AI Decoded: AI in Healthcare & Biotechnology (Part 8)

1. AI in Drug Discovery

Deep learning models now predict molecular properties and streamline compound screening, boosting Phase I success rates to 80–90%, far above traditional averages 0.

Source: Paging Dr. Fran on TikTok 1

2. AI in Genomics

Explainable AI techniques are uncovering regulatory regions and enhancer dynamics in cancer genomes, improving transparency and trust in genomic predictions 2.

Source: @radnetimaging on Instagram 3

3. AI‑Driven Personalized Medicine

Deep Generative Models (DGMs) are producing high-quality synthetic clinical data, overcoming privacy hurdles and enabling tailored treatment plans in oncology and rare diseases 4.

Source: Medtronic on Facebook 5

4. Ethical Considerations

Generative AI in clinical settings raises concerns around consent, bias, and explainability. Ethical frameworks recommend transparency, human oversight, and robust validation to mitigate risks 6.

5. Emerging Trends: Telemedicine & AI Chatbots

AI-powered telehealth platforms and chatbots now handle up to 30% of routine triage, reducing clinician workload and improving access—especially in remote regions 7.

Warning: Deepfake “doctors” promoting misinformation have surged on TikTok, urging users to verify credentials and watch for unnatural lip movements 8.

Coming in Part 9: AI in Finance & Economics

• AI‑driven financial forecasting & investment strategies
• Algorithmic trading and risk assessment
• Fraud detection with machine learning