AI in Healthcare: 8 Universal Lessons For Guaranteed Success

“AI is neither good nor evil. It’s a tool. It’s a technology for us to use.” — Oren Etzioni, AI researcher

The AI revolution has crept into almost every industry, and healthcare is no different. In fact, the effects of using AI in the healthcare industry have had revolutionary impacts, leading to ground-breaking solutions that offer valuable lessons to other industries. 

Today, let’s dive into some examples of where AI has seen successful applications in the healthcare industry, and what we can learn from them. 

#1: Early Cancer Detection

Cancer leads to millions of deaths every year, creating not only a heavy weight on the healthcare industry, but also a tremendous emotional burden on countless families worldwide. 

Imagine if you could get diagnosed early enough to prevent death. 

Now, that is more accessible than ever. 

With tools that support individuals to more effectively self-diagnose as opposed to just relying on Dr. Google to make sense of their symptoms, early cancer detection AI tools could begin to ease the burden of losing a loved on to cancer, and the weight on the healthcare industry to support patients who are in the later stages of a fatal diagnosis. 

#2: Neurological Diagnosis

Neurology has a reputation for simultaneously being an incredibly high-stakes field to work in while also being beautifully (and frustratingly) complex. 

Naturally, there is little room for error when it comes to taking care of a brain. 

Still, from automating image interpretation tasks to accurately identifying brain structures, detecting abnormalities, and predicting different treatment outcomes, AI has already acted as a huge help to practitioners in the field of neurology. 

However, there is still a long way to go in developing more effective methods of integrating data science into healthcare. 

#3: Chatbots For Preliminary Diagnosis

Last but not least is Dr. GPT, who we find ourselves unknowingly putting greater and greater trust in as the days go by.

Available at the fingertips of anyone with internet connection, the increasing number of chatbots available to effectively screen patients is making healthcare more equitable than ever before.

While we are not ready to do away with human doctors and nurses just yet, AI chatbots can still help to streamline preliminary diagnosis through administrative tasks, para-clinical tasks (“consensus-building with multidisciplinary teams”), research, and education. 

In a world where healthcare feels out of reach for many, the importance of cutting costs and increasing accessibility cannot be overstated.

AI In HealthCare: Interdisciplinary Takeaways

1. Data-Driven Decision-Making

Steps:

1. Identify Key Metrics: What data is most relevant to your goals or challenges? E.g., student test scores in education or project completion times in construction.
2. Choose AI Tools: Use platforms like Tableau, Microsoft Power BI, or industry-specific AI tools.
3. Analyze Patterns: Look for trends, outliers, and correlations.
4. Make Predictions: Use data to forecast future scenarios.
5. Act on Insights: Implement changes based on predictions and measure the outcomes.

Questions to Explore:

• What specific decisions in your field could benefit from deeper data insights?
• Are there underutilized data sources you can access?
• What tools are available for data analysis?

Example:
Walmart uses AI to analyze sales data and predict inventory needs, optimizing stock levels to reduce costs and improve customer satisfaction.

2. Early Problem Detection

Steps:

1. Map Risk Areas: Identify vulnerabilities in your processes (e.g., structural weaknesses in engineering or environmental risks in conservation).
2. Implement Sensors or Monitoring Systems: Use IoT devices or AI tools to track critical data points.
3. Set Thresholds for Alerts: Establish early warning indicators.
4. Take Preventive Action: Develop action plans for when thresholds are breached.

Questions to Explore:

• What are the biggest risks in your field?
• What types of data could signal early warnings?
• How can you involve your team in defining response protocols?

Example:
Conservationists use AI-powered systems like Wildbook to analyze data on wildlife populations, identifying species at risk of extinction early enough to take corrective action.

3. Personalization

Steps:

1. Understand Your Audience: Collect data on user needs and preferences.
2. Leverage AI for Segmentation: Use AI tools to group users or customers by behavior, demographics, or goals.
3. Design Tailored Experiences: Create content, services, or solutions that address specific needs.
4. Measure and Iterate: Continuously analyze engagement and satisfaction metrics.

Prompts:

• How can AI help you better understand your audience?
• What data can you use to improve personalization?

Example:
Duolingo uses AI to personalize language lessons, adapting to each user’s pace, strengths, and weaknesses, resulting in higher retention and user satisfaction.

4. Automation of Routine Tasks

Steps:

1. List Repetitive Tasks: Identify tasks that consume significant time but require minimal creativity.
2. Choose Automation Tools: Use tools like Zapier, UiPath, or custom AI solutions.
3. Implement Workflows: Develop and test automated processes.
4. Reallocate Time: Focus freed-up resources on strategic activities.

Questions to Explore:

• What tasks in your workflow could be automated?
• How much time could automation save your team?

Example:
GE uses AI-powered software to automate routine inspections of jet engines, reducing downtime and improving accuracy.

5. Real-Time Monitoring and Feedback

Steps:

1. Install Monitoring Systems: Deploy sensors or AI tools to gather real-time data.
2. Set Up Dashboards: Use platforms to visualize and interpret data.
3. Provide Instant Feedback: Share actionable insights with relevant stakeholders.
4. Optimize Responsiveness: Create processes to act on real-time feedback quickly.

Prompts:

• Where in your workflow could real-time monitoring improve efficiency?
• How can you ensure the data is actionable?

Example:
SmartCap uses AI-powered sensors in mining helmets to monitor workers’ fatigue levels in real time, reducing accidents and improving safety.

6. Scaling Solutions for Accessibility

Steps:

1. Identify Underserved Areas: Where are there gaps in access to your services or solutions?
2. Leverage AI Platforms: Use AI-powered tools to deliver solutions at scale (e.g., Coursera for education or drones for conservation).
3. Partner Strategically: Work with organizations to expand reach.
4. Measure Impact: Track metrics like reach, engagement, and effectiveness.

Questions to Explore:

• How can AI expand access to your offerings?
• Who are your target underserved populations?

Example:
Microsoft’s AI for Accessibility program uses AI to develop tools for people with disabilities, such as Seeing AI for the visually impaired.

7. Predictive Analytics

Steps:

1. Define Objectives: What future outcomes do you want to predict?
2. Collect Historical Data: Gather relevant datasets.
3. Use Predictive Tools: Platforms like Google Cloud’s AutoML or IBM Watson.
4. Plan for Scenarios: Develop strategies for likely predictions.

Questions to Explore:

• How can predictive insights improve your decision-making?
• What trends or outcomes are most critical in your field?

Example:
UPS uses AI-powered predictive analytics to optimize delivery routes, saving millions in fuel costs and improving efficiency.

8. Training and Upskilling

Steps:

1. Assess Skill Gaps: Identify what your team needs to learn.
2. Implement Training Programs: Use AI-powered platforms like Coursera, LinkedIn Learning, or custom solutions.
3. Incorporate AI Tools: Introduce tools that align with new workflows.
4. Foster Continuous Learning: Encourage regular skill development.

Prompts:

• What new AI tools could enhance your work?
• How can you make learning a routine part of your team’s culture?

Example:
Adobe offers AI training to designers, helping them master AI-driven tools like Adobe Sensei for automated creative workflows.

Thought to Action 

1. Adopt a Growth Mindset for Upskilling: Treat AI as a tool for continuous learning. Take incremental steps to learn new technologies or approaches, regardless of your expertise level.
2. Leverage AI for Personal Development: Use AI-powered platforms to enhance your skills, such as language learning apps (Duolingo) or career coaching tools (LinkedIn Learning).
3. Integrate AI into Problem-Solving: Use AI tools to analyze and break down complex problems into manageable parts, leveraging insights to develop creative and data-backed solutions.
4. Develop a Personal AI Project: Apply what you’ve learned by creating a small project where AI plays a central role, such as automating a personal task or analyzing data from your hobbies or interests.
5. Identify Inefficiencies in Your Workflow: Analyze your current workflow for repetitive or time-consuming tasks and explore AI-powered tools to address these inefficiencies.

Sources

Ahmad, I. and Fahad Alqurashi (2024). Early Cancer Detection Using Deep Learning and Medical Imaging: A Survey. Critical Reviews in Oncology/Hematology, pp.104528–104528. doi:https://doi.org/10.1016/j.critrevonc.2024.104528.

Au Yeung, J., Wang, Y.Y., Kraljevic, Z. and T H Teo, J. (2022). Artificial intelligence (AI) for neurologists: do digital neurones dream of electric sheep? [online] Bmj.com. Available at: https://pn.bmj.com/content/23/6/476 [Accessed 6 Jan. 2025].

Kalani, M. and Anjankar, A. (2024). Revolutionizing Neurology: The Role of Artificial Intelligence in Advancing Diagnosis and Treatment. Cureus, [online] 16(6), p.e61706. doi:https://doi.org/10.7759/cureus.61706.

Kharat, P.B., Kabir Suman Dash, L. Rajpurohit, Tripathy, S. and Mehta, V. (2024). Revolutionizing healthcare through Chat GPT: AI is accelerating medical diagnosis. Oral oncology, pp.100222–100222. doi:https://doi.org/10.1016/j.oor.2024.100222.

Reardon, S. (2023). AI Chatbots Can Diagnose Medical Conditions at Home. How Good Are They? [online] Scientific American. Available at: https://www.scientificamerican.com/article/ai-chatbots-can-diagnose-medical-conditions-at-home-how-good-are-they/ [Accessed 6 Jan. 2025].

Soerjomataram, I., Bray, F., Stewart, B.W., Elisabete Weiderpass and Wild, C.P. (2020). Global trends in cancer incidence and mortality. [online] Nih.gov. Available at: https://www.ncbi.nlm.nih.gov/books/NBK606460/ [Accessed 7 Jan. 2025].

You, Y. and Gui, X. (2021). Self-Diagnosis through AI-enabled Chatbot-based Symptom Checkers: User Experiences and Design Considerations. AMIA … Annual Symposium proceedings. AMIA Symposium, [online] 2020, pp.1354–1363. Available at: https://pubmed.ncbi.nlm.nih.gov/33936512/ [Accessed 21 Oct. 2024].