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AI Risk Comparison

Medical Scientists, Except Epidemiologists vs Bioinformatics Scientists

Compare AI replacement risk, automatable work, resilient skills, and potential career pivots for both occupations.

Safer role
Medical Scientists, Except Epidemiologists
Higher risk
Bioinformatics Scientists
Risk gap
1 points
Science & ResearchO*NET: 19-1042.00

Medical Scientists, Except Epidemiologists

Conduct research dealing with the understanding of human diseases and the improvement of human health. Engage in clinical investigation, research and development, or other related activities.

AI Risk Score

40/100
Medium

Moderate risk: AI can reshape important parts of the role.

Automation factors

  • Conduct research to develop methodologies, instrumentation, and procedures for medical application, analyzing data and presenting findings to the scientific audience and general public.
  • Updating and Using Relevant Knowledge
  • Working with Computers
  • Analyzing Data or Information

Top skills

Writing4.25/5
Speaking4.12/5
Science4.12/5
Active Learning4.12/5
Reading Comprehension4.00/5

Recommended career pivots

Geneticists39/100Biochemists and Biophysicists41/100Physicians, Pathologists37/100Microbiologists39/100Medical and Clinical Laboratory Technologists43/100
Science & ResearchO*NET: 19-1029.01

Bioinformatics Scientists

Conduct research using bioinformatics theory and methods in areas such as pharmaceuticals, medical technology, biotechnology, computational biology, proteomics, computer information science, biology and medical informatics. May design databases and develop algorithms for processing and analyzing genomic information, or other biological information.

AI Risk Score

41/100
Medium

Moderate risk: AI can reshape important parts of the role.

Automation factors

  • Develop new software applications or customize existing applications to meet specific scientific project needs.
  • Consult with researchers to analyze problems, recommend technology-based solutions, or determine computational strategies.
  • Analyze large molecular datasets, such as raw microarray data, genomic sequence data, or proteomics data, for clinical or basic research purposes.
  • Working with Computers
  • Analyzing Data or Information

Top skills

Reading Comprehension4.12/5
Critical Thinking4.12/5
Active Listening4.00/5
Speaking4.00/5
Complex Problem Solving4.00/5

Recommended career pivots

Bioinformatics Technicians64/100Data Scientists8/100Molecular and Cellular Biologists39/100Geneticists39/100Bioengineers and Biomedical Engineers45/100

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