Of the many industries witnessing AI-led transformation, healthcare happens to be one. Today, AI’s promise in healthcare extends beyond automation-led operational and administrative efficiencies. It’s beginning to show a measurable impact directly on patient care and diagnosis. The AI in healthcare industry is predicted to reach $187.95 billion by 2030.
Today, technology companies of all sizes are conducting AI-related research and development in healthcare. From IBM Watson’s diabetes and cancer management, treatment, and drug development to Google’s Deep Mind’s use of virtual medical assistants, AI is beginning to make a tangible impact on healthcare.
Below, I outline a few different ways in which AI is reshaping the healthcare diagnostics and treatment processes, and how it is empowering physicians with better accuracy and efficiency.
1. Diagnosis Automation: The integration of AI technologies like image recognition and computer vision, along with advanced data analytics, can enhance the early detection of certain abnormalities, such as the detection of cancerous cells. When a patient presents myriad symptoms that could indicate a host of potential diagnoses, AI systems can quickly look through various datasets, such as device recordings, imaging scans, bloodwork, and through advanced analytics, thus enabling doctors to identify anomalies sooner than manual and conventional methods.
2. Personalized Treatment: AI can be a game-changer in delivering precise and personalized treatment plans to patients based on their unique genetic makeup, lifestyle, and different environmental factors. AI algorithms can more efficiently sift through vast amounts of an individual patient’s data – including genetic information, medical records, imaging scans, and lifestyle factors, to identify patterns and correlations that human analysis might miss. By analyzing this data, AI can help healthcare providers make more accurate personalized treatment plans for their patients, resulting in valuable outcomes and improved patient satisfaction instead of relying on population-based treatment approaches.
3. Robotic Surgeries: Today, AI-powered systems, like John Hopkins University’s STAR robot, are augmenting surgical procedures by surpassing human capabilities in tasks such as suturing and knot-tying. It’s, therefore, no wonder that the market for robot-assisted surgery alone is predicted to be worth $40 billion by 2026.
While AI systems trained to perform surgical procedures cannot yet be fully relied upon, they can be trusted to reduce human errors. AI algorithms can analyze pre-operative imaging scans such as MRI, CT, ultrasound, etc. to create detailed 3D models of the patient’s anatomy. During surgery, such models help an AI-powered surgical system to accurately identify important structures and reduce the risk of damage to a patient. Similarly, AI algorithms integrated into robotic surgical systems can provide real-time feedback and guidance to surgeons during procedures.
By analyzing a patient’s entire host of physiological parameters, AI can help surgeons make informed decisions and adjust their approach as needed. Besides real-time surgical assistance, AI can also guide remote surgeries by allowing expert surgeons to perform certain procedures from a distance. This is particularly valuable for providing specialized care to patients in remote or underserved areas. These robots can also be valuable to help create new learning and training opportunities.
4. Drug Research and Development: Developing drugs, which remains a time-consuming and expensive process, can benefit significantly from AI. AI can assist researchers with clinical trials by selecting the best candidates or analyzing vast amounts of data. Similarly, AI algorithms can predict how potential drug compounds will interact with biological targets in the body. This enables researchers to identify promising drug candidates more efficiently, potentially speeding up the development of new treatments for a wide range of diseases. AI can, similarly, also help with the drug approval process by helping researchers take into account all the requirements and checklist items.
5. Remote Monitoring and Telemedicine: AI-powered remote monitoring systems are continuously collecting and analyzing patient data, allowing healthcare providers to monitor patients’ health status in real-time and intervene promptly, as needed. This is particularly valuable for patients with chronic conditions who require ongoing monitoring and management.
6. Virtual Health Assistants: Similarly, AI-enabled virtual health assistants can help save doctors and healthcare providers time and resources needed to address some common and repetitive questions. AI-powered chatbots can provide personalized health advice, answer medical queries, schedule appointments, and remind patients to take medication, improving patient engagement and adherence to treatment plans.
Ultimately, AI models in healthcare can only thrive when they’re fed a variety of historical data, including patient medical records, diagnostic images, treatment outcomes, and more. This comprehensive dataset provides the necessary context for AI algorithms to make informed decisions.
The quality of AI-powered healthcare depends on both the quality and quantity of this data – that extends beyond patient data. Without robust training data, AI systems may produce unreliable results or even propagate biases present in the data. Therefore, efforts to curate high-quality datasets and maintain data integrity are critical for the success of AI applications in healthcare.
Simultaneously, AI systems should be viewed as powerful tools that complement the expertise of doctors and healthcare professionals rather than substitutes for human experience. While AI can analyze vast amounts of data and identify patterns that might escape human observation, it lacks the empathy, creativity, and contextual understanding that are essential components of patient care. Thus, the ideal scenario is one where AI augments human decision-making, providing insights and recommendations that enhance the quality and efficiency of healthcare delivery.
About Dr. Sameer Maskey
Dr. Sameer Maskey is the founder / CEO of Fusemachines and AI professor at Columbia University. After graduating Bates College and obtaining his PhD in computer science at Columbia, Dr. Maskey led pioneering AI research at IBM Watson and then founded Fusemachines, an AI company that provides AI products and solutions for various industries.
Originating from Kathmandu, Nepal, his educational journey took him to Bates College in the USA, where he obtained degrees in Math and Physics culminating with PhD in Computer Science in AI/ML from Columbia University. A prolific researcher, Dr. Maskey has authored over 20 papers published in international conferences and journals, accompanied by 10+ pending/granted patents.