AI Readiness Index Survey

Faculty and Staff Roles: What is your current role?

Please enter your current role here

Department/Administrative Unit: What is the name of your department/administrative unit?

Please enter your Department or Administrative Unit here

Formal AI or Machine Learning Training: Have you received any formal training in AI or related fields?

Formal training in AI (Artificial Intelligence) or Machine Learning typically involves a structured curriculum offered by educational institutions or professional organizations. These programs are designed to provide comprehensive knowledge and skills in AI and Machine Learning, covering both theoretical concepts and practical applications.

If Yes, please specify the nature and extent of this training. (Select all applicable options)

• University degrees: Master's degrees and PhDs in computer science, artificial intelligence, or related fields typically include coursework in machine learning, as well as the opportunity to conduct research in the field.
• Professional certificates: These programs are shorter than degree programs and are designed to provide professionals with the skills they need to use machine learning in their jobs. They are often offered by universities, colleges, and private companies.
• Bootcamps: Bootcamps are intensive, immersive programs that teach you the skills you need to become a machine learning engineer or data scientist in a short period of time. They typically last for a few weeks or months and are often very expensive.
• Online courses and MOOCs (Massive Open Online Courses): There are a wide variety of online courses available on machine learning, from beginner to advanced levels. These courses can be a great way to learn the basics of machine learning or to deepen your knowledge of the field.

For Professional certificates, please provide the certificate provider. (Select all applicable options)

• Several technology companies offer AI/ML training certifications, including Microsoft, Google, Amazon Web Services (AWS), IBM, and SAS.
• The following professional organizations offer certifications and educational resources in Artificial Intelligence (AI) and Machine Learning (ML): American Statistical Association (ASA), Institute of Electrical and Electronics Engineers (IEEE), Association for Computing Machinery (ACM), Society for Industrial and Applied Mathematics (SIAM) 

Please list the Tech Companies from where you obtain your professional certificates. (Select all applicable options)

Self-Rated AI Expertise Level: How would you rate your own expertise in AI?

Beginner: Individuals at this level have a basic understanding of AI concepts. They might be familiar with common terms and some of the ways AI can be used, but they have limited practical experience. They require guidance to use AI tools and cannot yet develop or apply AI solutions independently.

Intermediate: Faculty and staff with intermediate expertise have a good grasp of AI principles and can use some AI tools or systems related to their field. They understand AI's potential applications and limitations and can participate in AI-related projects with some support. They might be able to manage simple AI tasks but are not yet proficient in developing new AI technologies or complex problem-solving with AI.

Advanced: At the advanced level, individuals have a comprehensive understanding of AI, including its theoretical foundations and practical applications. They can effectively use a range of AI tools, contribute to AI project design, and solve complex problems using AI. They can also provide guidance to others in using AI technologies and may lead AI projects, although they may not be at the forefront of developing new AI innovations.

Expert: Experts possess a deep, comprehensive knowledge of AI, often with a specialization in a particular area. They are capable of leading research and development in AI, teaching others, and applying AI to solve novel or highly complex problems. They are often sought out for their thought leadership and have a track record of contributions to the field of AI. Experts can develop new AI models or methodologies and drive innovation within the institution.

Which of the following AI tools and applications are currently utilized in your clinical care practices? (Select all applicable options)

• Diagnostic Support Systems: Utilize machine learning algorithms to assist in diagnosing diseases from imaging studies, blood tests, and other data.
• Predictive Analytics: Tools for predicting patient outcomes based on historical health data and current health indicators.
• Treatment Recommendation Systems: AI-driven systems that suggest treatment options based on patient data and evidence-based guidelines.
• Patient Monitoring Systems: Wearables and remote monitoring tools that use AI to track patient health metrics in real-time.
• Robotic Surgery Assistants: Robots guided by AI to assist or perform surgical procedures with precision

Which AI-driven tools and applications are being used in your educational programs? (Select all applicable options)

• Adaptive Learning Platforms: Personalized learning experiences for students based on their learning pace and style using AI.
• Virtual Patients and Simulations: AI-driven simulations that provide realistic and interactive environments for clinical training.
• Automated Assessment Tools: Tools that utilize AI to grade exams and provide feedback on student performances.
• Learning Management Systems (LMS) with AI Features: LMS integrated with AI to enhance learning experiences through smart content curation and interaction.

What types of AI tools and applications are employed in your research activities? (Select all applicable options)

• Data Mining and Analytics Tools: For extracting insights from large datasets, including electronic health records (EHRs), genomic data, and research databases.
• Natural Language Processing (NLP) Tools: For analyzing unstructured data from medical literature, clinical notes, and other textual sources.
• Predictive Modeling and Simulation: Tools for developing predictive models and conducting simulations in public health, epidemiology, and clinical research.
• Genomics and Bioinformatics Tools: AI applications for analyzing genetic data, understanding disease mechanisms, and identifying potential therapeutic targets.

Which AI tools and applications are integrated into your administrative operations? (Select all applicable options)

• Resource Optimization Tools: AI-driven tools for optimizing hospital and clinic operations, including staff scheduling, patient flow management, and inventory control.
• Patient Engagement Platforms: Platforms that use AI to personalize patient communications, appointment scheduling, and health reminders.
• Healthcare Chatbots: AI chatbots for handling patient inquiries, providing information, and assisting with administrative tasks.

What specific training do you believe is required to effectively utilize Generative AI solutions in clinical care?(Select all applicable options)

What training do you consider necessary for educators and students to effectively use Generative AI in educational settings? (Select all applicable options)

What other training is necessary for effectively employing Generative AI solutions in medical education?

Availability of AI Specialists or Support in the Department: Do you have any Dedicated AI/ML Staff in your department?

Availability of AI or Support/Resources : Do you have any AI/ML support/resources in your department/Institution?

Please list the AI support or resources in your department/Institution. (Select all applicable options)

Please specify other Support/Resources

How integrated do you feel AI tools are in your day-to-day work processes?

Fully Integrated: AI tools are a central component of daily tasks and operations. They are seamlessly embedded into the workflow, with users frequently relying on AI for a wide range of activities. There's almost no aspect of the work that isn't touched or enhanced by AI capabilities.

Mostly Integrated: AI tools are a regular part of many work processes, though not all. They contribute significantly to productivity and efficiency, and users are comfortable and familiar with their use. There may be a few areas or tasks that have not yet adopted AI tools, but they are the exception rather than the rule.

Somewhat Integrated: AI tools are used in several work processes, but their use is not pervasive across all operations. They might be employed for specific tasks where their value is clear, but many activities still proceed without AI assistance. Users are generally aware of and occasionally engage with AI in their work.

Minimally Integrated: AI tools have a limited role in work processes. They may be used sporadically or in a small number of tasks where they can add value, but they have not become a standard part of the workflow. Many users may have little to no direct interaction with AI tools.

Not Integrated: AI tools have not been adopted into work processes at all. Either they are completely absent, or if they are available, they are not utilized by staff in their daily tasks. Work is performed without the aid of AI, relying on traditional methods.

How supported do you feel by the institution in using or developing AI tools

Very Supported: The institution provides robust backing for AI initiatives. This includes substantial funding, clear strategic direction, ample resources, dedicated AI departments or teams, and strong leadership advocacy. There's a culture of encouragement for innovation in AI, and initiatives are met with enthusiasm and institutional commitment.

Supported: There is a noticeable level of support from the institution. This could manifest as allocated budget for AI projects, access to necessary tools and resources, availability of training programs, and a general openness from leadership to explore and invest in AI technologies. Support is consistent but may not be as extensive or proactive as in environments where one feels "very supported."

Neutral: The institution neither particularly supports nor opposes the use of or development in AI. Resources and support may be available but not actively promoted or facilitated. There might be some pockets of AI activity, but they are mostly driven by individual interests rather than a strategic institutional effort.

Unsupported: There is a lack of institutional support for AI, which may stem from limited resources, lack of interest or understanding from leadership, or competing priorities. Efforts to use or develop AI tools may be met with indifference or bureaucratic hurdles, and any AI-related initiatives are typically driven by individuals or small groups without significant institutional backing.

Very Unsupported: The institution actively resists or obstructs efforts to use or develop AI tools. This could be due to a variety of factors such as a risk-averse culture, stringent budget constraints, regulatory barriers, or a lack of leadership vision for the role of AI. Initiatives are discouraged, and there is a significant lack of resources or institutional strategies for AI.

What do you see as the top three biggest challenges in adopting AI in your work environment?

What additional support or resources would you need to enhance your work with AI?

Any additional comments or suggestions that could help in understanding the AI readiness of our institution?