Assisted Intelligence in Medicine using Interactive Graphs
AIMIG.org

ES, version 2026-07-31

• Problem
  • Healthcare is far from optimal as well in developing regions as well in developed ones for many reasons including lack of appropriate access to knowledge, delays for access to doctors, budget constraints, poor availability in rural areas. No individual doctor can any more master all medical knowledge domains, including "rare disease".
    
• Mission
  • Make medical knowledge better available for decisions in a practical way for patient care.
    
  • Moreover useful for education, research, care quality and cost limitation.
    
• Approaches
  • "AI" as explainable "Assisted Intelligence" rather than "Artificial Intelligence". To be seen as assistance from consultants, but here virtual remote assistance, based on the know-how of teams of human medical experts.
    
  • Helping but not replacing healthcare professionals. The Care Provider in contact with the patient, taking the final decision.
  • A task oriented approach, identifying problems and seeking solutions.
  • Medical reasoning essentially based on graphs, while language technologies may beuseful for input and output.
    
  • Complex Medical Knowledge representation by means of graphs. Concepts as "nodes" and relationships as "edges" where any node can have a relationship with any other node in a space of millions of concepts and where relationships may be qualified. Decisions taking account of many factors presented as graphs, with the relative weights of relationships between concepts. This require the management of complex medical information in a N-dimentional space, by means of graph technologies. Therefore both medical knowledge and patient records need to be converted in graphs.
  • A multidisciplinary community including doctors, data scientists and software developers. Here trust is essential.
    
  • Seeking grants for international Open Source and Open Data collaborations as a not-for-profit initiative. Transparency of the sources of information by trusted authors. With support from scientific communities, universities and seeking grants of common goods.
  • However "support services" remain usual business for every healthcare organization, including installations, training and maintenance.
    
• Medical Knowledge as graph
  • Better access to already existing medical knowledge. Conversion of current medical knowledge into graphs. A synthesis from different sources as medical, courses, textbooks, ontologies, literature, and above all medical experts from specialized scientific communities.
  •  A large amount of biomedical knowledge is in principle already available but the question is now how to use this knowledge in a more efficient way.
    
  • Focus on the relations between symptoms, problems and to be recommended actions.
  • Maintenance of a synthesis of agreed medical know-how, based on an international community of experts.
    
• Patient record as graph
  • Patient information also structured as a graph with links to related knowledge. For example easy navigation from every "health issue" to symptoms, complication, actions, patient-doctor encounter, ...
    
• Decision support
  • Given information from both  the patient and from medical knowledge, try to provide recommendations. Using "graph navigation", "vectors", "agents", etc...
    
  • Qualified recommendations with both probability and degree of certainty.
  • What most matter here is the medical logic with explanations.
    
  • Typical Use Case:
  • A patient arrive in emergency with a problem, for example shortness of breath, what are the likelihoods of possible issues and what are the relative priorities of what should be done next?
    Step by step priorities may be of giving oxygen? of which questions ? which physical examination ? ask thorax xRay images ? ask an ECG ? ask lab tests ? begin a treatment ? decision about admission ?
    
  • After every new information, re-evaluate the new situation and adapt the visual graph of likelihoods and priorities.
• Intended users:
  • Telemedicine in situations where there is limited local qualifications and/or not enough doctor time per patient.
    
  • Contribution to medical education and training, in front of interactive decision strategies.
  • Preliminary sorting of patients waiting for an appointment or in a waiting room.
    
  • Reduction of doctor time spent on basic information gathering.
    
  • Many specialized health organizations are working on knowledge bases in traditional and incompatible ways.  The challenges  are integration and accessibility in a format suited for decision support.
  • Quality checks which may provide warnings.
• Human graph interface
  • Facilitate the understanding between graphs in human minds, based on neuron and synapses, and graphs in machines. Both humans and machine can understand and share graphs. Since graphs are anyhow essential, why not an interface based on graphs rather than only on texts ?
    
• Education
  • Involvement of teachers and training of students playing with graphs in order to discuss differential diagnosis and the potential benefits of next actions.
    
• Research
  • At a later stage, analysis of large populations of patient records in order to improve the knowledge base. Fine-tuning of the weights of relations between concepts. Evaluation of the results of treatments.
  • Discovery of unsuspected patterns using graphs software tools.
• Experimental prototype
  • Call for a community of physicians, data scientists and software developers. Seek grants for collaborations with similar international projects in Open Data and Open Source.
  • Medical:
  • Knowledge:
    
  • Conversion of medical Knowledge into graphs, at least in some medical domains,. Not only usual graphs of coagulation factors, but knowledge about dyspnea ? chest pain ? abdominal pain ? meaning of lab tests ? etc .... . Focus on the properties of relations between concepts.
  • Interactive decision support:
  • Conversions of patient record into graphs. Make links between observed symptoms of the patient and theoretical knowledge about these observations.
    
  • Experimental decision support. Initially limited to decisions about which additional information to seek next.
  • Evaluation about how the know-how of top medical experts could be made available and useful to beginners in remote regions.
    
  • Technical:
    
  • Make a better "Graph Editor" in order to facilitate the man-machine communication. Already possible but laborious.
  • Requirement analysis of medical experts and development of decision support software tools.
    
• Contact: etienne@saliez.be, ...