Imagine you’re tasked with urgently improving healthcare for people everywhere, and artificial intelligence (AI) promises to make a significant difference on a scale not seen in our lifetime. With the three options presented below, you can either:
- 1. Try to halt the development of AI until the risks and benefits are fully understood.
- 2. Go blazing into the unknown, deploying AI at every turn and dealing with issues as they arise.
- 3. Or fully embrace AI but take considered steps to limit the risks while unlocking the immediate benefits.
The general problems to be solved are
Many healthcare organizations are struggling to consistently deliver high-quality care.
There are more patients to look after and significant pockets of staff shortages, with doctors and nurses burdened by increasing clerical demands and complexity.
Costs are rising too.
Although many of us have received excellent care and great treatment, others struggle to access the care they need, especially people who live in under-resourced areas of the world
but also, increasingly, people living in wealthy urban areas.
We believe this crisis will only worsen unless we urgently deploy solutions that make a difference.
The good message is
AI can help, and it already has.
Generative AI tools and chatbots, for example, are cutting the time doctors and nurses spend on paperwork. Every day, we learn more about the potential of AI to improve healthcare. One recent study by the UK’s Royal Marsden NHS Foundation Trust and the Institute of Cancer Research showed that AI was “almost twice as accurate as a biopsy at judging the aggressiveness of some cancers.” This translates into different treatments and, ultimately, more lives saved. To learn about other developments on the horizon, consider some of the many predictions for what’s in store for 2024, from accelerating drug discovery to boosting personalized medicine and patient engagement.
But the goals are to be
- (1) Harmonize the many sets of AI principles/frameworks/blueprints for healthcare and biomedical science. Identify and fill the gaps to create a best practice AI Code of Conduct with ‘guideline interoperability.’
- (2) Align the field in advancing broad adoption and embedding of the harmonized AI Code of Conduct.
- (3) Identify the roles and responsibilities of each stakeholder at each stage of the AI lifecycle.
- (4) Describe the architecture needed to support responsible AI in healthcare.
- (5) Define the identified ways to increase the speed of learning about how to govern AI in healthcare in service of a learning health system.
Conclusion
To achieve these goals and realize the full benefits of AI in healthcare while mitigating risks, it will take cross-sector collaboration and coalition building every step of the way – because we have a collective industry-wide responsibility to get this right.
Here the full Article by the World Economic Forum:
