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  • The CHART Statement: A Reporting Guideline for AI-Driven Health Advice

    The CHART Statement: A Reporting Guideline for AI-Driven Health Advice

    The CHART Statement: A Reporting Guideline for AI-Driven Health Advice

    Artificial intelligence (AI) is increasingly governing modern day healthcare. (1, 2) One of the most common AI applications can be seen in the use of large language model-driven chatbots to offer health-related advice.(3) From helping clinicians with decisions on screening to answering questions of patients on treatment and prevention, these chatbots are increasingly becoming a topic of interest. Yet the sudden increase in studies evaluating chatbot health advice underscores a critical problem. The way in which these studies are performed and reported is often incoherent, making the interpretation of results difficult, thus possibly jeopardizing patient safety.(4)

    In the year after the release of ChatGPT in late 2022, over 130 studies were published assessing chatbot health advice, but many failed to include even basic details like the chatbot model version, how prompts were created, or what standards were implemented to evaluate the quality of responses. Without such transparency, the validity of results becomes questionable, thus increasing the risk of misinterpretation or harm.(5) Identifying this concern, a group of global experts across disciplines including medicine, AI, methodology, ethics, and publishing came together to create the Chatbot Assessment Reporting Tool (CHART), a clear guidance on the expectations for systematic reporting of these studies.(4)

    The CHART statement was developed through a careful systematic review of existing studies to determine gaps, a global Delphi consensus process including over 500 stakeholders, panel discussions with about 50 experts, and pilot testing to ensure adaptability. This collaborative effort resulted in a 12-item checklist, with 39 subitems, developed to standardize the reporting of chatbot health advice studies. These items encompass every stage of a research report, from stating clearly in the title and abstract that the study is evaluating chatbot health advice, to describing the model version and its accessibility, to elucidating how prompts were obtained and what approaches were used to question the chatbot. They also highlight the need to define performance standards, elaborate assessment methods, and present results clearly, including variations from established medical evidence or the possible harmful or biased responses. Moreover, items related to open science are equally important, including disclosure of conflicts of interest, sources of funding, ethical approval, safety measures for patient data, and whether datasets and code are accessible for verification.(4)

    By supporting inclusive reporting, the CHART statement seeks to obtain trust and consistency in a fast evolving area of medical research. Transparent methods enable other researchers to reproduce findings, give clinicians and policymakers confidence in the generalizability of chatbot-generated advice, and provide journal editors and reviewers with a standardized framework for assessing overall study quality. Just as existing reporting guidelines like CONSORT for randomized controlled trials (RCTs) and STROBE for observational studies enhanced the quality of health research, CHART is expected to advance the standards of this new domain.(4)

    Notably, CHART has been created as a living guideline. Given the rapidly advancing nature of generative AI, with constantly emerging multimodal models and fine-tuned systems, the checklist will be regularly updated to maintain its relevance and robustness. Extensions are also scheduled to adapt the guideline for various study designs, such as RCTs or longitudinal cohort studies that involve chatbot interventions.(4)

    Finally, the CHART statement is more than a checklist; it is a guidance for responsibly incorporating generative AI into healthcare research. By promoting transparency, methodological precision, and accountability, it helps ensure the scientific rigor of chatbot health advice studies along with their safety, reproducibility, and importance for patients, clinicians, and the wider public.

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    References

    1. Kolbinger FR, Veldhuizen GP, Zhu J, et al. Reporting guidelines in medical artificial intelligence: a systematic review and meta-analysis. Commun Med. 2024; 4:1.
    2. Han R, Acosta JN, Shakeri Z, et al. Randomised controlled trials evaluating artificial intelligence in clinical practice: a scoping review. Lancet Digit Health. 2024; 6:e367–73.
    3. Huo B, Cacciamani GE, Collins GS, et al. Reporting standards for the use of large language model-linked chatbots for health advice. Nat Med. 2023; 29:2988.
    4. Huo B, Collins G, Chartash D, et al. and CHART Collaborative. Reporting guideline for Chatbot Health Advice studies: the CHART statement. BMC Med. 2025; 23(1):447.
    5. Huo B, Boyle A, Marfo N, et al. Large language models for chatbot health advice studies: a systematic review. JAMA Netw Open. 2025; 8:e2457879.

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  • Transforming Pharma Market Access: The Rise of Artificial Intelligence

    Transforming Pharma Market Access: The Rise of Artificial Intelligence
    Transforming Pharma Market Access: The Rise of Artificial Intelligence

    The pharmaceutical industry is amidst a profound transformation, driven by the rapid adoption of Artificial Intelligence (AI). Among its many applications, AI holds immense promise in optimizing market access, a critical stage in delivering new drugs to patients efficiently.

    One of the primary advantages AI brings to market access is its capability to enhance data analysis and insights. Traditionally reliant on human expertise, market access now benefits from AI algorithms’ automation. By automating data collection and leveraging machine learning, AI can uncover intricate patterns within vast datasets, providing deeper insights into payer behavior, treatment costs, and potential market barriers. Moreover, AI’s predictive capabilities can enable companies to simulate various market scenarios, aiding in the development of effective pricing, launch strategies, and value propositions tailored to specific markets and patient demographics.[1]

    Furthermore, AI can streamline regulatory navigation, a pivotal aspect of market access. By automating repetitive tasks and analyzing regulatory data, AI can expedite document preparation for submissions and identify potential regulatory hurdles. This empowers companies to proactively address regulatory requirements, accelerating the approval process. Additionally, AI can assist in risk assessment, enabling companies to prioritize resources and develop mitigation strategies for potential regulatory obstacles.[2]

    Pricing and reimbursement strategies also benefit from AI integration. AI can facilitate the development of value-based pricing models by analyzing real-world data to determine a drug’s true value proposition based on clinical and economic outcomes. Segmenting payers based on preferences and budget constraints allows companies to tailor pricing strategies, increasing negotiation success rates. Moreover, AI potentially predicts reimbursement likelihood from different payers, aiding in targeted market prioritization and strategy development.[3]

    Personalized patient targeting and engagement are critical for successful market access, and AI plays a pivotal role in this realm. Through data analysis, AI can identify patient populations most likely to benefit from new therapies, enabling targeted outreach and education campaigns. Real-world data analysis demonstrates a drug’s effectiveness, bolstering its value proposition to healthcare providers and patients. Additionally, AI-driven patient support programs can improve adherence and thereby improve health outcomes.[4]

    Enhanced risk management and compliance are paramount in market access, where AI offers significant assistance. By analyzing healthcare claims data, AI can detect potentially fraudulent activities and can monitor compliance with regulations, enabling timely corrective actions. Furthermore, AI can expedite adverse event identification, allowing companies to address safety concerns promptly and effectively.[5]

    Despite its immense potential, AI adoption in pharmaceutical market access faces multifaceted challenges. Ensuring data accuracy and transparency emerges as a critical hurdle, necessitating the implementation of robust data governance processes. Given the sensitivity of healthcare data, maintaining data integrity becomes paramount to mitigate risks associated with erroneous insights or decision-making. Additionally, transparency in AI algorithms becomes imperative to build trust and accountability, demanding measures to ensure algorithm explainability and fairness. Moreover, the complexity of healthcare data further complicates the challenge, requiring sophisticated data management solutions to handle diverse data sources and formats effectively.[1, 6]

    Integration with existing IT infrastructure poses another significant challenge in AI adoption. The seamless integration of AI solutions with legacy systems becomes essential to leverage the full potential of AI-driven insights. However, disparate data sources and incompatible formats often hinder this integration process, necessitating investments in data integration solutions and interoperability standards. Furthermore, the scalability and performance of AI solutions within existing infrastructure frameworks must be carefully evaluated to ensure optimal performance without disrupting existing workflows. Addressing these integration challenges effectively is crucial to harnessing AI’s transformative power in pharmaceutical market access.[1,6]

    In conclusion, AI presents a monumental opportunity to revolutionize pharmaceutical market access. By enhancing data analysis, decision-making, and overall efficiency, AI enables companies to deliver new drugs to patients faster, more effectively, and at a lower cost. Addressing challenges related to data quality, algorithm transparency, and system integration is imperative to realizing AI’s full potential in market access. Through strategic navigation of these challenges, the pharmaceutical industry can unlock AI’s transformative power, ensuring that life-saving medications reach those in need efficiently and expeditiously.

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    References

    1. Lee D, Yoon SN. Application of artificial intelligence-based technologies in the healthcare industry: Opportunities and challenges. International journal of environmental research and public health. 2021 Jan;18(1):271.
    2. Kumar P. Artificial Intelligence: Reshaping Life and Business. BPB Publications; 2019 Sep 19.
    3. Paul D, Sanap G, Shenoy S, et al. Artificial intelligence in drug discovery and development. Drug discovery today. 2021 Jan;26(1):80.
    4. Godman B, Fadare J, Kwon HY, et al. Evidence-based public policy making for medicines across countries: findings and implications for the future. Journal of comparative effectiveness research. 2021 May;10(12):1019-52.
    5. Al Kuwaiti A, Nazer K, Al-Reedy A, Al-Shehri S, Al-Muhanna A, Subbarayalu AV, Al Muhanna D, Al-Muhanna FA. A Review of the Role of Artificial Intelligence in Healthcare. J Pers Med. 2023 Jun 5;13(6):951.
    6. Askin S, Burkhalter D, Calado G, El Dakrouni S. Artificial intelligence applied to clinical trials: opportunities and challenges. Health and Technology. 2023 Mar;13(2):203-13.

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  • The Integration of Artificial Intelligence Solutions in Medical Affairs

    The Integration of Artificial Intelligence Solutions in Medical Affairs

    In the era of digital transformation, artificial intelligence (AI) acts as a catalyst to revolutionize the landscape of medical affairs. AI has the potential to disrupt the way a medical affairs department in a pharmaceutical company functions, by virtue of its capabilities in functions as diverse as data handling, data analysis, literature review, information retrieval, and cleaning. In fact, AI can also help medical affairs professionals focus their KOL engagement activities.

    Given the ability AI has to process vast amounts of data, uncover hidden patterns, and automate complex tasks, it is not surprising that AI has already started to transform the clinical trial data analytic landscape. Further, by the capacity to handle and analyze big data, AI has made it possible to assimilate vast amounts of real-world data (RWD) from various sources, including claims data, electronic health records (EHRs), registries, and social media as well, and to generate real-world evidence (RWE) through the analysis of RWD. This capability of handling big data has empowered medical affairs teams to discern elusive patterns and trends, thereby elevating decision-making capabilities.[1,2]

    Next, clinical trials have been known to face challenges such as prolonged recruitment times and suboptimal design. Here, AI algorithms prove instrumental, facilitating the identification of suitable patient populations, predicting enrolment rates, and optimizing protocols with efficiency and ethical considerations. This not only expedites trial completion but also fast-tracks the development of life-saving medications. [3]

    AI also plays a pivotal role in publication planning and medical writing. AI algorithms, in this context, conduct an automated literature review to identify gaps and opportunities for new publications. This ensures the informed strategic planning of publications that contribute meaningfully to scientific discourse. In medical writing, AI-powered tools enhance efficiency and contribute to the overall quality and value of publications by analyzing language patterns to meet both scientific and regulatory standards.[3]

    AI can also contribute to regulatory affairs by identifying and facilitating essential documentation. Further, it can also facilitate communication with relevant stakeholders, thereby ensuring smooth interaction between different departments leading to regulatory submission. This can ensure improved adherence to evolving guidelines and freeing resources for strategic efforts.[4]

    Moving on, AI also has an important role to play in enhancing KOL (key opinion leader) engagement strategies by helping in the refinement of communication and fostering effective dialogue between pharmaceutical companies and stakeholders. Through efficient social network analysis, AI identifies relevant KOLs, ensuring focused efforts for successful communication and market access. By using AI, it is also possible to craft targeted communication campaigns aligned with KOL expertise. AI-powered chatbots can enhance KOL engagement by providing them with on-demand access to essential information. AI can also facilitate planning and executing CMEs, matching the interest and expertise of KOLs with the CME topics. AI can further enhance the effectiveness of CMEs by aligning content with individual learning styles and preferences, ultimately contributing to the professional development of healthcare professionals and strengthening connections with influential leaders in the field.[5,6]

    Analysis of complex datasets leading to predictive modeling using AI can enable a thorough assessment of market dynamics, allowing pharmaceutical companies to make strategic decisions that optimize their market presence. AI also plays a crucial role in pricing strategies, dynamically adjusting them based on the intricate dynamics of healthcare systems, ensuring competitiveness and responsible access to innovative healthcare solutions.[7]

    Health Economics and Outcomes Research (HEOR) significantly benefits from AI’s capabilities. AI-powered systematic literature reviews can significantly shorten the time required for completion of the review, thereby enhancing the speed of market access. AI-driven models facilitate the assessment of cost-effectiveness and budget impact, empowering decision-makers with crucial insights that shape market access and reimbursement strategies.[7]

    Patient support programs are also revolutionized by AI, offering sophisticated and responsive personalized support. AI’s continuous monitoring contributes to enhanced patient adherence and outcomes, fostering a proactive approach to healthcare management for improved patient experiences and overall health outcomes.[8]

    In competitive intelligence, AI stands as a game-changer, continuously monitoring competitor activities and providing nuanced insights based on the competitive landscape. This empowers organizations to anticipate market shifts, identify strategic opportunities, and position themselves effectively within the dynamic healthcare ecosystem. Continuous monitoring of competitor activities, insights gained, and a proactive approach shaped by AI-driven analysis position pharmaceutical companies strategically in the marketplace.[8]

    AI has a role in social media analysis as well. By scanning social media for sentiment analysis, AI identifies emerging trends and unmet needs. Armed with these insights, medical affairs teams can deliver highly relevant information through tailored communication strategies, fostering deeper relationships and trust with both healthcare professionals and patients alike. Additionally, AI plays a pivotal role in forecasting drug demand, ensuring consistent supply chain logistics. This predictive capability minimizes stockouts and overstocks, contributing to a seamless healthcare system where patients have reliable access to the medications they need. Furthermore, AI facilitates communication through telemedicine platforms, breaking down geographical barriers and improving patient access to care.[9]

    Beyond the above-mentioned use cases, AI has been evolving and finding new applications to enhance the efficiency of a medical affairs department. Implementing AI solutions in medical affairs is not merely an upgrade but a strategic leap toward optimized performance and enhanced patient care. While challenges exist, careful planning, a focus on ethical considerations, and proactive change management can pave the way for successful integration and unlock the transformative potential of AI in this critical domain. By leveraging the power of AI, medical affairs teams can gain deeper insights, make data-driven decisions, and ultimately contribute to a more efficient, personalized, and effective healthcare system for all.

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    References

    1. Bedenkov A, Moreno C, Agustin L, et al. Customer centricity in medical affairs needs human-centric artificial intelligence. Pharmaceutical Medicine. 2021 Jan;35(1):21-9.
    2. Yang H, Khatry DB. Reinventing Medical Affairs in the Era of Big Data and Analytics. InData Science, I, and Machine Learning in Drug Development 2022 Oct 3 (pp. 245-263). Chapman and Hall/CRC.
    3. Mayorga-Ruiz I, Jiménez-Pastor A, Fos-Guarinos B, et al. The role of AI in clinical trials. Artificial Intelligence in Medical Imaging: Opportunities, applications and risks. 2019:231-43.
    4. Khalifa AA, Ibrahim MA. Artificial intelligence (AI) and ChatGPT involvement in scientific and medical writing, a new concern for researchers. A scoping review. Arab Gulf Journal of Scientific Research. 2024 Jan 4.
    5. Patil RS, Kulkarni SB, Gaikwad VL. Artificial intelligence in pharmaceutical regulatory affairs. Drug Discovery Today. 2023 Jul 12:103700.
    6. Bedenkov A, Rajadhyaksha V, Beekman M, et al. Developing medical affairs leaders who create the future. Pharmaceutical medicine. 2020 Oct;34(5):301-7.
    7. Farah L, Borget I, Martelli N. International Market Access Strategies for Artificial Intelligence–Based Medical Devices: Can We Standardize the Process to Faster Patient Access? Mayo Clinic Proceedings: Digital Health. 2023 Sep 1;1(3):406-12.
    8. Hoffman FP, Freyn SL. The future of competitive intelligence in an AI-enabled world. International Journal of Value Chain Management. 2019;10(4):275-89.
    9. Roski J, Gillingham BL, Just E, et al. Implementing and scaling artificial intelligence solutions: considerations for policy makers and decision makers. Health Affairs Forefront. 2018.

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  • Using AI to Accurately Assess Risk of Bias in Published Articles: Are We There Yet?

    Using AI to Accurately Assess Risk of Bias in Published Articles: Are We There Yet?

    In the realm of medical research, the credibility and accuracy of published articles are paramount. Healthcare professionals rely on these articles to make informed decisions regarding patient care, treatment modalities, and developing clinical guidelines. However, the presence of bias in scientific studies can significantly undermine the validity and trustworthiness of their findings, potentially leading to misguided conclusions and inappropriate healthcare practices. The emergence of artificial intelligence (AI) has sparked considerable interest in utilizing its capabilities to assist in assessing bias in published articles.(1)

    Bias can occur at various stages of the research process, including study design, data collection, analysis, interpretation, etc. Identifying and minimizing bias is crucial to ensure that research findings are unbiased, reliable, and can effectively translate into clinical practice. Typically, this assessment involves thoroughly examining various aspects of a study, such as study design, methodology, data collection, analysis, and reporting. Experts evaluate factors that may introduce bias, including conflicts of interest, selective reporting, inadequate blinding or randomization, and other potential sources of bias. This manual process requires expertise and can be time-consuming, especially when analyzing a large number of articles. The introduction of artificial intelligence (AI) in risk of bias assessment offers several advantages over traditional means. By leveraging machine learning algorithms, AI tools can identify patterns and indicators of bias in titles, abstracts, and full-text articles. This technology accelerates the screening process, increases consistency in assessments, and provides additional insights into potential biases.(2)

    While AI cannot replace human expertise, it serves as a valuable tool for initial screening and prioritization, enabling researchers and clinicians to focus their attention on articles with a lower risk of bias and facilitating evidence-based decision-making in a more timely and efficient manner. The integration of AI in assessing the risk of bias in published articles signifies a significant advancement, promising enhanced reliability and objectivity in evaluating scientific literature.(3)

    AI algorithms can analyze vast amounts of data and identify patterns that might be challenging for humans to detect. In recent years, researchers have developed AI-based tools and techniques to assist in assessing the risk of bias in scientific studies. These tools utilize machine learning algorithms to evaluate published articles based on predefined criteria and indicators of bias.(3)

    While AI has shown promise in assessing bias in scientific literature, it is crucial to emphasize the need for collaboration between researchers, clinicians, and AI experts. By combining domain expertise and technical knowledge, interdisciplinary teams can develop more accurate and reliable AI models. Ongoing research and development are necessary to refine AI models, improve their performance in detecting various types of bias, and address the limitations, such as the reliance on limited text information.(4, 5)

    The application of AI in assessing the risk of bias (ROB) in scientific articles is not without its challenges. AI models may lack contextual understanding, struggle with interpretation and identifying subtle bias, and have limited adaptability to evolving research practices. They can also perpetuate biases present in training data, raise ethical concerns, and create accountability challenges. Further, despite reasonably accurate predictions, the imperfections of AI models highlight the need for manual verification to ensure comprehensive and reliable assessments.(5)

    While AI has showcased promise in assessing bias within the scientific literature, a collaborative approach between researchers, clinicians, and AI experts is vital for further advancement. The fusion of domain expertise and technical acumen within interdisciplinary teams can foster the development of more accurate and reliable AI models. Continued research and development efforts are essential to refine existing models, augment their performance in detecting diverse types of bias, and address inherent limitations, such as the dependence on limited textual information.

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    References:

    1. Jardim PS, Rose CJ, Ames HM, et al. Automating risk of bias assessment in systematic reviews: a real-time mixed methods comparison of human researchers to a machine learning system. BMC Medical Research Methodology. 2022 Jun 8;22(1):167.
    2. Arno A, Elliott J, Wallace B, Turner T, Thomas J. The views of health guideline developers on the use of automation in health evidence synthesis. Systematic Reviews. 2021 Dec;10:1-0.
    3. Soboczenski F, Trikalinos TA, Kuiper J, et al. Machine learning to help researchers evaluate biases in clinical trials: a prospective, randomized user study. BMC medical informatics and decision making. 2019 Dec;19:1-2.
    4. Marshall IJ, Kuiper J, Wallace BC. Automating risk of bias assessment for clinical trials. IEEE J Biomed Health Inform. 2015 Jul;19(4):1406-12.
    5. Marshall IJ, Kuiper J, Wallace BC. RobotReviewer: evaluation of a system for automatically assessing bias in clinical trials. J Am Med Inform Assoc. 2016 Jan;23(1):193-201.

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  • Big Data in Medicine and Artificial Intelligence: A Real World Challenge?

    Big Data in Medicine and Artificial Intelligence: A Real World Challenge?

    Artificial Intelligence (AI) refers to a computerized system that performs physical tasks, cognitive functions, solves problems, and/ or makes decisions without overt human instructions.[1] First proposed by McCarthy in 1955, the concept of AI has been applied in many health-related areas, including clinical research, hospital care, drug development, disease diagnosis, prognosis, and treatment monitoring. Advancement in the field of research has led to low-cost computational resources leading to digitalization of healthcare, innovation in daily routine examination, and improving overall quality of treatment. It has become an important element of medical diagnosis, for example, in the assessment skin lesions, detection of diabetic retinopathy, interpretation of chest X-rays, etc. In addition, AI has great value in aiding clinicians to improve quality and safety of healthcare delivery.[2]

    Over the years, big data has unknowingly been part of daily routine in the medical field as well, through clinical trials data, patients’ records, pharmaceutical research, claims data, fitness and diet apps, and various data storage platforms. Big data originated in 1997, when it became difficult to display large data sets that were stored in computers and limited the analysis of data. Doug Laney summarised the challenges of big data as the ‘3 Vs’: volume, variety, and velocity.[3]

    • ‘Volume’ refers to the quantity of the new data that is created from multiple sources, such as health records, insurance claims, transactions, sensors, etc.
    • ‘Variety’ refers to the difference in the format of Big Data from different sources, which can be structured, unstructured, semi-structured or a mixture of the three.
    • ‘Velocity’ refers to the temporal component of Big Data, in terms of creation, storage, and analysis: this can be in batches, near time, real time, streaming, retrospective, prospective, or a combination.

    There has been integration of AI and big data in the field of medicine. AI concepts such as data mining, complex statistical analysis, machine learning, and neural networks are being increasingly used for faster and more precise big data analysis in medicine.[4] This integration of AI and big data has been seen in different applications, such as mHealth (wearable devices such as smartwatches, fitness bands) and eHealth (Google fit, Apple health) applications for self-management and home care. AI also has a role in the analysis of big data originating in:

    • Electronic health records (EHRs) and electronic medical records (EMRs) for epidemiological, safety, efficacy, and patient-reported outcomes insights
    • Imaging data for precise diagnosis and treatment monitoring, especially in oncology, wherein the AI has been used for precise description of tumour biology and implementation of precision medicine for the assessment of tumour, its microenvironment, and radiomics.
    • Personal health records (PHRs) and practice management software (PMS) to improve the service, quality and costs of healthcare delivery with less medical errors.
    • Genomic sequencing like genotyping and gene expression.
    • Development and usage of wellness monitoring devices has gained popularity in improving health.[5,6]

    Though AI and big data work in synergy, often it is observed that health care systems are not fully equipped, as in the case of medical imaging, to share large amount of images. Analysis, labelling, accurate curation and clinical applications are the major challenges.[7] Repeated models with homogeneous data, lack of diversity in different populations and restricted clinical setting with poor generalizability remain the major setbacks of AI currently. The currently available AI systems, though advanced, still have a far way to go to be completely and reliably accurate enough to totally replace human intervention. There are concerns surrounding quality control, accuracy, consistency, security and privacy with AI, which mandate human intervention and participation in health-related data curation. [8]

    The analysis of big data in medicine, either through stand-alone AI protocols or with assistance of manual data curation, largely contributes to the development of real-world evidence (RWE). This offers incredible amount of data that can support regulatory drug approvals. In fact, RWE has been the basis of several approvals, such as the recent approval of Novartis’ alpelisib as the first and only treatment for PIK3CA-related overgrowth spectrum, which is considered as a rare disease.[9] This showcases the importance of RWE generation through big data curation by means of manual curation-assisted AI protocols.

    AI has a big role to play in big data curation and analysis in health care, but at the present, human intervention still cannot be completely replaced by AI. Manual curation still has an important role to play in medical big data analytics.

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     References

    1. Kaplan A, Haenlein M. Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Bus Horiz. 2019;62(1):15–25.
    2. Ahmad Z, Rahim S, Zubair M, Abdul-Ghafar J. Artificial intelligence (AI) in medicine, current applications and future role with special emphasis on its potential and promise in pathology: present and future impact, obstacles including costs and acceptance among pathologists, practical and philosophical considerations. A comprehensive review. Diagn Pathol. 2021 Mar 17;16(1):24.
    3. Laney, D. (2001). 3D data management: controlling data volume, velocity, and variety. Meta Group. http://blogs.gartner.com/douglaney/files/2012/01/ad949-3D-Data-Management-ControllingData-Volume-Velocity-and-Variety.pdf
    4. Rahmani AM, Azhir E, Ali S, Mohammadi M, Ahmed OH, Yassin Ghafour M, Hasan Ahmed S, Hosseinzadeh M. Artificial intelligence approaches and mechanisms for big data analytics: a systematic study. PeerJ Comput Sci. 2021 Apr 14;7:e488.
    5. Yang YC, Islam SU, Noor A, Khan S, Afsar W, Nazir S. Influential Usage of Big Data and Artificial Intelligence in Healthcare. Comput Math Methods Med. 2021 Sep 6;2021:5812499
    6. Dash S, Shakyawar SK, Sharma M, Kaushik S. Big data in healthcare: management, analysis and future prospects. J Big Data 2019;6:54
    7. Willemink MJ, Koszek WA, Hardell C, Wu J, Fleischmann D, Harvey H, Folio LR, Summers RM, Rubin DL, Lungren MP. Preparing Medical Imaging Data for Machine Learning. Radiology. 2020 Apr;295(1):4-15.
    8. Wehner MR, Levandoski KA, Kulldorff M, Asgari MM. Research Techniques Made Simple: An Introduction to Use and Analysis of Big Data in Dermatology. J Invest Dermatol. 2017 Aug;137(8):e153-e158.
    9. https://www.novartis.com/news/media-releases/fda-approves-novartis-vijoice-alpelisib-first-and-only-treatment-select-patients-pik3ca-related-overgrowth-spectrum-pros

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  • How Next Generation Intelligent Data Systems are Helping in Patient Care?

    How Next Generation Intelligent Data Systems are Helping in Patient Care?

    The 90’s saw the Internet and the World Wide Web entering commercial markets as a result of major advancements in Information Technology (IT). Another huge development was later followed when mobile devices connected to the Internet became a rage in late 2000’s. Today, we’re in the middle of the next major leap, i.e. the next generation of intelligent (IT). (1)

    These leaps will not only continue having a significant impact on our personal lives, but they will also introduce new business models and product and service opportunities. The ever expanding technological progress is taking ahead the scientific knowledge, thus reducing costs and presenting the healthcare industry with innovative medical devices and procedures to diagnose, monitor and treat patients. (2)

    Big data has already transformed almost every aspect of life, including healthcare; and it’s time we implemented data-driven healthcare in our routine. Advances in data collection, storage and analytics have been accompanied by the proliferation of data; for e.g. from sensors and devices, clinical information systems and electronic health records (EHRs). Simultaneously, widespread application of data standards and interoperability is being observed, thus allowing developers to find more functions for health data. (3)

    As a result, many healthcare organizations, including pharmaceuticals, biopharmaceuticals as well as medical devices firms; are turning these recent and emerging technological advancements to good account, thereby providing innovative solutions using mobile health applications, sensor technology, data analytics, and artificial intelligence. The last decade has also witnessed the steady growth of venture capital investments stimulating medical technology (MedTech) products, especially in areas like bioinformatics and biosensors. (2)

    In addition, the volume of data produced by healthcare organizations is expanding and it is facilitating the delivery of cancer treatments, personalization of medical interventions, prediction of chronic diseases, driving behavioral changes through next-generation analytics technologies such as big data, cognitive computing and machine learning. Furthermore, artificial intelligence (AI) is constantly evolving and improving. Today, technology exists to capture data from incongruent sources and provide a real-time view of a patient’s health. All the associated technologies are evolving faster and continuously, such as mobile, cloud, analytics and the Internet of Things, to deliver solutions in advanced AI. As a result, the global predictive analytics market is expected to grow by almost 20% a year, reaching $6.5 billion by 2019.(3)

    Sensors and connected devices capturing all kinds of data are omnipresent. The worldwide market for wearable technology is expected to rise from 45 million units shipped in 2015 to more than 125 million by 2019. (4) Digital consumer devices entering regulated markets have increased in numbers, with expected FDA approvals for these products to triple in 2018 (relative to 2014 levels).(5)

    Next generation intelligent devices are creating immense opportunities for traditional healthcare as well as medical device companies. For instance, the smart contact lens has been developed by Novartis and Google to monitor glucose levels in people with diabetes. Another example is recently launched LOGIQ E10, the next generation radiology ultrasound technology by GE Healthcare. In LOGIQ E10, the digital system incorporates AI, cloud connectivity and advanced algorithms to gather and reconstruct imaging data faster; thus significantly improving image quality and giving clinicians better confidence in their diagnoses, particularly in difficult cases. (6)

    Big data can be derived from mobile medical health systems, wearable devices, and other next generation mobile communications technology and can be further used to integrate the primary medical services and improve primary healthcare quality, residents’ health index, control the growth rate of a variety of common acute and chronic diseases and increase residents’ awareness of health management and disease prevention. (7,8) Therefore, the next generation IT systems can most certainly be used into healthcare field to overcome worldwide health problems such as uneven distribution of medical resources, the growing chronic diseases, and the increasing medical expenses and can help provide patients with overall better quality of care.

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    References

    1. Glaser J, et al. The Next Generation of Intelligent Healthcare Information Technology. Convergence. Sept 15th, 2017. 
    2. Next-generation “smart” MedTech devices- Preparing for an increasingly intelligent future. Deloitte Analysis.
    3. Reily T. How data is making healthcare better. World Economic Forum.
    4. Worldwide Wearable Market Forecast to Reach 45.7 Million Units Shipped in 2015 and 126.1 Million Units in 2019, IDC.
    5. Patient Engagement: How the Colossal Clash Will Disrupt the Digital Health Landscape – Infographic.  Accenture. 
    6. Monegain B. Artificial intelligence powers GE Healthcare’s next-gen ultrasound system. March 2nd, 2018. 
    7. Li G. Big data related technologies challenges and future prospects. Inf Technol Tourism 2015; 15(3):283-285.
    8. Ma Y, et al. Big health application system based on health Internet of Things and Big Data. IEEE Access 2016; 5:7885-7897.

    Written by: Ms. Tanvi Laghate

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  • Is There a Role of Artificial Intelligence in Medical Affairs?

    Is There a Role of Artificial Intelligence in Medical Affairs?

    Artificial intelligence (AI) has no universally agreed definition. It roughly includes computing technologies that are similar to processes related to human intelligence; for e.g. reasoning, learning and adaptation, sensory understanding, and interaction. (1,2) At present, most AI applications are limited, since they can only perform specific tasks or solve pre-defined problems. (3) Recently, AI has been gaining significance in the field of healthcare. The AI industry is estimated to be worth $6 billion dollars by the year 2021. (4)  A recent McKinsey review has projected that healthcare would be one of the top 5 industries to involve AI. (5)

    Artificial intelligence aims to emulate human cognitive functions. It is transforming healthcare with the help of abundant healthcare data and ever growing progress in data analytics. Major AI techniques include machine learning methods (ML, for structured data) and natural language processing (NLP, for unstructured data). Important therapeutic areas that deploy AI tools include cancer, neurology and cardiology. (6)

    Rapid technological advances in the world of life sciences are leading to ever more diverse and unstructured data, thus expanding the number of sources and the volume of accessible data. However, better decision making and long term business value are achievable by transforming these widely available data into actionable insights. For instance, marketing and medical affairs teams often deal with an increasingly complex network of external stakeholders and market influencers. In this case, AI can help faster obtain more comprehensive insights about key stakeholders at lower costs. (7)

    Various data sets represent different stakeholders of interest. The scientific profile of these data sets provides a sound idea of the expertise; whereas, previous collaborative regulatory arrangements might offer profound understanding to support key decisions. Identification and extraction of this data from the hugely different, siloed sources is a herculean task, so is manually structuring the diverse information and mapping each activity to the right stakeholder, even at a small scale. Applying the same process for the global landscape and then re-doing the same thing each day to keep the data fresh and updated is unimaginably difficult. In these instances, if applied correctly, AI can structure, refresh and update actionable insights to improve decision making. The power of automation and AI can facilitate efficient use of huge volumes of information in decision making, instead of wasting time to just gather, structure and analyse the data.

    The global medical affairs functions have seen a significant shift in the performance in last two years. The days of limited stakeholder engagement with the existing network are gone already. In many progressive companies, a data-driven culture has taken over, which goes all-out to produce better, most unbiased results. Such an approach provides a huge competitive advantage today and lays the foundation for a structural competitive advantage for future. Furthermore, implementation of AI can provide speed, operational excellence and long-term competitive advantage; thereby saving millions and decreasing costs. The advances of machine learning and AI open up a range of new possible approaches that, when applied, can create positive feedback loops to different parts of your organization. (7)

    However, in spite of the attractive benefits of AI technologies, their real-life large scale applications are still facing obstacles. The first hurdle comes from the regulations, since they lack standards to assess the safety and efficacy of AI systems. This can be overcome by the guidance provided by the US-FDA for assessing AI systems. Another hurdle is data exchange which originates from the need of AI systems to be constantly trained by data from clinical studies in order to function properly. Also, continuous data supply can pose critical issues in further development of the system; post the installation of an AI system after initial training with historical data. (8)

    Artificial Intelligence has the A) ability to analyse large and complex data sets in healthcare, and B) plenty of potential ranging across functions like discovering new medicines through to analysing journals, reports, trial results, conference proceedings and determining the best treatment for specific patients. (9) One of the predictions for AI is that it will greatly improve the efficiency of the drug discovery process, reducing both the 15-year timeline and $1 billion plus costs in getting a drug to market. (10) However, to achieve success in this rapidly growing AI environment will require implementation of tools of computer science (i.e. statistics, probability, and logic) to understand the complexities of healthcare datasets as well as the concepts of Big Data, deep learning and cognitive computing.

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    References:

    1. Artificial intelligence technologies. Engineering and Physical Sciences Research Council. 
    2. Artificial intelligence (AI) in healthcare and research. Nuffield Council on Bioethics. May, 2018. 
    3. Preparing for the future of artificial intelligence. US National Science and Technology Council. 2016. 
    4. Frost & Sullivan. Artificial Intelligence & Cognitive Computing Systems in Healthcare, 2016.
    5. McKinsey & Company, Artificial intelligence: The time to act is now. January, 2018. 
    6. [Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present and future. Stroke and Vascular Neurology 2017; 2: e000101.  
    7. Jansson F. 3 ways to boost marketing and medical affairs with artificial intelligence. March, 2018.
    8. Graham J. Artificial Intelligence, Machine Learning, and the FDA. 2016. 
    9. Trends in medical affairs. 
    10. Cox D. Artificial intelligence in healthcare. August, 2018.

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