Future prospects

Successfully translating AI proof of concepts into clinical practice remains pivotal for fully realizing AI’s impact.
While practical guidelines and best practices are emerging in medical AI, they are not always adhered to and require frequent reassessment due to the pace at which the AI field is moving forward. When implementing AI, it is strongly recommended to verify available guidelines to ensure applications are reliable and provide meaningful outcomes. We here propose a set of minimal requirements for good practice in AI (Table 1) based on published guidelines of the FDA, literature on best-practice model development in biomedicine, or expert-based checklists for developing and reporting algorithms (e.g., STARD-AI, TRIPOD checklist, and awaited TRIPOD-AI adaptation).
In the allergy and immunology field, research beyond proofs-of-concept is relatively scarce, let alone meaningful clinical applications. We provide an expert outlook on noteworthy AI trends. Firstly, the ever-increasing accessibility, automation, and transferability ofML tooling are expected to drive AI adoption further, enabling non-specialized researchers to apply novel techniques. Secondly, we expect an increase in the use of unstructured data. Innovations such as AI-based image analysis, NLP, and generative AI are at the forefront of academic efforts in computer science while being underutilized in our field. For clinicians, an AI clinical assistant akin to readily available ‘home assistants’, could quietly listen in on consults and subsequently support in documentation in EHRs, diagnosis, and therapy suggestions. Clinical solutions that leverage speech recognition are entering the market, aiming to improve the clinical workflow and efficiency, although adoption and showcases of tangible impact are still limited. Thirdly, the emerging trend of multi-modal learning can open new research avenues by integrating multiple data sources and modalities in a singular analytical approach, hereby creating more holistic models and insights.
The largest future impact from AI is expected when current proofs-of-concept are translated successfully to clinical practice. The US and the EU are making steps towards developing AI and algorithm regulations, to facilitate updates and improve privacy, security and transparency.
The developers of algorithms play a role in clinical translation, and clinicians would need to adapt to the integration of AI within healthcare. While most AI systems are designed as a support mechanism rather than a replacement, it will change their work and role. Clinician training in the fundamentals of AI is needed to gain trust in these systems and work with them effectively. One of the common concerns regarding AI is that these systems will replace humans in their installment. While many studies position their analytical solution in a head-to-head comparison with humans, most clinical applications are designed as decision-support tools that strengthen and assist experts in their profession rather than replacing them41. Lastly, we foresee further developments in dynamic learning systems, which continuously evolve based on clinical usage. Such approaches are rare, and FDA-approved tools are generally ‘locked’, referring to a fixed algorithm state. The FDA is working on an action plan to better assess and support such applications.
In conclusion, the potential of AI to transform clinical medicine is evident, but the steps from a proof of concept to clinical applications are not easily made. Innovations from the field of AI can address many important open questions in allergy; we anticipate that good future utilization of AI (Table 1) will deepen our knowledge of disease mechanisms and contribute to precision medicine in allergy.