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Healing Healthcare: Uses AI to Target the Root Cause of the Healthcare Industry’s Troubles

Updated: Jul 1, 2023

AI has become an indispensable tool in the healthcare industry, utilizing data and machine learning algorithms to enhance various healthcare tasks. By harnessing the power of AI, doctors and pharma can improve diagnosis, treatment, and research, leading to better patient outcomes. However, adopting AI also creates risks that require careful consideration. leverages AI technology to solve healthcare’s core challenge of disconnection between healthcare organizations while mitigating risks through responsible model guardrails.

The Benefits of AI in Healthcare

AI offers significant advantages in healthcare, enhancing the quality, accessibility, and affordability of various healthcare processes. Algorithms can improve diagnosis by detecting diseases, predicting risks, and recommending treatments. They enhance treatment by monitoring and managing chronic conditions, delivering personalized medicine, and improving adherence and compliance. Additionally, AI and digital technology expedite research by accelerating drug discovery, development, testing, and identifying new targets and biomarkers. They streamline management by automating and optimizing processes, reducing costs, increasing efficiency, and ensuring regulatory compliance. Moreover, these technologies enhance engagement by utilizing apps, voice assistants, and chatbots to communicate with patients, provide information, support decision-making, and collect valuable feedback. Notably, Accenture projects that AI applications in healthcare could save up to $150 billion annually in the US health system by 2026.

The Challenges of AI in Healthcare

While AI offers immense potential, it also introduces challenges and risks that must be effectively addressed and managed. Ethical concerns arise regarding fair use and algorithmic safety. Regulatory issues stem from the uncertainty surrounding the standards governing its use in healthcare. Data privacy risks emerge from the potential exposure or misuse of personal and sensitive information collected and processed by these technologies. Security challenges arise from vulnerabilities to attacks on the systems and devices employed in healthcare. Quality issues may occur due to errors or failures in these algorithms and devices. Talent shortages and competition for skilled professionals pose additional challenges. Reputation issues and trust deficits can undermine confidence and credibility in these technologies. All these issues compound and make paramount the growth of teams with the ability to design safe, secure, and compliant health AI models.

Examples of AI Initiatives

Key healthcare stakeholders have implemented numerous AI initiatives to enhance patient outcomes and experiences. These initiatives include:

  1. Patient-centric Apps: Applications that enable patients to access their health records, test results, prescriptions, and appointments, empowering them to take an active role in their healthcare journey.

  2. Conversational AI: Voice assistants or chatbots employ conversational AI to engage with patients, provide information, support decision-making, and collect valuable feedback to improve services.

  3. Remote Monitoring and Alerts: Sensors and apps that alert for medical interventions by tracking vital signs, symptoms, and behaviors of patients with chronic diseases.

  4. Personalized Medication Management: Devices and platforms that tailor medication dosage and types to individual patient characteristics and responses, optimizing treatment outcomes.

  5. Data-Driven Research: Data analytics and AI-driven approaches that accelerate clinical trials, aid in participant selection, and identify new insights into disease causes, mechanisms, and potential therapeutic options.

  6. Robotic Process Automation (RPA): Automating repetitive or routine tasks such as data entry, verification, and claims processing through the use of RPA, freeing up human resources for more complex and critical responsibilities Uses AI to Solve Healthcare's Biggest Challenges has emerged as a leading healthcare platform that leverages AI to connect industry stakeholders. Equipped with AI algorithms developed by data scientists from Harvard and MIT, revolutionizes the processing of treatment request claims for insurance companies. Its advanced machine learning algorithms, including natural language processing (NLP) and large language models (LLMs), enable efficient and accurate handling of requests across varying complexity levels. From rule-based automatic approvals to advanced techniques for interpreting and annotating ambiguous requests, streamlines processing before passing complex queries to administrators for review.

Furthermore, extends its capabilities beyond request processing by incorporating computer vision technology. This technology aids doctors in identifying disease progression from medical images, facilitating precise and informed decision-making. Additionally,'s chatbot, trained on extensive medical data, guides users seamlessly through administrative workflows, enhancing overall user experience and support.

The effectiveness and impact of's computer vision algorithm have earned the company recognition, leading to its invitation to present at the esteemed American Diabetes Association's 83rd Scientific Sessions. This recognition further validates's commitment to leveraging advanced AI technologies to drive innovation in healthcare and improve patient outcomes. As members of basys' team contributed to the creation of the Affordable Care Act, the company holds a deep understanding of healthcare policy and regulations. basys’ data scientists reflect this expertise in their responsible model training guardrails, aligning with national health data policy and protecting data security.

AI presents immense opportunities for healthcare professionals to improve patient outcomes and experiences while boosting efficiency and profitability. By leveraging these technologies, healthcare stakeholders can enhance diagnosis, treatment, and research. It is also important to address the privacy, security, and quality issues of healthcare AI. Stakeholders must prioritize the strategic adoption of responsible AI to gain a competitive edge in the ever-evolving healthcare landscape. is a prime example of an AI healthtech leader, addressing these challenges and transforming the industry models.

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