AI's growing role in medicine demands careful ethical consideration, and recent research highlights the importance of collaborative approaches in navigating these complexities. A study published in BMC Medical Ethics details a co-creation workshop designed to operationalize AI ethics in the medical field [1, 2, 3]. The workshop, involving multiple sessions, aimed to identify key issues, dilemmas, and contextual information related to AI implementation in healthcare [3]. Facilitators used tools like MAXQDA to analyze transcripts and Mural boards to capture insights, ensuring a reflexive approach by documenting analytical decisions and potential influences on workshop dynamics [2, 3].
Workshop Dynamics and Outcomes
The workshop sessions revealed interesting dynamics in participant engagement. Session 1, focused on communication and self-perceived technical and medical literacy, received higher scores [1]. In contrast, Session 2, which involved prioritization and Planguage definition, received lower scores across various metrics, including general feeling, content and outcomes, interpersonal communication, and self-perceived technical and medical literacy [1]. The anonymized transcripts from the workshop are publicly available, promoting transparency and further research in this critical area [3].
Generative AI's ROI and Measurement
Outside of medicine, the business world is also grappling with AI's impact, particularly concerning return on investment. A Wharton study reveals that companies measuring their generative AI (GenAI) investments are seeing significant payoffs [4]. Industries like banking, technology, and telecommunications are experiencing notable wins, while retail, manufacturing, and marketing sectors lag [4]. The study emphasizes the importance of establishing clear measurement architectures, drawing parallels to earlier digital revolutions where disciplined measurement led to better governance and outcomes [4]. This echoes the sentiment that "you can't manage what you don't measure" [4]. Meanwhile, in the realm of sports, self-learning AI is being used to make player prop picks for events like "Thursday Night Football," demonstrating AI's diverse applications [5].
TL;DR
- A co-creation workshop study explored operationalizing AI ethics in medicine, highlighting the importance of communication and collaboration [1, 2, 3].
- Analysis of workshop sessions revealed varying levels of engagement, with prioritization and definition tasks receiving lower scores [1].
- A Wharton study found that companies measuring their GenAI investments are experiencing better ROI, particularly in banking, tech, and telco sectors [4].
- Self-learning AI is being applied in various fields, including making player prop predictions for sports events [5].