INTRODUCTION:
Artificial intelligence is bringing a change in the field of medical science through evolution that enhances diagnosis, treatment, and patient care. AI can process large volumes of data and identify similar patterns. It provides a modification on how healthcare professionals approach disease detection, drug discovery, and personalized medicines. AI has been remarkable in improving healthcare efficiency, detecting early diseases, providing solutions, and reducing medical errors. 4.5 billion people lack access to essential healthcare services, and the gap can be bridged with the help of AI. It helps providers make difficult decisions about treatments and mental health care. AI tools can be used to examine CT scans, X-rays, MRIs, and other findings that humans might miss.
AI AS DISEASE DIAGNOSIS AND EARLY DETECTION:
AI programs interpret medical images, patient information, and genetic data to ascertain diseases earlier and in a more accurate manner than traditional methods. Machine learning models are trained on large datasets to identify subtle prognostic patterns that can be missed by human experts, which enables a faster diagnosis, earlier intervention, and improvement in patient outcomes.
AI-powered tools were developed for numerous conditions, such as cancer, cardiovascular diseases, and neurological disorders. AI dermatological tools detect early signs of cancers with higher accuracy than human diagnosis, presenting them with immediate feedback to patients and doctors, giving them enough time to take measurable actions to cure the underlying cause.
AI-driven mammography has improved its analysis exceptionally in detecting the early signs of breast cancer, which typically cannot be diagnosed until stage 1, but AI has made it possible for breast cancer to be recognized at an earlier stage, reducing the false negatives and enabling early-stage treatments, increasing survival rates.[1].
- Case Study: An AI model developed by Google DeepMind, which was capable of detecting breast cancer with higher precision than human radiologists. Equivalently, based on clinical data, IBM Watson Oncology assists doctors in recognizing optimal treatment plans for cancer patients[2].
- AI in COVID-19 Outbreak: AI models were significant in estimating the spread of COVID-19, and also analyzed medical images for faster diagnosis, and reprocessing drugs for treatment. COVID-induced pneumonia was analysed with the help of an AI-driven CT scan.[3]
SURGERIES WITH AI:
Robotic surgical systems, which are enhanced with the capabilities of AI, provide greater precision, reducing interference and improving the recovery times of patients. AI helps surgeons in pre-surgery planning, surgical guidance, and post-surgery monitoring. AI systems reduce human errors and make sure that even complex surgeries are organized with utmost precision.
The da Vinci is a surgical system that is an AI-powered robotic assistant that has increased the precision of the surgery process, specifically in the fields of urology and cardiothoracic surgeries. Such robotics also enabled remote surgeries, giving access to specialized care. With the help of AI, robotic systems are capable of performing microsurgeries, and they do so accurately that they can work on individual cells, which could be a great change and a chance of development in the future of medicine and medical science.[4].
DRUG DISCOVERY AND MEDICINE WITH AI
AI is bringing a change in drug discovery and personalized medicine approaches. AI analyses vast amounts of molecular, clinical data, and gene research to identify prospective drug targets and understand the efficiency of the drug, increasing the process of drug development and reducing the overall costs. It predicts drug response through huge datasets and identifies promising compounds faster than any traditional way.
Companies like DeepMind have structured an AI program, AlphaFold, that predicts protein structures and increases drug discovery for diseases like Alzheimer’s and cancer. It analyses the vast databases of genetic mutations and, with the help, it, develops therapies for the targeted patients that improve the survival rates. AI technologies tailor the treatments according to the needs of individual patients based on their genetic profiles, lifestyle, and medical history. Individual analyses help in identifying the type of disease and the best medicine available to cure such a disease, which increases the life span of the person[5]. AI is also used in reprocessing existing drugs, used during the COVID-19 pandemic.
RADIOLOGY AND MEDICAL IMAGING WITH AI:
AI in radiology and medical imaging enhances the quality of images, automating image analysis, and improving diagnosis and its accuracy. Models such as Deep Learning detect abnormalities in X-rays, MRIs, and CT scans with higher accuracy, and assist radiologists in identifying upcoming issues faster and in a more precise manner.
Image Reconstruction techniques powered by AI also reduce radiation exposure through the procedure, making it safer for the patients. There was superior accuracy in detecting pneumonia from chest X-rays by AI algorithms. It also detected early signs of Alzheimer’s disease and brain tumours when used in neuroimaging.
- AI in data privacy: The case of United States v. Microsoft Corp. (2020) highlights the importance of AI compliance in healthcare data privacy.
- AI in stroke Diagnosis, Qure.ai and Westchester Medical Center: It highlighted how AI mitigates diagnostic errors in stroke cases, showing accuracy and efficiency[6].
- ai in Pediatric Radiology: AI solutions assisted in detecting abnormalities in chest X-rays in children[7].
- AQUARIUS for Quality Assurance: Re-defining Radiology Quality Assurance with AI-based AQUARIUS automatically detects intracranial haemorrhages, and reduces human errors[8].
AI IN HEALTHCARE MANAGEMENT AND RESOURCE OPTIMIZATION:
AI systems are introduced to enhance healthcare management and resource allocation. AI machines can predict patient admission rates, optimize staff schedules, and maintain supply chain management in healthcare facilities.
AI analysis helps healthcare providers foresee and prepare for potential diseases or resource scarcity, which overall brings an improvement in the healthcare system. AI virtual assistants are also deployed to handle routine queries of patients, which gives extra time to medical professionals to have their attention on more complex cases.
Hospitals that use AI analysis are reported to have reductions in the waiting time of patients and improved efficiency in maintaining ICU beds during the peak period of admissions. University Hospital in Queensland implemented AI solutions that addressed a backlog of 54,000 medical scans. By incorporating AI, hospitals improved the speed and accuracy of scan interpretations and enhanced patient outcomes.
In India, Apollo Hospitals has increased its investments in AI to relieve the workload of medical staff by giving routine tasks to AI, such as medical documentation.[9]. Palantir partnered with Mount Sinai and the Cleveland Clinic to use AI in revenue cycle management, reducing workforce and planning patient capacity, which resulted in cost savings and improvement in patient care.
MENTAL HEALTH AND TELEMEDICINE WITH AI:
AI applications are taking their place in mental healthcare and telemedicine. Natural language processing of AI can analyze patient conversations, which helps in detecting the early signs of underlying mental health issues. Virtual therapies and chatbots powered by AI provide the initial treatment and mental support for the patients, overall improving their access to psychological care.
Woebot and Wysa are AI-driven bots that provide patients with emotional support and cognitive behavioural therapy (CBT). Speech tools of AI can detect early depression and anxiety through analysis of voice tone and speech patterns. In Telemedicine, AI helps in monitoring patients remotely, analyzing symptoms, and recommending treatment options, which overall improves access to healthcare in remote areas. AI has launched its wearable devices that track mental healthcare indicators, which help provide real-time intervention for individuals at risk[10].
CHALLENGES AND ETHICAL INSPECTION IN AI:
AI providing ease of access in the areas of medical science is not less than a miracle, but with such tools comes great challenges and responsibilities. The integration of AI in medicine raised ethical considerations and challenges, including:
- Data Privacy and Security: Maintaining patient data, ensuring its confidentiality, and preventing unauthorized access is a task. AI systems are required to comply with global health data privacy laws such as HIPAA and GDPR.
- Algorithmic Bias: Addressing bias in AI models to make sure there is fair and equitable healthcare delivery. AI models that are trained without supervised datasets may produce uneven results, leading to disparities in patient outcomes.
- Transparency: There is a need for explainable AI to ensure that the AI decisions are understandable and justifiable, and also ensure that the professionals and patients trust the AI-generated diagnosis and treatment recommended by it[11].
- Legal Challenge: Ensuring there are policies to govern AI’s role in healthcare. Governments should establish clear frameworks and regulations to handle AI accountability and liability.
WHO issued six guidelines to ensure AI works for the public interest in all countries. Those are to protect human autonomy, promoting human well-being, safety, and public interest, ensuring transparency, intelligibility, and explainability, fostering responsibility and accountability, ensuring equity, promoting AI that is responsive and sustainable.[12].
CONCLUSION:
AI in medicine holds huge potential to bring an improvement in healthcare delivery, patient outcomes, and medical research. However, it is important to use this integration with caution. The need for human oversight and ethical and data privacy concerns must be kept in mind and addressed. Establishing the reliability, transparency, and comprehensibility of such AI systems is supreme. With the use of proper regulations, innovations, and responsible implementations, AI can become a requisite asset in the future of medical science.
Author(s) Name: Kirti Bansal (Karnataka State Law University/ ISBR Law College)
References:
[1] Google Health, ‘Artificial Intelligence Could Help Spot Breast Cancer’ (Google Health,2020) https://blog.google/technology/health/improving-breast-cancer-screening/ accessed on 7 April, 2025.
[2] IBM, ‘Watson for Oncology Overview’ (IBM,2020) https://research.ibm.com/publications/watson-for-oncology-and-breast-cancer-treatment-recommendations-agreement-with-an-expert-multidisciplinary-tumor-board accessed on 7 April 2025.
[3] Y Song and others, ‘Deep Leaning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images’ (2021) 12 IEEE/ACM Transactions on Computational Biology and Bioinformatics 1 https://research-repository.griffith.edu.au/items/26b5ff5e-6bdb-41a6-b872-955b6b306df7 accessed on 7 April 2025.
[4] Boston Engineering, ‘Breakthrough in Robotic Precision at the Cellular Level’ (Boston Engineering, 2024) https://blog.boston-engineering.com/robotic-precision-cellular-level accessed 8 April 2025.
[5] Laboratorios Rubió, The Role of Artificial Intelligence in Personalized Medicine’ (Laboratorios Rubió, 2024) https://www.laboratoriosrubio.com/en/ai-personalized-medicine/ accessed on 8 April 2025.
[6] Qure.ai, ‘The Imperative of AI for Improving Radiological Accuracy’ (Qure.ai, 2024) https://www.qure.ai/blog/the-imperative-of-ai-for-improving-radiological-accuracy accessed on 8 April 2025.
[7] Annalise.ai, ‘Case Studies in AI-assisted Radiology’ (Annalise.ai, 2024) https://annalise.ai/case-studies/ accessed on 8 April 2025
[8] R Tiwari and others, ‘AQUARIUS: An AI System for Radiology Quality Assurance in Intracranial Haemorrhaged Detection’ (2022) arXiv https://arxiv.org/abs/2205.00629 accessed on 8 April 2025.
[9] Reuters, ‘India’s Apollo Hospitals Bets on AI to Tackle Staff Workload’ (Reuters, 13 March 2025) https://www.reuters.com/business/healthcare-pharmaceuticals/indias-apollo-hospitals-bets-ai-tackle-staff-workload-2025-03-13/ accessed on 9 April 2025.
[10] Fitzpatrick KK, Darcy A and Vierhile M, ‘Delivering Cognitive Behaviour Therapy to Young Adults with Symptoms of Depression and Anxiety using a Fully Automated Conversational Agent (Woebot): A Randomized Controlled Trial’ (2017) JMR Mental Healthcare 4(2) https://mental.jmir.org/2017/2/e19 accessed on 9 April 2025.
[11] European Commission, ‘Ethics guidelines for Trustworthy AI’ (European Union, 2019) https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai accessed on April 9 2025.
[12] World Health Organization, WHO Issues First Global Report on Artificial Intelligence (AI) in Health and Six Guiding Principles for its Design and Use (WHO, 28 June 2021) https://www.who.int/news/item/28-06-2021-who-issues-first-global-report-on-ai-in-health-and-six-guiding-principles-for-its-design-and-use accessed on 9 April 2025.
