We know that busy executives don’t always have time to read through an entire magazine, like AIMed Magazine, so we’ve condensed AIMed Magazine 04 into takeaways for executives which will allow you to peruse the mag and extract what’s most important for you!

Summary for executives

P 12 -15

A Texan’s Guide for Newcomers to Big Data and Cloud Computing
By Curtis Kennedy, MD

  • The bigger your data set is, the more difficult it is to utilize, but the greater its potential.
  • Larger data sets, like larger machinery, necessitate more skilled teams.
  • Those storing more data will likely need to outsource to the cloud, as with outsourcing to a storage company.


P 17 – 19

The Continuum of Cloud
by Timothy Chou, PhD

  • High computing power has been democratized and immense power can be briefly rented relatively cheaply.
  • We are moving towards a world where cloud storage is essentially free.
  • A vertical internet of healthcare machines would enable global collaboration.


P 20 – 24

Cybersecurity: You are the Weakest Link
by Nick van Terheyden, MD

  • Even experts in social engineering have been phished and hacked.
  • Healthcare data is extremely valuable if sold on the black market.
  • Staff participation from the top down is necessary to create a culture of security.


P 26 – 29

Blockchains in Medicine
by E. Kevin Hall

  • Blockchains are decentralized internet-based ledgers based upon Merkle trees.
  • Blockchains may go far in answering the problems of a “single truth” in medicine.
  • For blockchains to be applied to medicine, problem-sets need to be defined.


P 32 – 35

The challenge of data protection diversity for AI research
by Multiple

  • Risk averse decisions by ethics committees can make the creation of databases impractical due to increased costs.
  • International regulations like GDPR may seriously restrict global AI research.
  • Greater harmonisation internationally in the use of data in AI research will be beneficial both to investigators and to members of the public.

P 40 – 43
Machine Learning and EHRs
by Multiple

  • Machine learning needs to be incorporated within EHRs to cope with increasing amounts of data.
  • Techniques need to be developed for real time data analysis and these then need to be harmonized for clinical interpretation.
  • The many known and unknown data silos need to be disrupted and opened up for research and development.

P 44 – 47
Physician Perspective
by Naila (Siddiqui) Kamal, MD, MBBS

  • Clinicians need to engage with disruptive technologies and make recommendations to their CIOs.
  • Age is not a barrier to engage with emerging fields of knowledge and skill.
  • It is always good to be an advocate of innovation rather than a barrier by inaction or disinterest. 

P 48 – 51
Empathy, Values and AI
by Jules Sherman, MFA

  • Creating AI that is aligned with human values is impossible, because different people have different values.
  • But we can work to engineer AI that incorporates insights about the group it is built to serve and their specific behavior.
  • We need to determine which experiences require AI, which are meaningfully enhanced by AI, and which do not benefit from AI or are even degraded by it.

P 56 – 66
Building a Strong Future – Diversity Feature
by Multiple

  • The AI Med community, sitting at the intersection of medicine and computer science, reflects roughly the same (or worse) gender imbalance as we see in either field.  
  • Sheer intelligence is not as powerful as having multiple points of view to find new and better solutions and to help correct for human error and biases.
  • A lack of diversity in the development of AI models can drastically reduce its effectiveness.
  • Intentional inclusivity gives a strategic advantage in solving challenges in medicine.
  • Changing perceptions about the AI Med field will remove a barrier to entry for young women.
  • Whenever you ask for recommendations for speakers at a conference, formally or informally,

This is a guide for executives, but you can find the full AIMed Magazine issue 04 here.