AI Uses in healthcare

AI in healthcare
Prediction or Precaution for Death

Like every other field ai is also transforming global Healthcare, moving from sci-fi to Present day in clinics, homes and hospitals. The last two years have seen surge in AI adoption, driven by mature digital infrastructure post-COVID, smarter and more affordable AI models, and regulatory frameworks. This has created Fertile ground for high-impact projects from both startups and established firms.

Market Evaluation:
The global AI market in healthcare is valued at approximately $ 19-27 billion in 2024 and is projected to expand to approximately $ 180-600 billion by 2030-2034. Data proliferation, administrative demands and significant medical breakthroughs fuel this growth.

Reasons:
 Key trends include a sharp increase in research funding, cross-sector partnerships between tech companies and healthcare institutions, and Support by governments of U.S. and EU to ensure AI is deployed safely and effectively.

AI Diagnosis:
systems for image analysis have been adopted rapidly, particularly in radiology. As of Aug 2024, the FDA had cleared approximately 950 AI/ML-enabled medical devices, with 221 of those approvals occurring in 2023 alone. Over 75% of these tools are designed for imaging to improve scan quality, optimize radiation dosage, or flag potential issues for clinicians.

• Radiology and Imaging: Tools like Siemens Healthineers’ AI-Rad Companion assist with CT and MRI interpretation, while Digital Diagnostics’ LumineticsCore automatically grades diabetic retinopathy from retinal images. A study of 260,739 women found that AI-assisted mammography improved breast cancer detection by 17.6% and reduced patient recalls. Startups such as Aidoc and RapidAI are deploying targeted solutions for stroke triage and pulmonary embolism.
• Pathology: AI is also making strides in pathology. The FDA granted its first “breakthrough device” designation to Paige’s PanCancer Detect, an AI capable of flagging cancerous tissue across multiple organs.
• Consumer Technology: The Apple Watch’s atrial fibrillation history feature was qualified by the FDA for use in clinical trials, signaling growing confidence in consumer wearables as medical-grade tools.

 

Drug Discovery and Development

AI is fundamentally reshaping pharmaceutical research and development through a series of recent "megadeals" between major drug companies and AI biotech firms.

• Major Partnerships: AstraZeneca signed a deal worth over $5 billion with China's CSPC Pharmaceutical for access to its AI drug-design platform. Pfizer expanded its collaboration with AI-drug firm XtalPi, and Sanofi entered a $1.7 billion partnership with AI startup Earendil Labs to license AI-generated antibody drug candidates. Isomorphic Labs, a DeepMind spin-off, has partnerships with Eli Lilly and Novartis valued at over $3 billion.linkedin
• Accelerated Timelines: AI is dramatically shortening the drug discovery timeline. Rentosertib, the first fully AI-designed drug for idiopathic pulmonary fibrosis, entered mid-stage clinical trials in under 30 months. Companies like Exscientia and Insilico Medicine have also moved their first AI-designed drug candidates into human trials.
• Economic Impact: Analysts estimate that AI-driven R&D could unlock $15–$28 billion in annual value by optimizing drug targets and simulating clinical trials.

Clinical Applications and Support:

Beyond diagnostics, AI is being integrated into clinical workflows to support healthcare professionals and improve patient care.

Support: This involves providing technical, functional, and user assistance to ensure these applications run efficiently, remain updated, and are correctly used by doctors, nurses, and other healthcare staff.

• Clinical Decision Support (CDS): EHR-integrated models provide early warnings for conditions like sepsis or predict hospital readmission risk. However, regulators emphasize that AI should augment, not replace, a clinician's judgment.
• AI Consultations: The free, anonymous AI diagnosis platform Doctronic has already conducted over 10 million chat-based consultations, offering up to four possible diagnoses with HIPAA-compliant data handling.
• AI Physiotherapy: In the UK, Flok Health, an AI-run physiotherapy clinic, halved wait times for back pain treatment in an NHS pilot, cutting a 12-week backlog by 44% and saving 856 clinician hours per month.
• Space Medicine: NASA and Google developed the CMO-DA (Crew Medical Officer Digital Assistant), an AI that can diagnose medical issues autonomously, which is crucial for long-duration space missions and has potential for use on Earth.

 

Patient Monitoring and Wearables:

AI powered patient monitoring has expanded from hospitals to homes, leveraging wearables, smart sensors, and telehealth apps.

• Wearable Technology: In May 2024, the FDA qualified the Apple Watch's atrial fibrillation (AFib) history feature as a digital endpoint for clinical trials, a major milestone for wearables in medicine. The UK’s MICA wearable uses large language models to collect health data and communicate a user's status to caregivers.medtechdive
• Smart Textiles and Remote Monitoring: Smart clothing and patches use AI to detect falls or respiratory events. In "hospital-at-home" models, AI algorithms analyze data from remote sensors to predict events like heart failure exacerbation.
• Telemedicine: Conversational agents like Babylon and Ada Health use natural language processing to gather patient symptoms and offer guidance.

Regulatory and Ethical Landscape:

As AI becomes more integrated into healthcare, a new regulatory and ethical framework is emerging to ensure its safe and equitable deployment.

• New Regulations: In August 2024, the EU’s landmark Artificial Intelligence Act came into force, classifying most healthcare AI as "high-risk" and imposing strict requirements for risk mitigation, data quality, transparency, and human oversight. Similarly, in the U.S., the FDA is updating its framework for AI/ML-based software, and the Office of the National Coordinator for Health Information Technology (ONC) has proposed standards for AI transparency.sciencedirect+8
• Ethical Concerns: Experts warn that an over-reliance on AI could lead to an erosion of clinicians' diagnostic skills, with one study suggesting a potential reduction of up to 20% in tumor diagnosis ability. Additionally, tools like Harvard’s FaceAge, which estimate biological age and disease risk from photos, raise ethical concerns about potential misuse. To avoid bias, it is crucial that AI models are trained on diverse and representative data.