Medical AI: Education and Career Choices

Artificial intelligence (AI) is facilitating dramatic changes in healthcare – from more precise diagnoses to personalised therapies. As AI makes delivery of care become more efficient, corresponding reduction in costs follows.

Amidst this AI-driven transformation in healthcare, attention has focused on the need to have more professionals with adequate training and know-how when it comes to expanding applications of AI in medicine. A good way to start will be to integrate specialised AI courses in medical training and education.

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"How to teach students to practise successfully in a healthcare environment transformed by AI applications should be a central focus of educational reform today," according to researchers from Sun Yat-sen University in Guangzhou, China (Yun et al. 2020).

New expertise and skills will be needed to meet the demands of medical AI, the researchers note, including better use of algorithms in cognitive psychology and performing simulations to enhance integration of new technologies in care delivery.

In a survey study conducted by the Chinese research team, a vast majority of 710 respondents recognised the importance of institutionalising AI training in medical schools. The online survey, which covered social media users from both medical-related fields and non-medical professions, aimed to examine people's attitudes regarding medical AI talent cultivation.

Survey results also show that more than half of the respondents had basic knowledge of AI application scenarios and which medical specialties benefit the most from AI use, such as radiology and clinical laboratory. Those from the medical-related professions had a higher level of AI awareness and willingness to participate in educational events (eg, conferences and lectures) to enhance their knowledge on medical AI.

In terms of career choices in the era of healthcare AI, respondents preferred to be surgeons, ophthalmologists, physicians or researchers.

The study’s findings highlight the need for the creation of academic curriculum and research platforms in medical schools to effectively expand the expertise pool and “accelerate the translational progress of AI in medicine,” wrote the authors, who took note of some important limitations to their research.

Most of the study participants are Chinese and data from other countries are limited, hence the results may not be generalisable, the authors pointed out. Moreover, as the online survey was only accessible by computers and mobile phones with internet connection, this limited the scope of participation and excluded people with no online access.

Source: Annals of Translational Medicine

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