Opportunities and Challenges of AI in Healthcare (CCAIM Module 2)

A comprehensive set of Q&A flashcards covering the opportunities, examples, challenges, ethical issues, mitigation strategies, and future directions of Artificial Intelligence in Healthcare as presented in CCAIM Module 2.

What three factors are driving the rapid adoption of AI in healthcare?

Rapid progress of algorithms, increased compute power, and growing volumes of data.

Name two major promises of AI for the healthcare sector.

Improved diagnostics & operational efficiency, and personalised care delivered at scale.

According to Wubineh et al. (2024), why is it necessary to pre-empt challenges when adopting AI in healthcare?

Because addressing challenges early is essential to fully realise AI’s potential benefits and avoid harms.

How does AI improve diagnosis and clinical outcomes?

By performing fast, accurate pattern-recognition on multimodal data—such as EHRs, imaging and genomics—to enable earlier intervention.

Which Indian start-up uses thermal imaging plus AI for early breast-cancer detection, and what are its key advantages?

NIRAMAI; it is radiation-free, painless, inexpensive, and effective even for dense breast tissue.

List four key AI contributions to drug discovery and development.

Virtual screening of compound–target interactions, novel target identification, rapid lead optimisation & ADMET prediction, and drug repurposing discovery.

How does AI reduce attrition in the traditional drug-development pipeline?

By selecting more promising molecules earlier, predicting toxicity/efficacy, and shortening each phase, thus lowering failures and time to market.

Which company initiated a first-in-human study of ISM001-055 discovered fully with an AI platform?

Insilico Medicine, using its proprietary Pharma.AI end-to-end platform.

Give three common functions of Virtual Health Assistants (VHAs).

24/7 symptom triage, medication reminders, and side-effect alerts or remote monitoring.

What service does HealthifyMe’s ‘Ria’ provide and what impact has it shown?

An AI nutritionist that offers real-time personalised diet coaching, leading to higher user engagement and growth.

How can AI support healthcare teamwork and decision-making?

By analysing large multimodal datasets, delivering evidence-based suggestions at the point of care, and generating care-gap alerts or study-eligibility invitations.

Name five future directions for AI in healthcare listed in the lecture.

Fully personalised medicine, advanced robotic surgery & rehabilitation, continuous wearable/implantable monitoring, AI-enabled mental-health conversational therapy, and epidemiologic modelling for global-health crisis prediction.

What are the five broad opportunity areas for AI identified in the systematic review (with their approximate study percentages)?

Virtual health assistants (30%), technological advancements (25%), diagnosis & patient monitoring (20%), drug development (15%), and teamwork & decision-making (10%).

List four ethical and privacy concerns associated with AI in healthcare.

Data-protection obligations, informed consent for secondary uses, algorithmic bias/fairness, and transparency or explainability of ML decisions.

Why can public and professional hype or fear around AI be problematic?

Unrealistic expectations or misconceptions foster distrust and hinder adoption of beneficial AI tools.

State three technological limitations that slow AI integration in hospitals.

Need for high-performance compute and secure storage, lack of interoperability across EHR systems, and algorithmic opacity hindering clinician confidence.

What lesson was learned from the IBM Watson Health oncology case study?

Training on limited, unrepresentative data led to unsafe recommendations and cancelled partnerships, highlighting risks of poor generalisability and over-marketing.

How can AI usage affect professional liability for physicians?

Over-reliance on incorrect AI advice may cause harm, while rejecting correct AI advice once it becomes standard could be deemed negligent.

Why are training gaps a barrier to AI adoption among healthcare workers?

Variable digital literacy means many clinicians lack confidence or skills to integrate AI tools effectively into workflows.

Name four mitigation strategies recommended for responsible AI deployment in healthcare.

Robust privacy safeguards, continuous AI literacy education, collaborative co-design with stakeholders, and adherence to ethical frameworks like WHO guidelines or the EU AI Act.

Summarise the key takeaways about AI’s role in healthcare from the module.

AI can transform diagnosis, therapeutics, operations, and population health, but success depends on addressing ethics, fairness, explainability, liability, and fostering multi-stakeholder collaboration.

What policy step is suggested to safely scale AI in healthcare systems?

Development of comprehensive governance and regulatory blueprints with risk-tiered approval and auditability.

Why are ‘sandbox’ pilot projects recommended before full AI rollout?

They allow evaluation against measurable KPIs to ensure safety, effectiveness, and user acceptance before large-scale deployment.

Which two international data-protection regulations were referenced as models for healthcare AI privacy safeguards?

HIPAA (in the United States) and GDPR (in the European Union).

What continuous education requirement is highlighted for both clinicians and the public regarding AI?

Embedding AI competencies in medical curricula, CME programs, and public awareness initiatives to maintain realistic and informed expectations.