Artificial Intelligence and Healthcare

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

High-performance medicine: the convergence of human and Artificial Intelligence

Topol, E.J.

Nat Med 25, 44-56. 2019. DOI: 10.1038/s41591-018-0300-7.

Process of AI in medicine
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The use of deep learning in medicine is beginning to have an impact upon clinicians, healthcare systems and patients. The journey has just begun.

  1. In the near future, almost every type of clinician, ranging from specialty doctor to paramedic, will be using AI technology, and in particular deep learning.
  2. Using electronic health record data, machine- and deep-learning algorithms are already predicting many important clinical parameters.
  3. The goal for patients is to develop deep-learning algorithms to support them to take their health care into their own hands.

Mapping ethico-legal principles for the use of Artificial Intelligence in gastroenterology

Stewart C, Wong SKY, Sung JJY.

J Gastroenterol Hepatol. 2021. DOI: 10.1111/jgh.15521.

Ethical decision box
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This article outlines a number of core ethical and legal principles to consider when examining the use of AI in gastroenterology and laying the foundation for an ethical framework that could be employed in the resolution of difficult choices concerning the use of AI in clinical practice. 

  1. AI systems must function in a robust, secure, and safe way throughout their life cycles, and potential risks should be continually assessed and managed.
  2. Respect for people is the primary value that underpins many of the values. Respect for people is illustrated by a moral attitude of deference and consideration of the interests of others.
  3. In real-life clinical practices, there will not be a single correct decision but a number of ethically viable alternatives.

Artificial intelligence in healthcare: past, present and future

Jiang F, Jiang Y, (...), Wang Y.

Stroke Vasc Neurol. 2017. DOI: 10.1136/svn-2017-000101.

Data types considered in AI literature
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AI is bringing a paradigm shift to healthcare, powered by the increasing availability of healthcare data and the rapid progress of analytic techniques. 

  1. The increasing availability of healthcare data and rapid development of big data analytic methods have made possible the recent successful application of AI in healthcare.               
  2. AI can be applied to various types of healthcare data in the form of Machine Learning (structured, imaging, genetic and EP data) and natural language processing (unstructured data).                   
  3. Regulatory process and data exchange are the main obstacles for real-life implementation.

A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations

Sabanayagam C, Xu D, (…), Wong TY.

Lancet Digit Health. 2020 DOI: 10.1016/S2589-7500(20)30063-7.

Kidney disease retinal photographs
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An Artificial Intelligence deep learning algorithm (DLA) to detect chronic kidney disease from retinal images.

  1. A retinal image DLA shows good performance in detecting chronic kidney disease.
  2. Combining DLAs with known risk factors for chronic kidney disease showed better accuracy than both used separately.
  3. This study linking the retina and the kidney underlies the feasibility of applying AI to retinal photography as a screening tool for chronic kidney disease in community populations.

Machine Learning and Natural Language Processing in mental health: systematic review

Le Glaz A, Haralambous Y, (...), Lemey C.

J Med Internet Res. 2021. DOI: 10.2196/15708.

Geographical distribution of authors.
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Machine Learning and NLP techniques provide useful information from unexplored data (eg, patients’ daily habits that are usually inaccessible to care providers). Before considering it as an additional tool of mental health care, ethical issues remain and should be discussed in a timely manner.

  1. Ethical issues, such as predicting psychiatric episodes or implications in the physician-patient relationship, should be discussed in a timely manner.
  2. ML and NLP methods may offer multiple perspectives in mental health research, and they should be considered as a tool to support clinical practice.
  3. Language in both spoken and written forms plays an important role in Machine Learning (ML) mental health applications. It is therefore essential to understand what natural language processing (NLP) is before discussing the joint applications of ML and NLP in mental health.

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.