificial Intelligence analytics can be used in chronic disease management characterized by multi-organ involvement, acute variable events and the progression of long term illness.

Human-machine partnership with Artificial Intelligence for chest radiograph diagnosis

Patel BN, Rosenberg L, (...) Lungren M.

NPJ Digit Med. 2019. DOI: 10.1038/s41746-019-0189-7.

Chest radiograph diagnosis
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An investigation of a new AI technology designed to amplify the diagnostic accuracy of networked human groups by forming real-time systems modeled on biological swarms applied to chest radiographs for the diagnosis of pneumonia.

  1. Both the swarm-based technology and deep-learning technology achieved superior diagnostic accuracy of pneumonia than the human experts alone.
  2. When used in combination, the swarm-based technology and deep-learning technology outperformed either method alone.
  3. These findings have broad implications for the growth in clinical AI deployment and implementation strategies in future practice.

Ethical machines: the human-centric use of Artificial Intelligence

Lepri B, Oliver N, Pentland A.

iScience. 2021. DOI: 10.1016/j.isci.2021.102249.

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This publication focuses the attention on existing risks related to the use of algorithmic decision-making processes, including computational violations of privacy, power and information assymetry, lack of transparency and accountability, and discrimination and bias. 

  1. Only with multidisciplinary approaches we can achieve algorithmic fairness.
  2. It’s very important to have an ethical and human-centric use of Artificial Intelligence.
  3. We must help people to understand AI-driven decision making processes.

Artificial Intelligence-enabled rapid diagnosis of patients with COVID-19

Mei X, Lee HC, (...), Yang Y.

Nat Med. 2020. DOI: 10.1038/s41591-020-0931-3.

Chest CT images of COVID19 patients
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An AI system can help to rapidly diagnose COVID-19 patients.

  1. Rapid detection of patients with COVID-19 is vital because an initial false negative could delay treatment and increase risk of viral transmission to others.
  2. With a higher AUC, the joint model integrating CT images and associated clinical information outperformed the model trained on CT images only.
  3. The AI system could be implemented as a rapid diagnostic tool to flag patients with suspected COVID-19 infection, when CT images and/or clinical information are available, and radiologists could review these suspected cases identified by AI with a higher priority.