# data[].attributes.body
I chose healthcare because it is the field that I am going into as a future healthcare provider. Personally my life will affected by the use of AI in medical care and its important that future healthcare providers know how to leverage it. Healthcare is now a data-driven field, and AI bridges the gap between massive datasets and actionable care decisions.

This Padlet explores three major applications of AI: diagnostic imaging, personalized medicine, and remote patient monitoring. Together, they illustrate how machine learning, computer vision, and predictive analytics are already shaping the patient experience.

What drew me to this topic is AI’s balance between hope and risk. While it can detect cancer earlier and monitor chronic conditions in real time, it also raises ethical questions about data privacy, bias in training sets, and the limits of algorithmic decision-making. Understanding these trade-offs is vital for anyone planning a career in healthcare.

AI is not the future—it’s the present, embedded in hospital systems, wearable devices, and genomic analysis. The goal of this project is to show how these tools are revolutionizing medicine while prompting us to rethink trust, responsibility, and empathy in an AI-assisted clinical world.

# data[].attributes.body
Artificial intelligence is transforming medical diagnostics by improving how doctors interpret imaging data. In Dr. Rohan Khera’s Yale lab, AI trained on thousands of cardiovascular scans detects subtle abnormalities invisible to the human eye. Using machine learning and computer vision, it helps identify early signs of heart disease and other conditions without invasive procedures. This speeds up diagnosis, reduces costs, and expands access to expert-level care. Still, challenges remain, AI can inherit data bias, raise privacy concerns, and always requires human oversight to verify results.

# data[].attributes.body
AI is pushing healthcare toward true personalized medicine. Sensors and wearables stream biomarkers like brain signals and sweat chemistry; machine-learning models fuse that data with images and history to flag risk earlier and tailor care. In epilepsy, adaptive algorithms learn a patient’s neural patterns to predict seizures and trigger targeted stimulation before symptoms start. For paralysis, brain-machine interfaces decode intention so users can control robotic limbs or cursors more smoothly. Crucially, efficient “edge” chips bring these models onto tiny implants, improving speed and privacy. The result: care that’s proactive, precise, and personal—designed for your biology, not the average patient today.

# data[].attributes.body
AI-powered chatbots such as Woebot, Replika, and Wysa are reshaping how people access mental-health care. The Firstpost video “Are You Using AI for Mental Health & Therapy? Think Again” highlights both promise and danger: these tools offer instant, judgment-free emotional support, yet they only simulate empathy. Algorithms predict comforting responses but can miss signs of crisis or store sensitive data insecurely. While millions turn to them for affordability and privacy, experts warn that unregulated use risks misinformation and emotional harm. True empathy still requires a human therapist, but AI chatbots can serve as helpful supplements when carefully monitored.

# data[].attributes.attachment_link.title
Are You Using AI for Mental Health & Therapy? Think Again | Vantage on Firstpost

# data[].attributes.headline
Future Trends and Ethical Considerations

# data[].attributes.subject
Future Trends and Ethical Considerations

# data[].attributes.body
Artificial intelligence will continue to redefine healthcare through precision medicine, real-time diagnostics, and autonomous decision support. Advances in multimodal AI models will integrate imaging, genomic, and behavioral data to predict disease before symptoms appear, while generative models could simulate personalized drug responses and speed clinical trials. Hospitals are moving toward AI-driven command centers that allocate resources and predict patient flow. Yet, with these gains come deeper ethical dilemmas. Training data often reflect existing health inequities, meaning algorithmic bias can amplify disparities in diagnosis and treatment across race, gender, or socioeconomic lines. The growing use of patient data by private firms also raises questions of consent, ownership, and commercial use. Transparency and explainability will be critical—clinicians and patients must understand why an AI makes a recommendation. Regulators like the FDA are developing frameworks for “adaptive” algorithms that evolve after deployment, but accountability remains unclear. The future of healthcare AI demands collaboration among engineers, ethicists, and clinicians to ensure technology augments, not replaces, human judgment. Personally, I find this intersection inspiring yet cautionary—it represents medicine’s next frontier, but one that must be guided by empathy, equity, and ethical oversight.

# data[].attributes.attachment_link.title
AI in healthcare Future Trends and Ethical Considerations - Google Search

# data[].attributes.body
AI’s influence on society through healthcare is already profound. Early-diagnostic tools and wearable monitoring devices expand access to preventive care, empowering patients to manage chronic conditions and live longer, healthier lives. Rural and underserved regions benefit from AI-enabled telemedicine and image-analysis systems that bring specialist expertise to areas lacking doctors. However, these same systems risk creating a new form of digital divide: populations without internet access, data literacy, or affordable devices may be left behind. Automation also changes workforce dynamics—radiologists, pathologists, and even scribes must now learn to collaborate with AI rather than compete against it. On the societal level, large-scale health data collection fuels public-health research but heightens concerns about surveillance and misuse. Privacy scandals or biased outcomes could erode public trust in medicine, slowing adoption of otherwise life-saving tools. In my view, society must approach healthcare AI as a shared public good: its benefits should extend beyond profit and convenience to equitable health outcomes. Education, transparency, and ethical regulation are essential to make AI an ally rather than an amplifier of inequality.

# data[].attributes.body
Researching this topic showed me how quickly AI is transforming patient care and how complex the ethical landscape has become. I learned that while AI offers speed, accuracy, and personalization, it also magnifies issues of bias, privacy, and trust. Gathering credible sources and videos helped me understand both the promise and the risk behind each innovation. The biggest challenge was balancing optimism with realism—AI is not a cure-all but a tool that depends on human oversight. This project reinforced my motivation to enter healthcare prepared to use AI responsibly and advocate for patient-centered design.

# data[].attributes.body
Khera, R. (2025). How AI is Revolutionizing Medicine [Video]. Bloomberg Originals. https://www.youtube.com/watch?v=gJQnk6nZUTQ
Rationale: Demonstrates diagnostic imaging breakthroughs relevant to AI in cardiology and radiology.

Caltech. (2023). AI for Personalized Medicine [Video]. https://www.youtube.com/watch?v=KO1G9lbFKe0
Rationale: Explains personalized-medicine applications such as seizure detection and brain–machine interfaces.

Firstpost. (2025). Are You Using AI for Mental Health & Therapy? Think Again [Video]. https://youtu.be/pfbiHmRPmV8
Rationale: Explores benefits and ethical risks of AI chatbots for emotional support.

Topol, E. J. (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books.
Rationale: Provides foundational context on how AI can enhance empathy and clinical decision-making.

U.S. Food and Drug Administration. (2024). Proposed Framework for Modifications to AI/ML-Based Medical Devices. https://www.fda.gov
Rationale: Details regulatory efforts guiding adaptive healthcare algorithms.