Using AI in Healthcare responsibly

Artificial intelligence(AI) is slowly demonstrating its ability to help, improve and transform medicine. Typical examples are

• Predicting health outcomes
• Modeling Drug Discovery
  
• Assisting in Triaging  
• Predicting disease outbreaks
  

I am very optimistic about AI’s potential use within the medical and clinical business, both in terms of making these disciplines more efficient and effective, as well as in the long term, changing what they will mean and represent to the rest of the world. 

My worry though, is that, its being promoted almost as a fad in today's times. By promoting unrealistic expectations based on biased data, we run the risk of creating low levels of trust in the mindset of the users and everyone outside the immediate ecosystem, much like everything else within digital health has suffered from the very beginning.

A report published in JAMA  - Artificial Intelligence in Health Care: A Report From the National Academy of Medicine recommends that people developing, using, implementing and regulating AI for healthcare do seven key things
to ensure these technologies and tools are developed, implemented and maintained responsibly

  

Promote data that accurately represents populations with accessibility, standardization and quality.  - to overcome data availability biaseses ensure accuracy for all populations

 

Prioritize ethical, equitable and inclusive medical AI while addressing explicit and implicit bias. - to understand the potential of the Underlying biases to worsen or address existing inequity 

 

Clarify the level of transparency needed across a AI developers, implementation teams, users and regulators -   Contextualize the dialogue of transparency and trust, accept differential needs.

 

Focus in the near term on augmented intelligence rather than AI autonomous agents. - supporting data creation, data interpretation and decision-making by clinicians and patients is where opportunities are now

 

Develop and deploy appropriate Training Programs. - Training programs must be multidisciplinary and should engage AI developers, implementation teams, health care system leadership, front line clinical teams, ethicists, humanists, patients and caregivers

 

Have a robust and mature IT governance strategy in place before Health delivery systems use AI formally - Use and adapt existing frameworks and best practices for learning health care systems, human factors and implementation science.

 

Promote trust and balance innovation with safety through regulation and legislation - evaluate deployed clinical AI for effectiveness and safety based on clinical data.

The above is a mashup of original thoughts and ideas curated from:

Artificial Intelligence in Health Care: A Report From the National Academy of Medicine

https://www.ama-assn.org/practice-management/digital/7-tips-responsible-use-health-care-ai

https://www.ama-assn.org/practice-management/digital/10-ways-health-care-ai-could-transform-primary-care

 

Image credits: 

First Image via http://www.brother.co.uk/business-solutions/healthcare/future-of-hospital-technology

Second Image via https://www.vpnsrus.com