I am a pediatric cardiologist and have cared for children with heart disease for the past three decades. In addition, I have an educational background in business and finance as well as healthcare administration and global health – I gained a Masters Degree in Public Health from UCLA and taught Global Health there after I completed the program.
The future will need to be created with a paradigm towards education democratization for everyone in healthcare in not only data, databases, and AI but emerging technologies as well.
There will be an increasing number of healthcare professionals who have an education in data science as many schools aim to include data science and AI in the curricula. There will also be a growing cohort of clinicians with a dual degree in data science and AI. The need for programming as a prerequisite for data science in healthcare will no longer be entirely necessary as there will be increasing automated machine learning (AutoML) capabilities built into healthcare datasets.
There will also be a growing number of clinicians with some background in AI to become medical AI architects or chief intelligence officers. There will be much more robust discussions of ethical issues in AI in healthcare with a diverse coalition of stakeholders involved in these discussions and policy formation towards an AI ethics code for healthcare.
Technology and People
For the future, some of the health organizations will undergo a cultural transformation in data knowledge and data science towards an AI center of excellence.
This focused effort on AI in clinical medicine and healthcare will usually be led by the aforementioned chief intelligence officer. A myriad of areas such as quality improvement, peer review, clinical research, business intelligence and data science will reach a multidisciplinary convergence in this AI center of excellence.
In addition, regional efforts of deploying AI for an AI-enabled population health strategy will be seen in several areas around the world (with AI centers for health resembling the National Security Agency approach to security). With the realization that AI is far more complex than deep learning, cognitive neurosciences will play an important role in AI in clinical medicine and healthcare as there will be a yearn for innovative AI architecture.
The future will have AI embedded in so many clinical decisions as well as hospital administrative processes that there will be less labeling of processes or projects as “AI”. In addition, AI in the future will be coupled tightly to outcomes and equity in decisions so that it is no longer called “artificial” but rather “medical” intelligence: synergy between artificial and clinical intelligence with justice for all.
In the very prescient words of Alan Turing: We can see only a short distance ahead, but we can see that much remains to be done.
Part one of this two part series, ‘Technology’, can be read here
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