Table 1.

Selected Similarities and Differences between Medical Informatics (MI) and Bioinformatics (BI)

	Feature Comparison and Contrast
Discipline Feature	Bioinformatics	Medical Informatics
1. Academic discipline, development, and interdisciplinary links.	Gradual build-up, focusing on science-driven methods of analysis for data from emerging gene and protein technologies. Early connections with cybernetics, information, and automata theory gave an information-oriented focus to genomics.	Rapid launch in the 1960s based on computer technology opportunities in health care. Few connections of medicial practice with “preinformatics” disciplines (mathematical modeling, statistics, linguistics, AI, etc.)—though fundamental for public health epidemiology, laboratory medicine, and radiology.
2. Scientific content and informatics goals.	Molecular biology and biochemistry—massive data analysis and management demands for biological scientific discovery.	Medicine—standardization, efficient data processing and specialized analysis and management tools for clinical practice, biomedical research, and education.
3. Data quality and analysis: noise and uncertainty. Role of data mining.	Scientific data—controlled noise and uncertainty models and prospective studies allow relatively frequent direct application of data mining methods.	Clinical data—other than for controlled clinical trials, the incompleteness, subjectivity, and uncertainty of retrospective patient data make mining results difficult to replicate and transfer.
4. Integrating data and knowledge: networks, databases, interoperability, standardization, and information retrieval.	Many specialized databases publicly available over the Web. Interoperability good; quality issues addressed by increased curation.	Pioneering networking (Medline, SUMEX-AIM), documentation standards, vocabularies, and coding (e.g., UMLS, SNOMED, HL7). Clinical databases mostly nonpublic.
5. Tools to support medical practice.	Recent emphasis on ontologies, network models, etc. No involvement in clinical applications until gene expression and SNP studies—still in early stages.	Emphasis on information systems for patient care, hospital, and medical information (HIS, MIS), education, and telemedicine. Representation of medical knowledge and language (i.e., terminology, indexing, disease taxonomies), central for information retrieval and decision support.
6. Tools to support research and practice: signal processing, imaging and visualization, computational modeling.	Signal processing biophysics-oriented. Three-dimensional graphic visualization of molecular structures for scientific research. Image analysis of functional data and network modeling and simulation.	Signal, image processing, and computational modeling bioengineering-oriented. Three-dimensional visualization of radiologic, histologic, other imaging for clinical and educational use. Related to clinically useful (logic, coding, text, and graphics) knowledge representations and systems architectures.
7. Professional and health consumer education.	Widely available tools for scientific data analysis. Prevention and personalized diagnosis and treatment seen as future promises.	Widely available Web sites, multimedia tools for accessing wide range of biomedical information, analysis tools less widely disseminated.
8. Education and training of MI and BI professionals: role as scientists, engineers, and information brokers.	Recent creation of training programs, rapidly expanding.	Training programs from the 1970s, firmly consolidated—much interdisciplinary experience.