Introduction:
Medical diagnosis is a complex process that relies on a combination of clinical expertise,
diagnostic tests, and patient information. However, the current diagnostic approach faces several
challenges, including variability in interpretations, limitations of traditional diagnostic
methods, and the increasing volume of medical data. Integrative AI has emerged as a promising
approach to address these challenges and revolutionise the way diseases are diagnosed and treated.
Integrative AI for diagnosis utilises artificial intelligence (AI) to combine information from
multiple sources, including text, images, and electronic health records (EHRs), to provide a more
comprehensive and accurate diagnosis. This approach has the potential to overcome the limitations
of traditional methods and improve patient care.
Integrative AI: A Paradigm Shift in Diagnosis:
Integrative AI for diagnosis addresses these challenges by combining information from multiple
sources, including text, images, and EHRs, to provide a more comprehensive and accurate diagnosis.
AI algorithms are trained on large datasets of medical data, enabling them to learn patterns and
relationships between image features, clinical findings, and patient characteristics.e.
Text-Image Fusion: Enhancing Interpretation:
Text-image fusion plays a crucial role in integrative AI for diagnosis. This technique involves
combining text information, such as medical reports or patient histories, with corresponding
medical images to provide additional context and enhance interpretation. For instance, text
descriptions of lesions in dermoscopy images can aid in skin cancer diagnosis, while clinical text
reports can supplement the interpretation of echocardiography images for heart disease diagnosis.
Aim:
The primary aim of this PhD project is to develop and evaluate integrative AI models for
diagnosis, with a specific focus on enhancing interpretation through text-image fusion.
Objectives:
To achieve the overarching aim, the following specific objectives will be pursued:
Develop novel text-image fusion techniques for medical diagnosis.
Evaluate the performance of text-image fusion models in enhancing interpretation compared to
traditional methods.
Investigate the impact of text-image fusion on clinical decision-making and patient
outcomes.
Design and implement clinical trials to evaluate the effectiveness of text-image fusion in
real-world settings.
Potential Case Study:
In this PhD project, our primary focus will be on exploring the vast potential of text-image
fusion in medical diagnosis. Echocardiography will serve as a compelling case study, given its
clinical significance in diagnosing heart conditions. The project benefits from strong
collaboration with clinical partners, providing invaluable expertise and access to extensive
patient data related to echocardiographic studies. By honing in on this specific application, we
aim to develop and evaluate text-image fusion techniques that enhance interpretation and
contribute to more accurate diagnoses in the realm of heart disease. However, recognising the
broader implications of our research, we will also remain open to exploring other medical
applications. This flexibility allows us to adapt our innovative approach to various contexts,
ensuring that our findings can be applied across diverse domains within the field of medical
diagnosis.
Candidate profile:
We are seeking highly motivated candidates with a strong background in computer science, machine
learning, or a related field. The ideal candidate should possess solid programming skills. Strong
analytical skills,
problem-solving abilities, and excellent communication and collaboration skills are essential for
success in this PhD position.
Further information:
This PhD position offers a supportive research environment, access to state-of-the-art facilities,
and the opportunity to collaborate with leading researchers in the field. The successful candidate
will receive a competitive stipend and opportunities for conference participation and publication
of research findings.
Application details:
To apply for this position, interested candidates should submit a detailed CV, a cover letter
outlining their research interests and motivation for pursuing a PhD.
Shortlisted candidates will be invited for an interview to further discuss their research ideas
and suitability for the position.
Expected start date: January, May, and September of each academic year.
Duration: This is a three-year position
For more information about the project, please contact Dr Nasim Dadashi
