AI is becoming part of everyday decision-making in many organisations. In healthcare, it can help professionals analyse large amounts of information, identify patterns and assess complex cases. But introducing AI does more than add a new tool. It can also change what people pay attention to, how they understand a problem and how a decision is reached.
This is what Bijona Troqe wanted to understand in her doctoral research. Instead of asking whether AI makes better decisions than humans, she studied what happens when humans and AI contribute to the same decision-making process.
鈥淚 want to understand human-AI decision-making from an organisational perspective. I don't study the technology itself or try to optimise the tools,鈥� says Bijona Troqe.
Her research focuses on decision-making in organisations where many people, different types of knowledge and uncertainty are involved.
The roles are not fixed
Bijona Troqe studied three settings within Swedish personalised medicine: AI-assisted breast cancer screening, personalised medicine development and data-driven care. Her research included interviews as well as focus groups, observations, site visits and document analysis.
One of her main findings is that humans and AI do not have fixed roles. Who contributes what can change with the situation, the available information and the level of risk.
In breast cancer screening, for example, an AI-generated risk score can influence how a mammography case is handled and whether it needs further review. In other situations, the AI and the radiologist contribute at different stages of the process.
鈥淲hat I saw was a more distributed and fluid form of collaboration. Within the same workflow, the collaboration can take different forms,鈥� says Bijona Troqe.
AI can analyse large amounts of data and find patterns. People contribute professional experience and knowledge of the situation that the AI system may not have. Their strengths can therefore complement each other.
AI changes what we pay attention to
The research also shows that AI can affect a decision before the final choice is made. By sorting information, identifying patterns and producing risk scores, AI can influence what people notice and how they understand a problem.
One example comes from data-driven healthcare. Healthcare professionals were asked which factors they thought were important for predicting whether patients would return to an emergency department after being discharged. When a predictive model analysed the data, it identified some factors that the professionals had ranked as less important, as well as factors they had not considered.
鈥淎I can bring forward things that decision-makers haven't considered before,鈥� says Bijona Troqe.
Organisations therefore need to consider both what AI makes more visible and what may receive less attention as a result.
鈥淚t's not always better. That's why we need to understand more about how we collaborate and interact with these technologies,鈥� says Bijona Troqe
Start with the problem
Organisations considering AI should first define the decision they want to improve and the problem they want to solve, Bijona Troqe argues. The technology should not be the starting point.
They must also consider who makes the decisions, what professional knowledge they use, the organisation's goals and the technical and organisational conditions.
Teiksma Buseva
鈥淏e very aware of why you want to implement AI. What is the actual need? What are you trying to solve? I would start from there,鈥� says Bijona Troqe.
Although the research is based on healthcare, the findings may also apply to other organisations where decisions involve many people, several types of knowledge and uncertainty.
鈥淒ecisions are never isolated. They are always embedded in a context,鈥� says Bijona Troqe.
